Abstract

Achieving a sustainable and resilient energy future requires multidisciplinary approaches that connect fundamental science with engineering solutions and systems-level analysis. In this presentation will showcase research programs at the Energy Institute led by Prof. Vega spanning molecular and materials design, advanced process enginering, carbon management, hydrogen technologies, and circular approaches to energy and resource systems. Particular emphasis will be placed on methodologies that integrate computational modeling, artificial intelligence, experimental validation, techno-economic analysis, and life cycle assessment to accelerate the development and deployment of low-carbon technologies. The presentation will also briefly highlight complementary activities across the Energy Institute, including clean energy generation, systems integration, hydrogen production and power-to-X, energy storage materials and systems, carbon capture and utilization, water-energy nexus, energy efficiency, and sustainable fuels. It is expected to raise collaborative research opportunities and provide a framework for the development of the joint dual PhD program.

Biography

Dr. Lourdes F. Vega is a Full Professor of Chemical Engineering and the Senior Director of the Energy Institute at Khalifa University (UAE), where she previously founded the Research and Innovation Center on CO₂ and Hydrogen (RICH). Her international career spans academia and industry across the US, Spain, and the UAE, with research focused on carbon management, hydrogen technologies, sustainable fuels, and AI-driven materials discovery. An expert in translating fundamental science into industrial solutions, she has authored over 300 publications, holds six patents under exploitation, and co-founded two start-ups. Her global contributions to sustainability have been recognized with prestigious honors, including the Mohammed Bin Rashid Medal of Scientific Distinction, and global 2025 "Women in Hydrogen 50", and she serves as a UAE representative for Mission Innovation on Clean Hydrogen.

Abstract

This research program focuses on developing sustainable pathways to convert underutilized feedstocks such as biomass, municipal and industrial waste, plastics, and CO2 into fuels, chemicals, and advanced materials. Experimental studies are conducted using both batch and continuous bench-scale and pilot-scale reactor systems, supported by advanced analytical techniques to evaluate process performance, product quality, catalyst stability, and greenhouse gas reduction potential. One area of investigation is the development of biochar-based catalysts for clean fuel production. This includes the design of sustainable catalysts for dry reforming of methane and renewable natural gas production from CO2. Related research explores waste-derived catalysts for methane pyrolysis, enabling the production of hydrogen and carbon nanofibers. These projects encompass biochar production from a variety of feedstocks, followed by functionalization, characterization, and metal doping to tailor catalytic performance for specific applications. A second research theme focuses on thermochemical conversion of waste through torrefaction, pyrolysis, and gasification. Many projects are conducted in collaboration with industrial partners, with process conditions optimized to match feedstock characteristics and desired product specifications. The program also investigates the removal of emerging contaminants from water, including pharmaceuticals and per- and polyfluoroalkyl substances (PFAS). We examine thermal treatment of biosolids for PFAS destruction with integrated fluorine capture. In addition, we are developing engineered biochar adsorbents to selectively remove targeted contaminants from water. Potential areas for co-supervision of students would include partnering with experts who can investigate the integration of biochar into sustainable materials, or as an effective wastewater treatment medium. Our lab has the capacity to support performance testing of catalysts for renewable fuel production that have been produced using alternative or specialized synthesis techniques. In addition, our larger-scale continuous pyrolysis reactors facilitate the investigation of process conditions and the evaluation of emerging feedstocks under continuous operating conditions.

Biography

Naomi Klinghoffer is an Assistant Professor in the Department of Chemical and Biochemical Engineering with an appointment at Western University's Institute for Chemicals and Fuels from Alternative Resources (ICFAR). Her research focuses on developing thermal and catalytic processes for the conversion of low value feedstocks such as waste, biomass, and CO2, into fuels, chemicals, and materials.

Collaboration interests

Naomi Klinghoffer is an Assistant Professor in the Department of Chemical and Biochemical Engineering with an appointment at Western University's Institute for Chemicals and Fuels from Alternative Resources (ICFAR). Her research focuses on developing thermal and catalytic processes for the conversion of low value feedstocks such as waste, biomass, and CO2, into fuels, chemicals, and materials.

Abstract

This presentation focuses on an integrated research framework centered on non-thermal plasma (NTP) technology—a platform that activates thermodynamically stable molecules under ambient temperature and pressure, bypassing the extreme conditions of conventional thermochemical processes. We demonstrate a synergistic approach that leverages unique non-equilibrium environment of non-thermal plasma (NTP), enabling precise reaction control that delivers unparalleled selectivity for targeted chemicals. 1. CO₂ Valorization: Directly hydrogenation of CO₂ to alcohols, transform flue gas and methane-rich landfill gas into high-value platform chemicals (e.g., syngas, methanol) with high selectivity. 2. Low-Carbon Hydrogen: Splitting methane into gaseous H₂ and solid carbon, permanently sequestering carbon in a pure solid form—effectively a "carbon-negative" hydrogen route. 3. Chemical Hydrogen Storage: Green ammonia synthesis under mild conditions, creating a dense, transportable energy carrier that overcomes the storage challenges of gaseous hydrogen. Crucially, all three projects operate under the same core NTP principle, offering a modular, electrified, and renewable-energy-compatible solution that integrates seamlessly with existing industrial infrastructure. By activating inert molecules with energetic electrons rather than heat, our process achieves thermodynamic "impossibilities" at room temperature.

Biography

Ying Zheng is Professor and Canada Research Chair (Tier I). Dr. Zheng serves as the President of the Canadian Society for Chemical Engineering and is an elected Fellow of the Royal Society of Canada, the Canadian Academy of Engineering. With a focus on catalysis and clean energy, she specializes in developing novel catalytic materials and processes for applications in CO2 utilization, H2 production, functional materials synthesis and waste-to-biofuel. In addition to numerous licensed patented technologies, she has been consistently recognized as World’s Top 2% Most-cited Scientists in the Stanford University Analysis from 2019 to 2024. Representative publications in Chemical Society Reviews, Applied Catalysis B, Chem, Advanced functional materials, Angewandte Chemie. Her achievements have received global recognition including the 2025 Energy Systems Award from the American Institute of Aeronautics and Astronautics, the 2018 Applied Catalysis Award from the Royal Society of Chemistry (UK) and the 2018 Design and Industrial Practice Award from the Canadian Society for Chemical Engineering.

Collaboration interests

I am looking for co-supervising of graduate students on CO₂ Fixation, Clean H₂, and Green NH₃ synthesis

Abstract

Methane decomposition (thermocatalytic pyrolysis) is emerging as a promising pathway for low carbon hydrogen production, enabling CO₂ free hydrogen generation alongside solid carbon co products with significant economic value. This research focuses on the development of advanced catalytic systems and reactor strategies for efficient methane-to-hydrogen conversion, with particular emphasis on carbon formation control, catalyst stability, and integration with biogas feedstocks (CH₄–CO₂ mixtures). The work explores transition metal and bimetallic catalysts, as well as emerging approaches, to enhance methane conversion rates while controlling carbon morphology. Special attention is given to carbon nanostructure synthesis (carbon nanotubes, nanofibers) and strategies for valorization of the carbon co-product. These nanostructured carbons exhibit exceptional properties such as high surface area, conductivity, and tunable porosity, enabling their application in energy storage devices (supercapacitors, lithium-ion batteries) and as advanced adsorbents for gas storage, CO₂ capture, and environmental remediation. The study also investigates in-situ regeneration mechanisms, including CO₂-assisted gasification of deposited carbon, leveraging biogas compositions to extend catalyst lifetime and enable steady-state operation. Methodologically, the research combines experimental catalysis (fixed and fluidized bed reactors), materials characterization and modeling to understand reaction kinetics, deactivation pathways, and carbon growth mechanisms. Integration of biogas reforming and methane decomposition pathways is assessed to balance hydrogen yield with syngas formation and carbon recovery. The research offers strong potential for student collaboration in catalyst synthesis, reaction engineering, and carbon material characterization. It opens interdisciplinary opportunities across chemical engineering, materials science, and sustainable energy systems, with scope for both experimental and computational contributions.

Biography

Srinivasakannan Chandrasekar is a Professor in Chemical & Petroleum Engineering. His research focuses on catalysis, reaction engineering, and sustainable energy systems, with extensive contributions to thermochemical conversion processes.

Collaboration interests

Interested in collaborating with motivated students and co supervisors working in heterogeneous catalysis, carbon materials synthesis, and hydrogen production technologies, particularly those with expertise in experimental reactor systems, catalyst characterization, or process modeling, as well as applications of nanostructured carbon in energy storage and adsorption systems.

Abstract

Sorption-Enhanced Reforming (SER) integrates methane reforming, water-gas shift, and in-situ CO₂ capture in a single reactor, operating at 520–600°C with near-stoichiometric steam ratios and delivering hydrogen purity above 98%. Compared to conventional Steam Methane Reforming, which emits 9–10 kg CO₂ per kg H₂ across multiple high-temperature units, SER offers a compact, lower-cost blue hydrogen route. The central challenge is that dual-functional sorbent-catalyst materials with strong intrinsic properties do not always translate to superior system-level performance once cyclic stability, heat transfer, separation, and sorbent valorization are considered together.

This research addresses this gap through an AI-driven product design platform. System-level targets (e.g., cost, purity, cyclic stability, and circularity) define material specifications that guide the search and screening of candidate sorbent-catalyst composites. Rather than propagating material properties forward to process evaluation, inverse information flow feeds techno-economic and life-cycle outcomes back to redefine what materials, process and operating conditions must deliver before synthesis begins.

A key uncertainty in scaling such systems is predicting coupled gas-solid phenomena at industrially relevant conditions. To reduce this uncertainty, we integrate CFD modelling and model predictive parametric estimation with dedicated experimental activities in a newly commissioned high-temperature fluidized-bed reactor at KU. The CFD framework captures particulate hydrodynamics, heat and mass transfer, and reaction kinetics across scales. Experiments are designed using model-based methods to generate the most informative data, which are then used to identify and refine model parameters and validate model predictions and identification.

Biography

Ahmed Alhajaj is an Associate Professor of Chemical and Petroleum Engineering at Khalifa University, Theme Lead for CCS at the Research and Innovation Center on CO₂ and Hydrogen (RICH Center), and a current Visiting Fellow at the School of Chemical Engineering, UNSW Sydney. He received his PhD in Process Systems Engineering from Imperial College London in 2014 and was a Visiting Professor at MIT from 2015 to 2016. His research spans product design and process synthesis, multiscale modelling, supply-chain optimization, and carbon capture and utilization.

Collaboration interests

Seeking a collaborator in catalyst synthesis, materials characterization and kinetics model development

Seeking PhD students with an interest in developing new tools and paradigms for product design.

Abstract

This presentation focuses on the design, synthesis, and application of advanced functional porous materials tailored for a sustainable energy landscape. Addressing critical global challenges, the work targets energy storage and sustainable catalysis. A major cornerstone of the research is the synthesis and advanced characterization of novel composite materials to achieve multi-functional properties with exceptional control over particle size and morphology. These materials are strategically applied across crucial green energy vectors, including metal-air batteries, proton-exchange membrane fuel cells (PEMFCs), hydrodeoxygenation of bio-fuels, and the thermal or electro-reduction of carbon dioxide into value-added chemicals and fuels.

Potential student roles include optimizing advanced continuous-flow synthesis configurations, and material testing for CO2 conversion (thermal, electro, and plasma), energy storage and H2 fuel cells, with specific interest in establishing structure-property relationships. I am interested in collaborating with faculty working on complementary materials and processes, modelling, and on system level design.

Biography

Dr. Maryam Khaleel is an Associate Professor of Chemical and Petroleum Engineering at Khalifa University, where she also serves as the Theme Lead for Sustainable Fuels at the Research and Innovation Center on CO2 and Hydrogen (RICH). She holds a PhD in Chemical Engineering from the University of Minnesota, specializing in the synthesis and characterization of hierarchical zeolite nanostructures. Her research focus is on functional porous materials for clean energy applications, including CO2 conversion into value added products, bio-fuels upgrading, metal air batteries, and hydrogen technologies. Dr. Khaleel earned prestigious accolades, including the L’Oréal-UNESCO for Women in Science Middle East Regional Young Talents Fellowship (2020) and being named an Innovator Under 35 by the MIT Technology Review (2025).

Collaboration interests

I am interested in partnering with academic researchers and co-supervising PhD candidates to expand interdisciplinary, multiscale projects. Key areas for collaborative synergy include:

*Computational & multiscale modeling: Linking experimental material development with molecular simulations, Monte Carlo methods, or Machine Learning/AI to accelerate materials discovery.

*Material development and application testing and scaling: The flow synthesis of novel porous materials, and their testing for CO2 conversion (thermal, electro, and plasma), energy storage and H2 fuel cells, with specific interest in establishing structure-property relationships.

Abstract

The global transition toward carbon-neutral energy systems demands transformative advances in electrochemical energy conversion and storage technologies. Meeting this challenge requires the rational design of high-performance electrode and electrocatalyst materials that simultaneously deliver superior activity, long-term durability, and compatibility with scalable manufacturing processes. Our research group at Khalifa University focuses on developing advanced functional oxide materials and architectures that can be deployed across a broad spectrum of clean energy devices, addressing both power generation and energy storage needs in a unified materials-design framework. On the energy conversion front, we develop high-performance electrode materials for solid oxide fuel cells (SOFCs) and solid oxide electrolysis cells (SOECs). These high-temperature devices offer exceptional efficiency for electricity generation from hydrogen and hydrocarbons, as well as for the thermally assisted production of green hydrogen and synthesis gas. Our work targets electrode materials with enhanced mixed ionic–electronic conductivity, redox stability, and tolerance to operational degradation, enabling extended device lifetimes and improved round-trip efficiency in reversible SOFC–SOEC systems. In parallel, we investigate electrocatalysts for anion-exchange membrane (AEM) water electrolyzers, a promising low-temperature technology for scalable green-hydrogen production. We develop earth-abundant, noble-metal-free catalysts with high activity for the oxygen evolution reaction under alkaline conditions, addressing the critical challenge of combining catalytic performance with membrane compatibility and chemical stability. Beyond hydrogen technologies, our group explores advanced electrode materials for aqueous zinc-ion batteries—an emerging electrochemical energy storage platform that leverages the low cost, safety, and natural abundance of zinc. We design cathode materials with optimized crystal structures and surface properties to enable rapid Zn²⁺ insertion kinetics, high capacity retention, and resistance to dissolution and structural degradation during cycling. By integrating insights from defect chemistry, surface engineering, and electrochemical characterization, we establish structure–property–performance relationships that guide the development of next-generation zinc-ion battery systems suitable for grid-scale and distributed energy storage. Across all four technology domains, our research adopts a common approach: leveraging structural tunability, elemental substitution strategies, and defect engineering to optimize the electrochemical performance and stability of functional oxide materials.

Biography

Dr. Sivaprakash Sengodan is an Assistant Professor in Mechanical Engineering at Khalifa University. His research focuses on ionic and electronic transport processes in electrochemical devices, including fuel cells, electrolysers, membranes, and related energy-conversion systems. Before joining Khalifa University, he was an Imperial College Research Fellow in the Department of Materials at Imperial College London. He earned his PhD in Energy Engineering from Ulsan National Institute of Science and Technology, South Korea, in 2015

Collaboration interests

I am looking to co-supervise a student working on AI-assisted discovery and optimization of functional oxide materials for energy conversion and storage applications. Ideal collaboration profiles include expertise in machine learning interatomic potentials, high-throughput DFT-based screening of electrode and electrocatalyst compositions, and additive manufacturing / 3D printing of electrochemical device components with tailored microstructures

Abstract

Heterogeneous catalysis underpins the selective conversion of small oxygenates — alcohols and C1 molecules — into fuels and higher-value chemicals, yet rational catalyst design remains limited by an incomplete, molecular-level understanding of how active sites govern reactivity and selectivity. My research program addresses this gap by establishing quantitative structure-activity relationships for transition-metal-oxide catalysts (Fe-Mo, VOx, Mg-Al) used in the partial oxidation, oxidative dehydrogenation, and coupling of alcohols. The defining approach combines operando and in situ optical spectroscopy with steady-state and transient kinetic experiments, active-site quantification, and microkinetic modeling, so that spectroscopic signatures, redox-site and lattice-oxygen dynamics, and acid-base site functions can be linked directly to catalytic performance. A central outcome is a transferable thermochemical-kinetic framework in which bond dissociation energies and hydrogen-atom-addition energies serve as molecular and active-site descriptors of C–H, H2, and O2 activation across diverse oxide chemistries. This understanding is carried across length scales, from elementary kinetic steps to the engineering of active-phase distribution in industrial catalyst pellets — demonstrated in the conversion of methanol to oxymethylene ethers (OMEs), a clean-fuel additive. The same mechanistic principles extend to sustainable-fuel targets of shared interest, including ethanol and biomass-derived oxygenate upgrading and the reductive conversion of CO2. The program offers strong potential for doctoral co-supervision: a student would gain rigorous training in operando spectroscopy, reaction kinetics, and mechanistic modeling at Western, complemented by expertise in catalyst-material synthesis or computational and process modeling at Khalifa University. Such pairing would produce researchers able to connect catalyst structure, active-site chemistry, and reactor-scale performance within a single, coherent framework.

Biography

José E. Herrera is a Professor of Chemical and Biochemical Engineering at Western University whose research centers on the mechanistic study of heterogeneous catalysis, using operando spectroscopy and reaction kinetics to understand how active-site chemistry governs the selective oxidation and upgrading of alcohols and small oxygenates. His group's work appears in leading catalysis journals such as Journal of Catalysis and ACS Catalysis. He currently serves as Technical Program Chair for NAM30, the 30th North American Catalysis Society Meeting (Toronto, 2027).

Collaboration interests

I am seeking to co-supervise a doctoral student working at the interface of experimental mechanistic catalysis and complementary expertise at Khalifa University. The ideal student profile is one with a strong foundation in chemical reaction engineering and physical chemistry, motivated to develop skills in operando/in situ spectroscopy, reaction kinetics, and microkinetic modeling, and interested in connecting these to catalyst-material synthesis and/or computational and process modeling. On the faculty side, I am interested in partnering with a co-supervisor whose program emphasizes catalyst and porous-material synthesis, computational chemistry or microkinetics, or process and techno-economic modeling — areas that pair naturally with the molecular-to-reactor mechanistic framework of my group, particularly for sustainable-fuel and CO2-conversion applications.

Abstract

Abu Dhabi is underlain by extensive deposits of calcareous and carbonate sands whose behavior differs markedly from that of the silica sands on which most existing liquefaction triggering models are based. Carbonate sands possess distinctive characteristics, including particle crushing, cementation, high compressibility, and unique stress–dilatancy responses. As a result, conventional liquefaction evaluation procedures based on CPT, SPT, and shear-wave velocity measurements, which have largely been developed and calibrated using silica-sand case histories, may not accurately capture the liquefaction resistance of carbonate soils in the UAE. The proposed research aims to develop a region-specific liquefaction triggering model for Abu Dhabi carbonate sands by integrating advanced laboratory testing, centrifuge experiments, constitutive modeling, cavity expansion analyses, machine-learning techniques, and regional seismic hazard characterization. The outcome will provide a physics-informed framework for assessing liquefaction potential and improving the seismic design of infrastructure founded on carbonate soil deposits in the UAE.

Biography

Tadahiro Kishida is an Associate Professor at the Civil and Environmental Engineering, Khalifa University in Abu Dhabi, UAE. His research interests are dynamic soil behaviors, site characterization, site effects and dams/levees dynamic responses, soil liquefaction, strong ground motions and data processing, probabilistic seismic hazard analysis and design spectra.

Abstract

Granular geomaterials in coastal, arid, and resource-development environments often exhibit complex behaviour that challenges conventional site-characterization and ground-improvement approaches. My research program focuses on understanding, assessing, and improving the liquefaction and instability response of sands, mine tailings, and other problematic granular soils through an integrated combination of laboratory testing, physical modeling, numerical simulation, and bio-mediated treatment. This presentation will summarize recent work in three complementary areas. The first examines liquefaction and cyclic resistance of sands improved by microbially induced calcite precipitation (MICP), using cyclic/direct simple shear testing, shear-wave velocity measurements, microscopy, and mineralogical analyses to link bio-cementation, stiffness gain, and resistance to pore-pressure generation. The second focuses on sustainable and low-cost MICP approaches, including biochar-assisted stimulation of native bacteria and replacement of conventional laboratory-grade reagents with economical alternatives, with applications to erosion control, tailings stabilization, and environmentally responsible ground improvement. The third area addresses CPT- and seismic-CPT-based characterization of granular soils through calibration-chamber testing and discrete-element modeling, with emphasis on how density, stress history, particle shape, fabric, interparticle friction, and probe characteristics influence cone resistance. These themes align closely with emerging needs in the UAE, where carbonate sands exhibit crushing, cementation, high compressibility, and distinct stress–dilatancy behaviour that may limit the applicability of conventional silica-sand-based liquefaction correlations. Potential student collaborations with Khalifa University could combine advanced laboratory testing on carbonate sands, CPT/Vs-based liquefaction triggering, DEM or cavity-expansion interpretation, and MICP-based mitigation strategies. Such projects would support physics-informed, region-specific frameworks for seismic site characterization and sustainable improvement of infrastructure founded on carbonate and other crushable granular deposits.

Biography

Abouzar Sadrekarimi is a Professor in the Department of Civil and Environmental Engineering at Western University, Canada, where he leads research in geotechnical engineering, soil mechanics, and geotechnical earthquake engineering. His research focuses on liquefaction and instability of granular soils and mine tailings, seismic-CPT-based site characterization, advanced laboratory testing, discrete element modeling, and sustainable bio-mediated ground improvement using MICP/EICP. His work integrates laboratory experiments, physical modeling, numerical simulation, and field applications to develop practical solutions for geohazard mitigation, resilient infrastructure, and collaborative graduate-student research.

Collaboration interests

I am looking to co-supervise graduate students working on liquefaction assessment and sustainable improvement of problematic granular geomaterials, particularly carbonate sands, tailings, and crushable or cemented soils. Potential projects could combine advanced laboratory testing, CPT/SCPT and shear-wave velocity interpretation, critical-state analysis, DEM simulations, and MICP/EICP-based ground improvement to develop region-specific solutions for geohazard mitigation and resilient infrastructure in arid and coastal environments.

Abstract

Municipal wastewater treatment plants (WWTPs) generate large quantities of sewage sludge (SS), creating significant environmental and economic challenges. Conventional disposal methods, including landfilling, are costly, carbon-intensive, and may contribute to secondary pollution due to the presence of heavy metals and contaminants of emerging concern (CECs), such as PPCPs, PAHs, PFASs, and microplastics. Sludge management can account for up to 70% of WWTP operating costs while also contributing substantially to greenhouse gas emissions. Advancing circular and sustainable cities requires transforming sewage sludge and other organic wastes from environmental liabilities into valuable resources. This presentation will explore innovative waste-to-resource strategies that convert sewage sludge into high-value biofuels, functional carbonaceous materials, and sustainable construction materials, while supporting soil and water restoration. Through thermochemical and biochemical conversion technologies, sewage sludge/lignocellulosic materials can be transformed into renewable energy, effective sorbents for wastewater treatment, and soil amendments that improve water retention and soil quality. Drawing on recent research advances, the talk will highlight the environmental, economic, and societal benefits of integrated sludge valorization. By combining energy recovery, material reuse, and ecosystem restoration, these approaches can contribute to climate resilience, resource efficiency, and the achievement of net-zero sustainability goals.

Biography

Dr. Badr A. Mohamed is an Associate Professor in the Department of Civil and Environmental Engineering at Khalifa University. He earned his PhD in Chemical Engineering (Energy and Materials) from the University of British Columbia (UBC), Canada. His research focuses on developing innovative and sustainable solutions for environmental protection, hazardous waste management, emerging contaminant remediation, biofuel production, sustainable construction materials, and wastewater treatment. His work emphasizes the valorization of sewage sludge and other waste streams into high-value bio-based products for environmental remediation and resource recovery. He has extensive experience in the removal and destruction of emerging contaminants, including pharmaceuticals and personal care products (PPCPs), polycyclic aromatic hydrocarbons (PAHs), per- and polyfluoroalkyl substances (PFAS), and microplastics, from water and wastewater, as well as the production of biofuels, bio-based materials, and sustainable construction materials from waste-derived resources. He has published extensively in leading international journals, including Applied Catalysis B: Environment and Energy and Environmental Chemistry Letters.

Collaboration interests

I am interested in co-supervising PhD students to expand interdisciplinary research projects, with a focus on catalysis, thermochemical conversion processes, resource recovery, PFAS/PPCP removal and destruction, sustainable carbon capture and utilization, green construction materials, machine-learning-guided life cycle assessment (LCA) and techno-economic assessment (TEA), and bio-based products, including bioplastics and bio-asphalt.

Abstract

It is important to understand the mechanisms behind fluid-driven earthquakes, whether they are beneficial in the context of generating fractures for enhancing permeability, or hazardous in the case of large earthquakes that may cause structural damage or compromise caprock integrity. My work aims to first detect and characterise the microseismicity from these systems, and then to understand these observations in terms of their fundamental physics. I will discuss two representative case studies on this topic. I will first present a study on the Yellowstone Caldera, where we leverage machine learning to detect an enhanced catalogue of earthquakes, and then analyse spatial-temporal nature of its evolution. I then present a study from hydraulic fracturing in Alberta, where I link changes in pore pressure to the topology of microearthquake clusters and then extend this to the reservoir scale. I am interested in collaborating with students on geomechanics and induced seismicity problems across all geo-energy applications such as geothermal, carbon sequestration, nuclear waste storage, unconventional resources, etc. I am interested in working with students from a range of backgrounds such as Civil Engineering, Earth Sciences, Geological Engineering, Energy Resource/Petroleum Engineering.

Biography

I am an assistant Professor in Civil and Environmental Engineering at Western University. I completed my Ph.D. from MIT (2019) in the field of rock mechanics and holds a B.A.Sc. in Mineral Engineering (2013) from the University of Toronto. Prior to joining Western, I was a postdoctoral scholar at Caltech, where my work was focused on the development and application of machine learning and physical models to detect, analyse, and predict mining, oil&gas, and carbon sequestration induced earthquakes. I also worked as a consulting seismologist on greenfield geothermal projects, as well as at Total S.A. in geophysics.

My research interests include rockfall hazard analysis, radar and photogrammetry using ground and drone-based imaging, permafrost, erosion, micro-earthquake monitoring and fracture imaging in rocks at the field scale of mining, civil, and energy applications, as well as using laboratory-scaled models. The goal is to develop data-driven approaches to understand and more accurately predict the onset of failure modes in rock mechanics problems such as permafrost degredation, rock bursts, slope stability and fluid-induced fault slip.

Abstract

Khalifa University's Future Aviation Leadership Center and Outreach Network (FALCON) is a transdisciplinary innovation hub focused on industry-driven research and professional training in sustainable aviation, Advanced Air Mobility (AAM), and trusted autonomous aerospace systems. FALCON partners with prominent national and international organizations to drive advancements in these fields, working closely with industry to co-develop cutting-edge aerospace platforms, as well as next-generation flight and airspace management infrastructure. By transforming aerospace research into deployable solutions, FALCON supports the UAE's vision for net-zero aviation, intelligent mobility, and a knowledge-based economy. FALCON’s projects and initiatives underscore its commitment to tackling the multifaceted challenges of contemporary aerospace systems, including reducing greenhouse gas emissions, fostering energy efficiency, and advancing systems automation. The center bridges academic ingenuity with industrial priorities, positioning itself as a key player in co-developing next-generation aviation platforms and urban airspace management solutions. FALCON's technical pillars are grounded in rigorous scientific exploration: development of clean propulsion systems, optimization of electric and hybrid flight vehicles, and enhanced air traffic algorithms ensuring energy-efficient operations. Furthermore, the program actively supports Research and Development (R&D) in trusted autonomy, ensuring reliability and safety in aerospace design, critical for integrating autonomous vehicles into global aviation infrastructures. Through global collaborations with leading academic institutions, industry, and government organizations, FALCON also influences the evolution of policies and technical standards, amplifying its transformative impact on the aviation industry, both regionally and internationally.

Biography

Roberto Sabatini is a Full Professor in the Department of Aerospace Engineering at Khalifa University of Science and Technology, UAE, where he directs the FALCON Research and Training Program. With three decades of experience in aerospace systems, his work spans academia, industry, and government sectors across Europe, North America, Australia, and the Middle East. He holds a Ph.D. in Aerospace Engineering from Cranfield University (2004) and a Ph.D. in Geospatial Systems from the University of Nottingham (2017). He is also a licensed flight test engineer, airplane pilot, and drone operator. Prof. Sabatini's research addresses key contemporary challenges in avionics, power, and automation, with a special emphasis on the role of cyber-physical systems and artificial intelligence in the digital transformation and sustainable development of the aerospace sector. Throughout his career, he has led numerous industry and government-funded research projects, publishing over 400 peer-reviewed scientific articles and several books. He is a Fellow of IEEE, RAeS, RIN, IEAust, and IETI. Since 2019, he has been listed among the top 2% most cited scientists globally in aerospace and aeronautics (Stanford University Rankings) and was recognized among the top 0.5% of scholars worldwide by the 2024 ScholarGPS Rankings. His contributions to the field also include leadership roles within the IEEE Aerospace and Electronic Systems Society (AESS) and editorial roles in major aerospace, robotics, and navigation journals.

Collaboration interests

Sustainable Aviation, Advanced Air Mobility, Intelligent Vehicle Systems, Autonomous Systems, Avionics, Air Traffic Management, Hybrid-Electric Propulsion, Aircraft Systems, Intelligent Transportation Systems, Digital Twin, Optimization.

Abstract

Chemical flooding is a widely used method for enhanced oil recovery (EOR) in which polymers improve macroscopic sweep efficiency and mobility ratio, while surfactants promote microscopic sweep efficiency by reducing interfacial tension (IFT) between rock and brine. Low-salinity waterflooding is another key technique used in EOR that utilizes the ability of low-salinity water to alter reservoir wettability from oil-wet toward more water-wet nature, thereby increasing oil recovery. Recent advances have shown that low-salinity-polymer flooding (LSP) and low-salinity-surfactant-polymer flooding (LSSP) exhibit synergistic effects on EOR more than either method separately. Preconditioning the high-salinity carbonate reservoirs that are widely spread across the UAE area to low-salinity, may broaden the range of applications for certain low-cost polymers that were previously excluded due to their instability in high salinity. Several factors govern the selection of a suitable polymer, including rock-brine-oil interactions, reservoir conditions, polymer losses due to retention and degradation, polymer injectivity energy requirements, and cost-effectiveness. One way to mitigate the loss of chemicals is to smartly adjust the salinity and ionic composition of injection water. This study will investigate these factors to identify the optimum salinity/ionic composition conditions, polymer concentration, polymer slug size, and polymer type for low-salinity, high-temperature carbonate reservoirs, taking into consideration EOR performance of LSP and LSSP flooding. Experiments will include IFT measurements, Rheological studies, shear resistance and shear degradation assessments, thermal stability evaluation, single-phase core flooding, injectivity tests, and two-phase core flooding. This work is expected to boost oil recovery from UAE mature carbonate reservoirs in a cost-effective manner by leveraging low-salinity conditions, which can reduce chemical losses, and lower energy requirements. Moreover, it will help chemical injection reach its low-carbon footprint goal. Upon promising results, this study can be extended further to a field-scale pilot, which will be the first of its kind here in the UAE.

Biography

Dr. Srinivas Mettu is an Assistant Professor in the Department of Chemical and Petroleum Engineering at Khalifa University, Abu Dhabi, UAE. His research focuses on soft matter, colloids and interfaces, emulsions, wetting phenomena, and surface science, with applications in energy, materials, and chemical engineering. Prior to joining Khalifa University, he held research and academic positions at Xerox Research (USA), Lehigh University, the University of Melbourne, and RMIT University, contributing to advances in interfacial science and complex fluid systems.

Collaboration interests

Seeking collaboration with Western University faculty whose expertise complements research in low-salinity polymer flooding, colloids and interfaces, and enhanced oil recovery (EOR). Particular interest in faculty with expertise in polymer science, rheology, colloid and interface science, porous-media transport, wettability alteration, and reservoir fluid characterization.

Abstract

Agrivoltaics (AV) – the co-location of solar photovoltaic (PV) systems with agricultural land – represents a transformative dual-use strategy with the potential to simultaneously address food security, renewable energy generation, and climate resilience. This presentation draws on a comprehensive body of research conducted at Western University's FAST Lab, encompassing controlled indoor experiments, outdoor field trials, energy modeling, socioeconomic analysis, and a nationwide public survey, to demonstrate the multi-dimensional value of agrivoltaic systems across Canada. Using a 6S Framework – spanning Sustainability, Soil Crop, Socioeconomic, Solar Power, Spatial Efficiency, and Species – findings from a review of 88 global studies suggest agrivoltaics could yield an additional 1,800 million tonnes of crops, generate USD $1.7 trillion in additional revenue, and feed 2.1 billion people. Crop-specific experiments with strawberries, lettuce, turnips, and amaranth under varying PV module transparencies (10%–80%) and types (crystalline silicon and thin-film Cd-Te) demonstrate yield increases ranging from 4% to 483% depending on crop and module configuration. Notably, strawberry agrivoltaics alone could produce 2,861 GWh annually in Canada, generating over CAD $365 million in revenue, while lettuce agrivoltaics could add CAD $68 billion in value and cut 6.3 Mt of CO₂ emissions over 25 years. Energy modeling shows that deploying agrivoltaics on just 1% of Canada's agricultural land could supply 28%-43% of national electricity needs. A nationwide survey further reveals that 85.8% of Canadians support agrivoltaics, with opposition primarily stemming from lack of awareness. The research opens significant opportunities for student collaboration in areas including crop science, solar energy modeling, environmental policy, and socioeconomic impact assessment - making this an ideal entry point for graduate and undergraduate researchers seeking interdisciplinary sustainability projects.

Biography

Dr. Uzair Jamil is a Scientist at the Free Appropriate Sustainability Technology (FAST) Lab in the Department of Electrical and Computer Engineering at Western University, London, Ontario. He holds a PhD in Mechanical and Materials Engineering and is the author of 31 peer-reviewed publications with over 600 citations, specializing in agrivoltaic system design, energy modeling, and sustainable food-energy systems. Dr. Jamil is also the Founding Director of Agrivoltaics Canada, a national non-profit advancing solar-agriculture integration across industry, policy, and research.

Collaboration interests

Dr. Jamil warmly invites researchers, students, and practitioners interested in agrivoltaics to visit WIRED (Western Institute for Renewable Energy Deployment) at Western University. The FAST Lab can support visiting individuals or groups in setting up agrivoltaic test systems, offering hands-on experience with experimental design, PV module configurations, and crop trials. If you are interested in spending time at the lab, collaborating on a project, or simply learning more about what an agrivoltaic setup looks like in practice, please get in touch.

Abstract

Dr. Andreas Schiffer’s research lies at the intersection of mechanics of materials, advanced manufacturing, structural dynamics, and computational modelling, with a strong emphasis on developing materials and structures whose properties can be tailored for demanding engineering applications. His work focuses on architected materials, multifunctional composites, and additively manufactured structures, including cellular, lattice, and hybrid material systems designed for enhanced stiffness, strength, energy absorption, damage tolerance, and functional response. A central theme of his research is the integration of experimental characterization, finite element modelling, and data-driven approaches to understand how material architecture, manufacturing processes, and microstructural features govern mechanical performance across length scales. A significant part of Dr. Schiffer’s current research addresses hybrid additive manufacturing and additive repair. This work includes the development and modelling of repair strategies for damaged or worn high-value metallic components, with the aim of extending service life, improving sustainability, and enabling reliable performance in critical applications. His work also explores multifunctional and self-sensing composite systems, in which structural materials are designed not only to carry loads but also to provide information on deformation, damage, or service conditions. Another distinctive focus of his research is the use of granular-chain systems and solitary-wave propagation for non-destructive evaluation of materials and structures. By studying the interaction of highly nonlinear waves with defects, interfaces, and heterogeneous media, this work seeks to develop sensitive diagnostic methods for material characterization and damage detection. In parallel, Dr. Schiffer has a long-standing interest in impact- and blast-resistant structures, including underwater blast loading and related fluid–structure interaction. Across these areas, his research combines mechanics, manufacturing, experimentation, and modelling to support the design of resilient, lightweight, and multifunctional engineering materials and structural systems.

Biography

Dr. Andreas Schiffer is an Associate Professor in the Department of Mechanical Engineering at Khalifa University (KU) and currently leads the Advanced Materials Technologies cluster within KU’s Advanced Research and Innovation Center (ARIC). He obtained his Diploma in Mechanical Engineering from the Graz University of Technology (Austria) in 2009 and his DPhil in Engineering Science from the University of Oxford (UK) in 2013. He also held a postdoctoral position in the Department of Aeronautics at Imperial College London (UK) before joining KU in 2014.

Collaboration interests

I am interested in developing new collaborations with researchers whose applied expertise complements my work in architected materials, multifunctional composites, additive manufacturing, and advanced modelling. I am looking for a student working in areas that include biomedical engineering, aerospace, defense, energy, and other sectors where lightweight, resilient, sustainable, and multifunctional materials can address practical engineering challenges. I am particularly interested in curiosity-driven research that connects materials design and mechanics with practical applications, including additively manufactured components, impact- and blast-resistant structures, multifunctional composites, and data-driven approaches for structural performance prediction.

Abstract

Recent advances in flexible and low-power electronics have contributed to the fast-paced growth in wearable electronics. However, traditional bulky and rigid electrodes are still used to provide the required energy, sensing, and stimulation capabilities, severely hindering user comfort. To satisfy the flexibility requirements, robust yet lightweight electrodes with enhanced flexibility are needed. Bioinspired materials offer superior functional performance due to their hierarchical organization, extending from the nanoscale to the macroscale. Hierarchical design, integrated with active surface modification, optimizes interfacial performance. This leads to functional enhancements for electrodes used in wearable strain and tactile sensors, as well as in chemical sensors and flexible energy storage devices. This talk will explore various techniques for fabricating hierarchically structured functional electrodes that incorporate different micro- and nano-structured surface features. The design, manufacturing, and optimization of future functional hierarchical structures will rely on a data-driven approach. By integrating three-dimensional functional hierarchical materials printing and machine learning algorithms, we are creating future advanced manufacturing technologies that self-learn and self-improve to create optimized high-performance devices.

Biography

Dr. HaoTian Harvey Shi, is an Assistant Professor and the Director of the Data-to-Manufacturing (D2M) lab in the Department of Mechanical & Materials Engineering at Western University, Ontario, Canada. His research focuses on developing the next generation of functional materials using additive hierarchical manufacturing across multiple length scales. Dr. Shi received his PhD in Mechanical Engineering from the University of Toronto, Canada, as part of the Toronto Institute for Advanced Manufacturing, where he worked on flexible, wearable electrodes for electrochemical energy storage devices. He was a post-doctoral research associate at the University of Cambridge, U.K., with a focus on biosensors and biocompatible functional structures across multiple length scales. He has contributed to several high-impact journals, including Nature Materials, Advanced Materials, Advanced Energy Materials, Advanced Functional Materials, Chemical Engineering Journal, ACS Applied Materials & Interfaces, among others. Dr. Shi also serves on the Early Career Editorial Board for ACS Applied Materials & Interfaces, and he is the Associate Editor of the journal Transactions of the Canadian Society for Mechanical Engineering (CSME).

Abstract

My research program focuses on the development of next-generation multifunctional materials and advanced manufacturing technologies for aerospace, robotics, energy, and healthcare applications. The research integrates constitutive material modeling, multiscale micromechanics, advanced composites, architected metamaterials, additive manufacturing, optimization, and machine learning to design materials and structures with enhanced mechanical, thermal, electrical, and sensing capabilities.

A major focus of the program is the development of multifunctional fiber-reinforced composites incorporating graphene and other two-dimensional materials for structural health monitoring, sensing, and energy-related applications. Our group has developed self-sensing aerospace composites, graphene-enhanced sandwich structures, deployable composite systems, and multifunctional materials capable of simultaneously providing structural and sensing functions.

A second research thrust involves architected materials and additive manufacturing. We develop novel lattice structures, origami-inspired deployable systems, topology-optimized cellular materials, and multi-material 3D/4D printed structures with enhanced buckling resistance, energy absorption, thermal management, and programmable functionality. Recent work includes topology optimization of lattice structures, TPMS-based metamaterials, shape memory polymer systems, and multi-material additive manufacturing for aerospace and robotics applications.

The research combines experimental characterization, X-ray computed tomography (XCT), finite element analysis, constitutive modeling, multiscale simulations, topology optimization, and machine learning-enabled design frameworks. These approaches enable digital engineering of advanced materials from microstructure to structural performance. The program offers numerous opportunities for collaborative PhD supervision in advanced composites, nanomaterials, biomaterials, additive manufacturing, multifunctional materials, computational mechanics, architected materials, and AI-enabled materials design. Collaborative projects can combine expertise in material synthesis, manufacturing, characterization, modeling, and structural applications to address emerging challenges in aerospace, biomedical, energy, and sustainable manufacturing systems.

Biography

Dr. Kamran A. Khan is Associate Professor and Associate Chair of Graduate Studies in the Department of Aerospace Engineering at Khalifa University, UAE. His research focuses on advanced composites, multifunctional materials, architected metamaterials, additive manufacturing, constitutive modeling, and multiscale mechanics. He has published more than 170 journal papers, secured multiple competitive research grants, received Khalifa University’s Top 1% Publication Award, and has been recognized among the Stanford/Elsevier Top 2% Researchers worldwide.

Collaboration interests

I am interested in co-supervising PhD students and collaborating with faculty whose expertise complements one or more of the following areas:

Multifunctional composites and smart materials Nanomaterials and 2D-material-enhanced composites Biomaterials and soft materials Additive manufacturing and 4D printing Architected metamaterials and lattice structures Structural mechanics and computational modeling Topology optimization and machine learning for materials design

Students with backgrounds in Mechanical Engineering, Aerospace Engineering, Materials Science, Biomedical Engineering, Manufacturing Engineering, Computational Mechanics, Applied Mathematics, or Artificial Intelligence are particularly encouraged. Collaborative projects may involve experimental characterization, advanced manufacturing, multiscale modeling, finite element simulation, optimization, and data-driven materials design.

Abstract

Distributed Recycling for Additive Manufacturing (DRAM) has largely focused on recycled thermoplastic feedstocks and filament-based material extrusion processes with open-source, low-cost 3D printing systems. Although these approaches have demonstrated the potential of distributed and circular manufacturing, they only partially represent the variability of locally available resources and waste streams in real-world contexts, limiting the impact of DRAM. Expanding its applicability and relevance, therefore, requires going beyond a limited set of materials, feedstock types, and recycling approaches by matching specific, abundant secondary raw materials with appropriate material development strategies and manufacturing processes for end-use applications. This presentation focuses on current research directions aimed at expanding the range of sustainable materials for applications in DRAM by investigating different locally available waste streams, secondary raw materials, feedstock types, and 3D printing systems. Developed within the FAST research group at Western University, these directions include: (i) recycled thermoplastic feedstocks from additive manufacturing scraps, i.e., recycled PETG for functional applications and comparison with commercial recycled filaments; (ii) recycled polymer blends from post-consumer plastics, i.e., PET/HDPE from plastic bottles in different geographical contexts for large-format particle-based additive manufacturing; and (iii) bio-based composites with residues from the agri-food sector, i.e., biochar-filled virgin and recycled PLA for filament- and pellet-based additive manufacturing. Through material characterization, processability assessment, and application case studies, these works highlight how distributed approaches can support the development of sustainable materials for real-world applications from locally available waste streams. They also show how different material feedstock types, e.g., filaments, flakes, or pellets, influence material properties, processability, and potential applications, leading to different material valorization pathways within DRAM. This perspective also goes beyond single-recycling or downcycling approaches, extending DRAM toward multiple product-lifecycle strategies and more effective use of sustainable materials.

Biography

Alessia Romani is a Postdoctoral Associate at the Free Appropriate Sustainability Technology (FAST) research group, Western University. Her research focuses on the interconnections between design, materials, additive manufacturing, and sustainability, bridging design and materials engineering through open-source technologies. Her current work explores materials and applications in the Distributed Recycling for Additive Manufacturing context.

Collaboration interests

FAST Lab (https://www.appropedia.org/FAST) is interested in collaborating with students and co-supervisors working at the intersection of additive manufacturing, engineering, design, circular economy, and open-source technologies. The interdisciplinary nature of the group supports collaboration across different engineering disciplines and areas of practice, e.g., materials engineering, mechatronics engineering, software engineering, and design engineering. The group especially welcomes projects involving distributed additive manufacturing, open-source technologies, sustainable materials, or circular manufacturing systems. Collaborations on real-world application case studies are particularly welcome and encouraged.

Abstract

Structural degradation of containment infrastructure, specifically bolt cracking within nuclear Refueling Water Storage Tanks, necessitates robotic intervention across an unstructured environment comprising planar surfaces, geometric obstacles, and submerged zones. Current robotic architectures are fundamentally constrained by an inability to transition dynamically between diverse locomotion modes. To resolve this limitation, this doctoral research concentrates exclusively on the synthesis and control of a radiation-hardened hybrid-locomotion platform utilizing an integrated wheel-leg-track morphology. The scope of work encompasses the formulation of adaptive gait-transition algorithms, kinematics for complex obstacle negotiation, and the modeling of fluid-structure interactions during submerged operations. This research will culminate in a robust mobility framework capable of autonomous stabilization and multi-terrain traversal, securing critical nuclear safety systems.

Biography

Dr. Bashar El-Khasawneh is an ASME Fellow and Professor of Mechanical and Nuclear Engineering at Khalifa University, where he directs the Advanced Industrial Robotics (AIR) Lab. His research focuses on parallel kinematic mechanisms and advanced robotic platforms engineered for challenging industrial applications and extreme environments. A dedicated champion of engineering entrepreneurship, he is also the founder and CEO of Dexter Robotics, a commercial spin-off from Khalifa University leveraging his 25 years of global academic and industrial expertise.

Collaboration interests

I am looking to co-supervise a PhD student specializing in the computational modeling, simulation, and hardware design of high-performance robotic systems.

Abstract

We propose a joint KU-Western research program on deformable bubble-driven turbulence, combining interface-resolved DNS with mathematical modeling of inter-scale transport, viscoelastic stresses and bubble growth. Recent DNS from our group at Khalifa University shows that single-bubble wakes and bubble swarms can display spectral features reminiscent of Kolmogorov turbulence while violating the exact assumptions behind the classical 4/5 and 2/3 laws. In parallel, Prof. Roger Khayat’s work at Western University on moving interfaces, bubble growth, thin films and hydraulic jumps provides a rigorous reduced-model framework for identifying dominant balances in complex free-boundary flows. The proposed project will first derive and validate an interface-aware scale-by-scale energy budget for Newtonian bubbly flows, then extend it to viscoelastic liquids where polymer stresses introduce an additional elastic energy reservoir, and finally couple the resulting wake dynamics to scalar transport and bubble growth or dissolution in supersaturated liquids. The outcome will be a predictive framework for how bubbles convert buoyancy into kinetic, interfacial, elastic and scalar transport across scales.

Biography

Immanuvel Paul's research areas include turbulence, thermofluids, and computational fluid dynamics, with a focus on the mathematical and numerical modeling of complex fluid dynamical systems. His work aims to develop predictive frameworks for understanding transport processes and multiscale interactions in turbulent flows, with relevance to aerospace and energy applications.

Collaboration interests

This project is already discussed with Prof. Roger Khayat of Western University, and we both have agreed to supervise a potential PhD student together.

Abstract

My research is concerned with the intelligent prediction of pharmaceutical cocrystals, alongside the synthesis and scalable manufacturing of innovative nanomaterials for applications in drug delivery, CO₂ adsorption, and bio-friendly superabsorbents (bio-SAPs) for hygiene and agriculture industries. Salts and cocrystals are critical solid-form strategies for modifying the physicochemical properties of active pharmaceutical ingredients (APIs). These multicomponent forms are especially valuable for improving the aqueous solubility and bioavailability of drug candidates, 70–90% of which suffer from poor water solubility. However, experimental screening for salts and cocrystals is laborious, resource-intensive, and often produces undesirable physical mixtures. Developing an efficient, user-friendly AI-based framework for predicting cocrystals and salts, as well as estimating solubility, is a key objective of this proposal. This platform aims to provide a robust, AI-driven approach that accelerates the discovery of pharmaceutical solid forms, reducing reliance on trial-and-error experiments and expediting the development of safer, more effective medications. Targeted drug delivery systems are essential to reduce drug dosage and minimize side effects associated with systemic chemotherapy, particularly in cancer treatment. Metal-organic frameworks (MOFs) present promising platforms for safe, efficient, and precisely targeted drug delivery. Environmental safety remains a critical concern, especially in the context of climate change and industrial fire hazards. The research is focused on developing advanced MOF-based composite and hybrid materials for CO₂ capture. Finally, to address the environmental hazards associated with fossil-based synthetic superabsorbents used in products such as diapers, we propose to utilize cellulose derived from agricultural wastes to develop scalable production methods for bio-based superabsorbents for hygiene and agricultural applications.

Biography

I am a professor of chemical engineering. My research deals with the solid-state chemistry of pharmaceuticals and metal organic frameworks. I also use AI tools to predict the solubility of pharmaceuticals and their solid-state forms.

Collaboration interests

I am interested to host funded PhD students, in addition to visiting Khalifa University.

Abstract

Dr. Marwan El-Rich’s research focuses on computational biomechanics of the human musculoskeletal system, with a focus on translating engineering models into clinically useful tools for assessment, rehabilitation, and treatment planning. The work is organized around several connected areas. In spine biomechanics, musculoskeletal and finite element models are used to estimate trunk muscle forces, spinal stability, ligament loading, intervertebral disc stresses, and load-sharing in the cervical, lumbar, and thoracolumbar spine, with applications to low-back pain, degeneration, fusion, scoliosis, spondylolisthesis, and traumatic injury. In gait and movement biomechanics, the research combines motion capture, RGB-camera systems, OpenSim/AnyBody multibody modeling, and deep learning to predict ground reaction forces, joint moments, joint reaction forces, and muscle forces during healthy and pathological gait. A related stream develops rehabilitation technologies, especially cable-driven lower-limb exoskeletons, where simulation and control are used to improve trajectory tracking and gait assistance after stroke or other mobility impairments. The same modeling framework is also applied to orthopedic and pediatric problems, including artificial talus and ankle implant design, hip dysplasia, Pavlik harness treatment, patellofemoral contact mechanics, scoliosis surface-topography assessment, and occupational ergonomics for lifting, reaching, and construction tasks. More recent directions include additive manufacturing, functionally graded implants, porous scaffolds, and osteochondral tissue-engineering constructs and there biocompatibility evaluation. For students, we have ongoing projects on: 1. Development of female geometry based multibody dynamics model. 2. Design, additive manufacturing, mechanical testing, and simulation of osteochondral implants. 3. Design, additive manufacturing, biocompatibility, and osteogenesis of osteochondral implants. 4. Development of tele-rehabilitation tools assisting physiotherapists with functional assessment. 5. Development of OrthoMLLM a multi-modal LLM based orthopedic surgery planning tool.

Biography

Dr. Marwan El-Rich is an Associate Professor of Mechanical Engineering at Khalifa University in Abu Dhabi. His research focuses on computational biomechanics of the human musculoskeletal system, including the thoracolumbar and cervical spine, ankle joint, gait analysis, implants, biomaterials, and the use of artificial intelligence to support subject-specific biomechanical modeling. Before joining Khalifa University in 2016, he was an Assistant Professor in Civil and Environmental Engineering and an Adjunct Professor in Biomedical Engineering at the University of Alberta, and he previously worked as a biomechanics engineering expert at Altair Engineering in France. He holds graduate degrees in mechanical engineering from Polytechnique Montréal and has co-founded MASHYAH, a start-up focused on gait analysis using portable motion sensors and personalized musculoskeletal modeling.

Collaboration interests

I am looking forward to collaborating on data sharing, student and research staff exchange, and working on aligned projects in biomaterials, computational biomechanics, and rehabilitation technologies.

Abstract

Degradable polymers are of growing interest for many areas, including biomedical applications, smart materials and devices, and to address the challenges associated with plastics pollution. Significant progress has been made using backbones such as polysaccharides, polyesters, and a growing number of bio-based polymers. However, in some cases it is desirable to be able to control precisely when and where polymers degrade and to access their degradation under a diverse range of conditions. Self-immolative polymers are a growing class of degradable polymers that undergo controlled end-to-end depolymerization following a stimulus-mediated backbone or end-cap cleavage. This presentation will briefly describe our self-immolative polymer platforms and how they can be employed in applications such as nanopatterning, hydrogels, and self-assemblies that can encapsulate and release therapeutics. It will also briefly touch on our interests in antibacterial materials and other degradable polymer platforms.

Biography

Elizabeth Gillies is a Professor and Canada Research Chair in Chemistry and Chemical & Biochemical Engineering at the University of Western Ontario. She earned her B.Sc. from Queen's University and Ph.D. from UC Berkeley under Jean Fréchet, followed by postdoctoral work at the University of Bordeaux with Ivan Huc. Joining Western in 2006, her research focuses on biodegradable and self-immolative polymers, stimuli-responsive materials, coatings, and polymer assemblies for diverse applications. She has received awards including the Macromolecular Science and Engineering Award (CIC) and the E.W.R. Steacie Memorial Fellowship (NSERC). She is an Associate Editor at the American Chemical Society journal Biomacromolecules.

Collaboration interests

I am interested in collaboratively supervising a student where our group at Western can provide expertise in polymer chemistry and engineering (i.e., design and synthesis of polymers with new properties and applications or custom modifications on existing polymers) and the team at Khalifa can provide expertise on an application area such as biomedical or sustainability.

Abstract

My research program integrates artificial intelligence, computational biology, biomedical imaging, regenerative medicine, and tissue engineering to develop predictive biological systems capable of understanding, forecasting, and optimizing complex biological responses. A distinguishing feature of this work is the use of a closed-loop experimental-computational framework in which biological systems generate multimodal datasets, computational models generate predictions, and experimental validation continuously refines model performance. Current research activities span 2D-3D tissue regeneration platforms, complex wound healing models, regenerative biomaterials, biomedical imaging, environmental health, and computational biology. Ongoing projects include predictive tissue engineering frameworks, digital twins for regenerative systems, biological transport modeling, AI-enabled image analysis, digital phenotyping platforms, and sustainable biomaterial development derived from agricultural, fisheries, and slaughterhouse byproducts. Additional efforts focus on computational and experimental approaches for understanding microplastic-biological invasion/interactions and developing enzymatic remediation strategies. My laboratory combines in vitro and in vivo experimentation with machine learning, image analytics, mechanistic modeling, and multimodal biological data integration to study biological responses across molecular, cellular, tissue, and organ scales. Particular emphasis is placed on linking biological composition, structure, function, environmental exposures, and regenerative outcomes to generate predictive frameworks that support both scientific discovery and translational innovation. We achieve as we also focus on training the next generation of biomedical scientist/engineering. Potential opportunities for PhD co-supervision include regenerative biomaterials, tissue engineering, biomedical imaging, machine learning for health, digital twins, sustainable biotechnology, environmental health systems, and multimodal biological data integration. I am particularly interested in collaborations that integrate experimental and computational approaches to develop predictive frameworks for biological, environmental, and healthcare systems.

Biography

Peter R. Corridon, Ph.D., is a member of the faculty of Biomedical Engineering at Khalifa University and AI Champion for its College of Medicine and Health Sciences. His research integrates artificial intelligence, computational biology, biomedical imaging, regenerative medicine, and sustainable biotechnology to develop predictive biological systems that combine multimodal data, mechanistic modeling, and experimental validation. He leads an interdisciplinary research program focused on digital twins, tissue regeneration, environmental health, and AI-enabled healthcare technologies.

Collaboration interests

Interested in co-supervising PhD students working at the intersection of experimental and computational research. Potential collaboration areas include regenerative biomaterials, tissue engineering, wound healing, biomedical imaging, machine learning for health, digital twins, sustainable biotechnology, environmental health systems, biological transport modeling, and multimodal biological data integration. I am particularly interested in projects that combine predictive modeling with experimental validation to advance healthcare, sustainability, and translational biotechnology.

Abstract

My research program develops AI-driven biosignal analysis and translational medical devices for maternal–fetal and cardiovascular health. As Director of the Healthcare Engineering Innovation Group (HEIG) at Khalifa University, I lead work spanning electrocardiography (ECG) and heart-rate-variability (HRV) analysis, fetal phonocardiography, and explainable machine learning applied to physiological time series. A central theme is converting non-invasive, low-cost sensing into clinically actionable decision support. On the device side, this includes contactless fetal and maternal monitoring and wearable screening for hypertensive disorders of pregnancy — problems that are clinically urgent in the UAE and globally, where early, affordable detection of fetal and maternal cardiovascular risk remains an unmet need.

Biography

Professor Ahsan Habib Khandoker is Professor of Biomedical Engineering and Director of the Healthcare Engineering Innovation Group (HEIG) at Khalifa University of Science and Technology, Abu Dhabi. His research integrates biosignal processing, machine learning, explainable AI, and medical-device development, with a sustained focus on maternal–fetal health and cardiac autonomic function. He maintains active international research collaborations across Australia, Japan, Germany, Korea, and the USA.

Collaboration interests

Seeking a co-supervisor in clinical cardiology, obstetrics / maternal–fetal medicine, or applied machine learning, and a PhD student with a background in biomedical, electrical, or computer engineering (or data science) who has strong signal-processing and programming skills and a genuine interest in translational healthcare AI and medical-device development.

Abstract

Image-guided surgical intervention refers to surgical approaches that use preoperative medical imaging, such as CT and MRI, for diagnosis and surgical planning, together with intraoperative imaging, such as ultrasound and fluoroscopy, to visualize anatomy and guide surgical instruments during an intervention. Image-guided techniques are particularly important in minimally invasive surgery, where procedures are performed through small incisions and surgeons often lack direct access or line of sight to the surgical target. While minimizing trauma to the patient, these constraints raise two fundamental research questions: how can we assist surgeons when the target cannot be seen directly, and how can we enable precise surgical manipulation through small skin incisions, often less than 2 cm?

My research focuses on developing “GPS for surgery” technologies for minimally invasive procedures, with applications in spine, cardiac, and abdominal surgery. Similar to GPS for vehicle navigation, this work integrates surgical planning through medical image processing, including segmentation and image enhancement; sensorized medical instruments; and augmented-reality surgical navigation. This presentation will briefly describe our work on ultrasound- and fluoroscopy-guided treatment platforms for spine metastasis.

Biography

Prof. Elvis C. S. Chen is an Assistant Professor in the Department of Electrical and Computer Engineering at Western University, with affiliations at Robarts Research Institute and Lawson Health Research Institute. Prior to joining ECE, he was an Assistant Professor in the Department of Medical Biophysics at the Schulich School of Medicine & Dentistry. Prof. Chen obtained his PhD from the School of Computing at Queen’s University, Canada.

Collaboration interests

I am looking to co-supervise PhD student in the field of medical imaging computing (MIC) and computer-assisted interventions (CAI), with a focus on using machine learning for image-segmentation and multi-modal image registration.

Abstract

Touch is a critical sensing modality in surgical practice, enabling surgeons to assess tissue properties, apply appropriate forces, and perform delicate manipulations safely. However, minimally invasive and laparoscopic procedures significantly reduce tactile feedback, increasing the risk of tissue injury, imprecise handling, and prolonged operating times. Existing force and tactile sensing technologies for surgical instruments remain constrained by rigid sensor architectures, limited sensitivity, poor sterilization compatibility, signal instability, and challenges in integration within compact surgical tool geometries. This project proposes SoftoSenso, a next-generation bio-inspired microfluidic tactile sensing platform designed specifically for smart surgical instruments and laparoscopic tools. The platform leverages soft elastomeric materials, microfluidic sensing architectures, and AI-driven analytics to restore and enhance tactile perception during minimally invasive procedures. By embedding high-resolution microfluidic tactile sensors into laparoscopic graspers, dissectors, and robotic surgical end-effectors, the system will enable real-time measurement of tissue interaction forces, texture characterization, grip stability assessment, and early detection of excessive pressure that may lead to tissue damage. The project will integrate novel biocompatible elastomeric and hydrogel materials, advanced microfluidic channel designs, miniaturized electronics, and machine learning algorithms capable of interpreting complex tactile signatures from diverse tissue types. The resulting smart surgical tools will provide surgeons with enhanced situational awareness through real-time feedback and decision support, improving precision, safety, and procedural outcomes in minimally invasive and robotic-assisted surgery. Aligned with UAE priorities in healthcare innovation, medical technologies, and artificial intelligence, SoftoSenso aims to deliver clinically relevant, scalable sensing solutions for next-generation surgical systems while advancing the technology to TRL 6, paving the way for translation into operating-room environments and commercial surgical platforms.

Biography

Dr. Wael Othman is an Assistant Professor of Biomedical Engineering and Biotechnology at Khalifa University. His research focuses on surgical innovation through the development of smart surgical tools and feedback systems aimed at improving precision, safety, and clinical outcomes. In addition, his work spans computational physics for two-dimensional material–based biosensing and hydrogen storage applications, as well as dynamic mechanical approaches for the spatiotemporal biomechanical analysis of cell lines and bacterial biofilms. Dr. Wael received his PhD in Mechanical Engineering from New York University in 2023. His research and innovation efforts have been recognized internationally, including Forbes 30 Under 30 in Science and Technology (Middle East) and MIT Innovators Under 35 (MENA).

Abstract

The integration of intelligent nanomaterials and biointegrated sensing technologies is creating new opportunities to address pressing challenges in environmental sustainability, public health, and advanced healthcare. Recent advances in nanotechnology, artificial intelligence, and materials engineering have enabled the development of multifunctional nanostructures with precisely tailored chemical, optical, magnetic, electronic, and mechanical properties. These innovations provide powerful platforms for highly sensitive, selective, and real-time detection of biological, chemical, and environmental targets. Research in Dr. Jin Zhang’s Laboratory for Multifunctional Nanocomposites focuses on the design, synthesis, surface modification, and characterization of advanced nanomaterials and hetero-nanostructures for sensing, diagnostic, and theranostic applications. By integrating machine learning and data-driven approaches throughout the materials-to-device development pipeline, her research seeks to accelerate the discovery of structure-property-function relationships and optimize the performance of next-generation sensing systems. The ability to combine advanced nanofabrication with intelligent modeling enables the rapid development of functional devices with enhanced sensitivity, reliability, and adaptability. A major focus of her research is the development of biointegrated and non-invasive sensing platforms capable of monitoring chemical and biological markers in complex environments. These technologies employ innovative molecular transduction mechanisms and nanostructured interfaces to enable rapid and accurate detection for applications ranging from disease diagnosis and health monitoring to food quality assessment and environmental pollution control. In parallel, her group develops multifunctional nanocomposites for theranostics, integrating diagnostic and therapeutic capabilities within a single platform.

Biography

Dr. Jin Zhang is a Full Professor in the Department of Chemical and Biochemical Engineering at Western University, with affiliations in the School of Biomedical Engineering and the Department of Medical Biophysics. Her research focuses on intelligent nanomaterials, biointegrated sensing technologies, and nanostructured devices for environmental monitoring, healthcare, and biomedical diagnostics. By integrating machine learning with nanomaterial design and device development, her group develops innovative solutions for rapid, non-invasive, and real-time sensing applications. Dr. Zhang has published over 100 peer-reviewed papers, and holds multiple patents. Her research achievements have been recognized through several prestigious awards, including the Ontario Early Researcher Award, Grand Challenges Canada Rising Stars in Global Health, and the Outstanding Mid-Career Achievement in Nanoscience and Nanotechnology in Ontario. She currently serves as Editor-in-Chief of the International Journal of Nano and Biomaterials, and Associate Editor of IEEE Transactions on NanoBioscience.

Collaboration interests

I am interested in co-supervising PhD students and collaborating with faculty members from Khalifa University in the following areas: Nanotechnology and advanced functional nanomaterials, biointegrated and wearable sensing systems; non-invasive diagnostics, lab-on-a-chip and point-of-care diagnostic systems; theranostics and nanomedicine, machine learning and AI for materials discovery and biomedical applications.

Abstract

This proposal will establish a microfabrication-centered platform for producing hydrogel microarchitectures relevant to bone biomaterials, with the primary emphasis on device design, process control, and manufacturable hybrid microfluidic systems rather than end-use biological performance. The project will integrate passive and active microfluidic fabrication strategies to generate monodisperse hydrogel microgels, mineral-loaded droplets, fibers, and modular microporous building blocks with tunable size, morphology, porosity, and compositional gradients. This direction is motivated by prior work showing that microfluidic fabrication can control microgels, microfibers, core-shell particles, perfusable hydrogel networks, and mineralized structures for bone-regenerative materials. The passive component will combine flow focusing, droplet breakup, hydrodynamic mixing, and wettability-guided handling in hybrid devices fabricated by soft lithography and embedded digital-light-processing 3D-printed elements. Building on demonstrated PDMS microchannels containing 3D-printed helical structures for passive micromixing and graphene-oxide-coated structures for droplet trapping/coalescence, the proposed devices will use printed inserts, pillar arrays, and surface-functionalized rails to regulate precursor mixing, droplet stabilization, crosslinker exposure, and controlled hydrogel fusion. The active component will develop polymer-based acoustofluidic modules in which PDMS channels are bonded to COC or related thermoplastic substrates containing embedded pillar or tilted-feature arrays. Piezoelectric actuation will generate localized acoustic radiation forces for hydrogel particle focusing, size classification, residence-time control, and post-fabrication sorting, extending tilted-angle bulk-acoustic-wave concepts toward hydrogel manufacturing quality control. Student collaboration will be central to the project. Students can contribute to CAD design, SU-8/PDMS soft lithography, DLP-printed insert fabrication, surface modification, acoustofluidic testing, COMSOL modeling, automated microscopy, and image-based particle metrology. The expected outcome is a scalable microfabrication toolkit for reproducible hydrogel building blocks that can later be translated into bone-related scaffold and defect-filling studies.

Biography

Anas Alazzam is a Full Professor of Mechanical Engineering whose research focuses on the development of microfluidic systems and microscale devices for a wide range of applications, particularly in the energy and biomedical fields. He leads the Microfluidics Laboratory at Khalifa University, where his work advances innovative technologies at the intersection of microfluidics, energy systems, and bioengineering.

Collaboration interests

This will be a new project where the advisory committee will include members from both institutions

Abstract

As part of the ALLEars project—a collaboration between Western University, the National Centre for Audiology, and partner departments—this work addresses a persistent challenge in pediatric hearing care. Custom earmolds for children's hearing aids must be remade frequently as young ears grow rapidly, and conventional impression-to-earmold turnaround can take two to three weeks per remake, leaving children without full access to sound during critical periods of speech and language development. We are working toward shorter production times, which would be especially valuable for pediatric patients who need replacements often and for clinics aiming to serve more children with limited resources. This work presents a computational pipeline for automating the alignment, repair, trimming, sculpting, and boring of 3D scanned ear impressions. Raw 3D scans of the inner part of the auricle are frequently misaligned and contain numerous geometric defects that block direct downstream processing. We are developing an automated method that localizes and removes problematic regions, restoring meshes to a manifold state, then applies successive transformations: trimming to remove excess geometry, sculpting to shape contact regions, sound bore creation, and mesh smoothing for better fixation. To enable consistent automation across subjects, we employ template mesh projection, mapping a reference "average" ear impression onto new scans while preserving vertex ID correspondence, which allows the same geometric operations to be applied consistently despite individual anatomical variation. The resulting mesh is compatible with 3D printing, further compressing the time from scan to wearable earmold. In future work, we aim to use a growing collection of labelled meshes to predict pediatric ear growth, enabling earmolds to be designed and produced proactively. This pipeline is a step toward faster, accessible, and personalized earmold manufacturing.

Biography

Aliaksei Petsiuk is a Postdoctoral Associate in the FAST Lab at Western University, specializing in the intersection of computer vision, machine learning, and additive manufacturing. His research focuses on material extrusion 3D printing systems, where he integrates intelligent monitoring and optimization algorithms into open-source frameworks for failure detection and toolpath optimization. Complementing his academic work, he has industrial experience in software systems development for additive manufacturing.

Collaboration interests

Computer vision, computational geometry, geometric deep learning.

Abstract

Microfluidic platforms offer a route to process intensification by enabling large numbers of parallelized unit operations within a compact footprint. While such systems have been widely explored for reaction and separation processes, the role of surface chemistry becomes particularly important for the separation of emulsions. Spatially patterned wettability within microchannels can generate local variations in surface energy that promote selective wetting, droplet migration, coalescence, and emulsion destabilization without external energy input. However, achieving robust and selective surface patterning remains challenging due to the diversity of materials used in microfluidic device fabrication and surface modification. This project proposes the development of microfluidic phase separators incorporating tunable nanoporous materials to create selectively patterned surface chemistries for the separation of nanoparticle-stabilized (Pickering) emulsions. The work aims to establish design principles linking nanoporous surface properties, wettability patterns, and separation performance, providing a passive and scalable approach for the treatment of highly stable emulsions.

Biography

Nahla Alamoodi is an associate professor in the Chemical and Petroleum Engineering Department at Khalifa University. Her research interests are in developing sustainable separation technologies and waste valorization.

Collaboration interests

I am seeking a co-supervisor with strong expertise in nanoporous materials and interfacial surface chemistry whose research complements our work in microfluidic separations. The collaboration would focus on developing tunable nanoporous coatings and selectively patterned surfaces for the passive separation of nanoparticle-stabilized emulsions.

Abstract

The transition to a circular bioeconomy depends on engineering robust biological and physicochemical processes that convert waste streams and underutilized resources into valuable products. This presentation surveys an integrated research program spanning fermentation, separations, and water treatment, unified by a common methodology: characterizing microbial or physicochemical kinetics, then designing bioreactor and process systems that translate that understanding to scale.

On the conversion side, the group develops fermentation platforms that valorize lignocellulosic biomass, crude glycerol, and microalgal biomass into fuels (butanol, biodiesel), platform chemicals, and food-relevant products such as biosurfactants and recombinant proteins, using bacterial, fungal, and algal host systems. Work spans the full pipeline from biomass pretreatment (extrusion, pyrolysis, deep eutectic solvents) through strain and metabolic engineering to continuous bioreactor operation supported by real-time and AI-assisted process monitoring and control.

A parallel stream applies related separation and biological principles to water and wastewater treatment: ionic liquid-based extraction for lipid and contaminant recovery, advanced oxidation processes for micropollutant degradation, anaerobic digestion of waste streams, and, most recently, cryopurification and microbial fuel cell treatment of mine-impacted water in cold climates, again integrating AI for process optimization at bench and pilot scale.

Across these areas, the group's strength lies in connecting fundamental kinetic and mechanistic understanding to scalable process design, with extensive international collaboration (Germany, UK, Italy, Netherlands, Thailand) supporting student exchange and joint research.

Biography

Dr. Rehmann is the Associate Dean of Engineering and a Professor of Chemical and Biochemical Engineering at the University of Western Ontario. He is an accomplished mid-career researcher in biochemical and environmental engineering, with over 80 peer-reviewed publications, three patent applications, and a spin-off company. Since joining Western in 2009, he has attracted over $4M in research funding and established a highly automated bioprocessing infrastructure spanning micro- to pilot-scale, unique in Canada.

Collaboration interests

Interested in co-supervision in biochemical engineering.

Abstract

Large intelligent surface (LIS) is an emerging technology for future mobile communication systems. Benefiting from its near-passiveness and reconfigurability, LIS has been recognized as a power-efficient enabler to engineer the wireless channel and enhance the communication performance by manipulating the phase and amplitude of the impinging signals without active circuitry and radio-frequency (RF) chains. Current oversimplified models hinder reliable design of LIS-assisted communication systems. Overlooking mutual coupling, feedback among LIS elements, transmitter, and receiver arrays in the near-field can limit the promised performance gain of such systems. Therefore, developing physically consistent models is the way forward for accurate modeling and prototyping of LIS-assisted communication systems. This project aims to develop physically consistent yet tractable models for LIS-assisted communication systems, explicitly considering mutual coupling and feedback among LIS elements, transmitter, and receiver arrays in the near-field. Such physically consistent models will be contrasted with the widely adopted simplified models, and the simplifying assumptions will be listed. Efficient optimization algorithms will be developed to tune the precoder, combiner, and phase shift matrices. The performance gap between the developed and simplified models will be analyzed and quantified.

Biography

Hamad Yahya is an Assistant Professor with the Department of Computer and Information Engineering at Khalifa University. He received the M.Sc. degree (with Distinction) in Communications and Signal Processing from The University of Manchester, Manchester, U.K., in 2019, he received the Ph.D. degree in Electrical and Electronic Engineering from The University of Manchester, Manchester, U.K., in 2023. Since 2024, he has been affiliated with the Communications and Signal Processing Research Group at Imperial College London in the U.K.

Collaboration interests

This project brings together two closely related areas: RF and communications & signal processing. I would be very happy to collaborate with a co-supervisor who has strong expertise and a solid track record in RF. For the student profile, I am looking for a self-motivated student with a strong applied mathematics background, particularly in areas such as linear algebra and optimization, who is interested in developing physically consistent models for future wireless communication systems.

Abstract

The rapid advancement of artificial intelligence (AI), together with the evolution of communication technologies from 1G to 6G, is enabling increasingly sophisticated vertical applications and cyber-physical systems. These systems involve heterogeneous devices with diverse sensing, computing, communication, and intelligence capabilities operating in highly dynamic and resource-constrained environments. The successful execution of future physical tasks requires new AI paradigms that are computationally efficient, scalable, and trustworthy, while enabling effective collaboration among distributed entities.

Related research activities in Dr. Wang’s group focus on the following research directions. i) Networked Physical Computing. Future physical AI systems will rely on distributed and heterogeneous computing resources with diverse physical characteristics, capabilities, and constraints. Our research focuses on developing networked physical computing frameworks that explicitly incorporate the physical attributes of both computing hardware and tasks, enabling dynamic resource-task matching and task-specific collaborative computing among interconnected devices. ii) AI-Native Network Architectures for Collaborative Computing. Future 6G networks are expected to provide native support for distributed intelligence by tightly integrating communication, computing, and AI capabilities. We are interested in AI-native network architectures that jointly orchestrate bandwidth, computing power, storage, and energy resources to support large-scale collaborative intelligence. iii) Trustworthy AI and Trusted Collaborative Computing. Trustworthy collaboration among heterogeneous intelligent agents represents a fundamental challenge for physical AI systems. Effective collaboration requires aligning diverse application requirements with the capabilities, reliability, trustworthiness, and operating conditions of potential collaborators. Our research aims to develop trusted agent collaboration mechanisms for 6G-enabled networked systems, including trust modeling and evaluation, collaborator discovery and selection, secure task allocation, and trustworthy execution mechanisms.

Biography

Dr. Xianbin Wang is a Fellow of IEEE, a Distinguished University Professor at Western University, and a Tier-1 Canada Research Chair in Trusted Communications and Computing. His current research interests include 5G/6G technologies, physical AI, trusted computing, Internet of Things, machine learning, and AI application in vertical systems. He has over 800 highly cited journals and conference papers, in addition to over 30 granted and pending patents and several standard contributions.

Collaboration interests

I am interested in co-supervising PhD students and collaborating with faculty members from Khalifa University in the following areas: physical AI, networked computing, edge AI, trusted AI, 5G/6G communications, AI-native network design, trusted computing, integrated communication and computing, ISAC, internet of things, and AI application in vertical systems.

Abstract

As unmanned vehicles (UVs) become increasingly deployed in transportation, logistics, surveillance, and smart city applications, they face growing cyber threats targeting sensing, communication, and autonomous decision-making systems. Ensuring the cybersecurity, safety, and trustworthiness of UV systems has therefore become a critical research challenge. This research focuses on the development of AI-driven cyber defense capabilities for UVs through a comprehensive, multi-layered security approach. It aims to support secure and reliable UV operations by advancing intelligent monitoring and adaptive threat detection across multiple system layers. The research is structured around three complementary components. The Internal Network Layer investigates AI-based intrusion detection and response techniques to protect in-vehicle communication networks against attacks on sensors and electronic control units. The Wireless Link Layer develops secure device-to-device (D2D) communication mechanisms, including authentication, key management, and message integrity, to ensure resilient connectivity among high-mobility UVs. The Application Layer focuses on remote attestation, behavioral analysis, and AI-based anomaly detection to identify malicious or abnormal vehicle behavior while enabling privacy-preserving, trustworthy operation. Across all layers, the research leverages advanced machine learning and deep learning techniques to enhance cyber threat detection, anomaly identification, and autonomous security response. The work is intended to support system-level development through simulation, prototype implementation, and evaluation using realistic UV environments and cybersecurity datasets. It provides strong opportunities for interdisciplinary collaboration in AI, cybersecurity, wireless communications, and cyber-physical systems, and is well aligned with Dual PhD co-supervision between Khalifa University and Western University.

Biography

Dr. Chan Yeob Yeun is an Associate Professor in the Department of Computer Science at Khalifa University, UAE and the Cybersecurity Leader of the Center for Cyber-Physical Systems (C2PS). His research interests include cybersecurity, AI techniques for cybersecurity, and lightweight cryptography. Dr. Mohamed Jamal Zemerly is an Associate Professor in the Department of Electrical Engineering and Computer Science at Khalifa University, UAE. His research interests include information security, machine learning, and computer vision.

Collaboration interests

We are interested in co-supervising Dual PhD students and collaborating with faculty in AI, cybersecurity, and autonomous systems. I welcome students from computer science with interests in AI-driven cybersecurity for unmanned vehicles and cyber-physical systems, including intrusion detection, secure communications, and anomaly detection.

Abstract

Next-generation wireless systems require networks that can interpret context, coordinate distributed decisions, and provision services autonomously across fast-changing vehicular, industrial, and smart-city environments. This project advances AI-native 6G network architectures that integrate agentic AI, large language models, graph learning, multi-agent intelligence, deep reinforcement learning, and cloud-edge orchestration to support autonomous network service management. The research develops cognitive and self-organizing frameworks for zero-touch service deployment, microservice migration, adaptive resource allocation, and dependency-aware decision-making across vehicles, edge nodes, base stations, and distributed network functions. Methodologically, the work combines model-driven network representation, learning-based control, and system-level experimentation. Graph neural models will encode topology, mobility, service-chain dependencies, and resource relationships, while large language model agents will support policy interpretation, task planning, and orchestration reasoning. Multi-agent and deep reinforcement learning methods will optimize distributed actions under latency, reliability, mobility, and resilience constraints. The proposed frameworks will be evaluated through simulation, mobility-aware scenario modeling, cloud-edge orchestration environments, and testbed validation, with performance assessed against service continuity, scalability, low-latency operation, security exposure, and robustness under dynamic 5G/6G, vehicular, IIoT, and smart-infrastructure conditions. The joint dual-degree setting will enable doctoral students to work around AI for autonomous vehicles, network security, intelligent digital infrastructure and the development of scalable, context-aware, and resilient orchestration mechanisms for future wireless and vehicular services. The ideal doctoral student will have strong interests in AI/ML, wireless and vehicular networks, reinforcement learning, distributed systems, cloud-edge computing, or service orchestration with motivation to work across theoretical modeling, algorithm design, simulation, and experimental validation.

Biography

Professor Azzam Mourad is an AI advisor to the president and a professor of computer science at Khalifa University, Abu Dhabi 127788, United Arab Emirates, and founding director of the Artificial Intelligence and Cyber Systems Research Center at the Lebanese American University, Beirut, Lebanon. His research interests include applied artificial intelligence AI, agentic and generative AI, cybersecurity, federated machine learning, network and service optimization targeting the Internet of Things, Internet of Vehicles, and cloud/fog/edge computing. He is a Senior Member of IEEE.

Collaboration interests

I am interested in co-supervising doctoral students and collaborating with faculty working on AI-native 6G networks, agentic AI, vehicular and wireless systems, deep reinforcement learning, graph learning, cloud-edge orchestration, network security, IIoT, and smart-city infrastructure.

Abstract

The rapid evolution of autonomous and highly connected vehicles demands robust, multi-band wireless connectivity across diverse communication standards, including GNSS, SDARS, 5G cellular, and Vehicle-to-Everything (V2X) systems. Traditional external antenna solutions, such as visible sharkfin structures, increasingly compromise vehicle aesthetics, aerodynamics, and mechanical reliability. This research program addresses these limitations by developing innovative hidden antenna solutions that are seamlessly integrated within the vehicle's structural components, such as body panels, glass, spoilers, and interior trim. Using an integrated methodology combining theoretical electromagnetic modeling, high-fidelity commercial EM simulations (CST, ADS, Altair Feko), and rapid prototyping, this work characterizes in-vehicle integration environments from 0.5 MHz to 10 GHz. A core focus involves the utilization of advanced material technologies—including transparent conductors, metal foams, and additive manufacturing—to achieve ultra-compact, broadband antenna performance within highly constrained spatial envelopes. Furthermore, the research extends into intelligent adaptive systems, pioneering reconfigurable hidden arrays capable of dynamic beamforming and active electromagnetic compatibility (EMC) noise cancellation to mitigate interference in complex automotive environments. This comprehensive framework bridges the gap between electromagnetic hardware design and autonomous vehicle intelligence. Through this joint dual-degree program, doctoral students will engage in a high-impact training environment spanning advanced RF engineering, co-existence simulation, and vehicle-level validation in collaboration with prominent automotive industry partners. The ultimate objective is to deliver foundational, high-performance communication architectures that enable safe, reliable Level 4/5 autonomous driving while establishing next-generation international standards for structural vehicle connectivity.

Biography

Dr. Ali Attaran is an Assistant Professor in the Department of Electrical and Computer Engineering at Western University, specializing in RF systems, electromagnetics, and wireless communication. He brings over a decade of high-level academic and industrial experience, previously serving as a Senior RF Engineer at Ford Motor Company where he led automotive antenna packaging, integration, and EMC testing. His current research program focuses on hidden antenna architectures, intelligent adaptive systems, and advanced material integration for connected vehicles and biomedical applications.

Collaboration interests

I am seeking to establish a cross-institutional team with a Khalifa University faculty co-supervisor and a dedicated doctoral student who share a strong, hands-on background in applied electromagnetics, RF circuit design, and hardware development. The ideal collaborative partners will possess deep expertise in antenna design, low-noise amplifier (LNA) and filter design, utilizing high-fidelity commercial simulation platforms such as Keysight ADS, ANSYS HFSS, CST, or Altair Feko.

Abstract

The rapid growth of artificial intelligence at the edge is driving demand for computing platforms that simultaneously achieve high performance, energy efficiency, and robust security. My research focuses on the design of advanced hardware architectures, algorithms, and system-on-chip (SoC) implementations for AI inference, edge computing, hardware security, and intelligent embedded systems.

Current research activities span several interconnected areas. The first focuses on efficient AI acceleration through quantization, pruning, computational reuse, approximate computing, and specialized architectures for deep neural networks, transformers, and hyperdimensional computing. The second investigates hardware security mechanisms, including Physical Unclonable Functions (PUFs), AI-assisted authentication, secure edge devices, and hardware accelerators for post-quantum cryptography. A third research direction explores ultra-low-power biomedical and healthcare systems, combining machine learning with custom hardware to enable real-time, energy-efficient monitoring and diagnostic applications.

My research methodology integrates algorithm-hardware co-design, computer architecture innovation, ASIC/FPGA prototyping, and VLSI implementation. The goal is to bridge the gap between emerging AI algorithms and deployable hardware systems capable of operating under strict power, performance, and security constraints. This approach leverages both my academic research experience and extensive industrial background in processor and ASIC design at companies including Apple, Intel, AMD, Qualcomm, and Synopsys.

These research themes provide multiple opportunities for dual-degree PhD collaboration, particularly in AI hardware, edge intelligence, trustworthy computing, post-quantum security, and intelligent healthcare systems. I am interested in co-supervising PhD students working at the intersection of machine learning, computer architecture, hardware security, and VLSI design, with a focus on developing next-generation intelligent and secure computing platforms.

Biography

Hani H. Saleh is a Professor in the Department of Computer and Information Engineering at Khalifa University, UAE. He has over 30 years of combined industrial and academic experience, including leadership and design roles at Apple, Intel, AMD, Qualcomm, Synopsys, Fujitsu, and Motorola. His research focuses on AI hardware accelerators, computer architecture, hardware security, edge AI, post-quantum cryptography, and ultra-low-power intelligent systems. He has supervised numerous PhD and MSc students and has published extensively in leading journals in AI, VLSI, computer architecture, and hardware security.

Collaboration interests

I am interested in co-supervising PhD students and collaborating with faculty members in the following areas:

AI hardware accelerators for deep learning, transformers, and large language models. Efficient edge AI through quantization, sparsity, pruning, approximate computing, and algorithm-hardware co-design. Hyperdimensional computing and emerging computing paradigms for low-power AI. Hardware security, including Physical Unclonable Functions (PUFs), secure AI systems, and trusted edge devices. Hardware and architectural support for post-quantum cryptography. Computer architecture and VLSI design for high-performance and energy-efficient computing. Intelligent biomedical and healthcare systems combining AI, signal processing, and custom hardware. ASIC/FPGA prototyping and hardware-software co-design for next-generation intelligent systems.

Abstract

This research theme focuses on the development of next-generation hardware accelerators for artificial intelligence (AI) and machine learning applications, emphasizing high performance, energy efficiency, and scalability. The research addresses the growing computational demands of modern AI by co-designing algorithms, circuits, and architectures that overcome the limitations of conventional von Neumann computing platforms.

Core research areas include Very-Large-Scale Integration (VLSI) design, in-memory computing, neuromorphic computing, and reservoir computing. In VLSI, the work explores specialized digital, analog, and mixed-signal architectures that accelerate AI workloads while minimizing power consumption and silicon area. In-memory computing techniques are investigated to reduce data-movement bottlenecks by performing computation directly within memory arrays, enabling faster and more energy-efficient processing.

The research also develops brain-inspired neuromorphic systems that emulate neural computation for low-power edge intelligence and real-time learning. Reservoir computing is explored as an efficient framework for temporal data processing, signal analysis, and adaptive learning in resource-constrained environments. By integrating innovations across these domains, the research aims to enable intelligent hardware platforms capable of supporting edge AI, autonomous systems, robotics, signal processing, and future data-centric applications. The overarching goal is to establish efficient computing paradigms that bridge emerging AI algorithms with emerging technologies (memristor, RRAM, ...)

Biography

Baker Mohammad is Professor and Chair of Computer and Information Engineering at Khalifa University, where he also directs the System on Chip Laboratory. With over 16 years of industry experience at Intel and Qualcomm, his expertise includes microprocessor design, hardware accelerators, memory design,, and low-power circuits. He has authored 200+ publications, five books, and 20+ patents, receiving multiple awards for research, innovation, and leadership.

Collaboration interests

Computer architecture, Security experts, Software-hardware co-design

Abstract

The key resource restraint in planetary exploration is the data budget. Modern spacecraft and planetary rovers have sensor capabilities that can generate more raw data in a day than can be transmitted to Earth for processing and analysis. This creates a clear need for scientific autonomy. Rovers need to be equipped with AI-based reasoning engines capable of basic analysis of scientific observations. The role of the scientists on Earth cannot be duplicated. However, an advanced autonomous observer can play an important role in optimizing usage of the data budget. Observations that are flawed because of sensor noise or overexposure should be discarded. Observations that are boring, because they do not provide any new insights, should be given lower priority over observations that might potentially provide human scientists with new avenues of inquiry. All of this drives the need to put modern AI technologies into autonomous spacecraft. However, the state of the art in space-rated computing hardware lags up to a decade behind terrestrial capability. There is at this time no space rated GPU. As a result, there is a need for innovation in space-rated AI hardware. Our group is exploring multiple avenues of research. We are working to distribute AI processing across multiple computing platforms based on space-rated FPGA hardware. We also explore the acceleration of AI models using 8-bit or even 4-bit approximations. Our other recent work involves instruction-level optimization of GPU kernels to maximize utilization of limited compute resources.

Biography

Kenneth A. McIsaac is a Professor in the Department of Electrical and Computer Engineering. His research covers broad applications of autonomous intelligence from space exploration to biomedical applications. Dr. McIsaac has supervised 13 PhD students to completion. His students have gone on to faculty positions in Canada and Iran as well as positions in the NASA-JPL organization, the European Space Agency (ESA) and technical lead positions in industry.

Collaboration interests

I am interested in collaborating with colleagues using modern AI techniques such as the CNN and the ViT for scientific applications as well as colleagues working towards hardware acceleration and distributed implementation of AI models.

Abstract

The deployment of true Physical Artificial Intelligence (PAI) in unstructured environments requires a fundamental shift from rigid automation to adaptive, compliant embodiment. While digital AI architectures have advanced exponentially, a critical bottleneck remains in generalized manipulation and environmental interaction: traditional rigid hardware lacks the physical adaptability to handle real-world uncertainty. The Advanced Robotics and Mechatronic Systems (ARMS) Laboratory at Western University bridges this gap by providing the physical foundation for PAI through intrinsically compliant, torque-controlled actuation. Utilizing proprietary Distributed Active Semi-Active Actuator (DASA) systems powered by Magneto-rheological (MR) and Electro-adhesive (EA) technologies, our research embeds physical adaptability directly into the robot's morphology. This hardware-level compliance allows PAI agents to seamlessly conform to unpredictable surroundings, modulate contact forces natively, and manipulate diverse objects without the computational latency or instability of high-gain software control loops. Our program fuses machine learning and advanced control theory with full-cycle hardware prototyping. By shifting the burden of micro-interaction dynamics from algorithms to compliant hardware, we radically simplify the training of AI models for complex contact tasks. We validate these frameworks across high-impact domains, including intelligent assistive mechatronics and autonomous agricultural robotics for delicate contact operations.

This forward-looking framework offers a premier foundation for Dual Degree PhD collaborations between Western and Khalifa University. Joint doctoral projects will explore the co-design of embodied AI algorithms and compliant hardware, focusing on how intrinsic torque control accelerates reinforcement learning for robust, real-world manipulation. Students will gain interdisciplinary expertise across smart materials, embedded systems, and machine learning, equipping them to lead the paradigm shift toward truly adaptive PAI.

Biography

Dr. Mehrdad R. Kermani is a Professor of Robotics in the Electrical and Computer Engineering Department at Western University, where he directs the Advanced Robotics and Mechatronic Systems (ARMS) Laboratory. His research focuses on collaborative robotics, advanced actuation, and intelligent human-robot interaction. Dr. Kermani is a licensed Professional Engineer in Ontario, and serves as an Associate Editor for IEEE Robotics and Automation Letters, Frontiers in Robotics and AI, and Actuators (MDPI), reflecting his leadership in shaping the future of embodied robotic systems.

Collaboration interests

I seek highly motivated PhD candidates with backgrounds in mechatronics, reinforcement learning, or control systems who want to bridge the gap between AI algorithms and physical embodiment. I welcome collaboration with Khalifa University co-supervisors specializing in machine learning, autonomous systems, or advanced manufacturing.

Abstract

Dr. Sadhu's research is focused on addressing the practical challenges of structural health monitoring and infrastructure asset management while harnessing the capability of modern sensing technology and AI. His current projects involve both theoretical as well as experimental research in areas including structural condition assessment, damage detection, pattern recognition, construction and maintenance digitization, AI, BIM, and information modeling techniques of large-scale structures. This research resulted in numerous research articles, and the proposed algorithms have been successfully implemented in several full-scale structures including bridges, dams, buildings, and highways located in North America and Europe. Dr. Sadhu’s research is funded through Canada Research Chair program, NSERC, Mitacs, CFI, Ministry of Transportation, Conservation Authorities, and various other industry partners. He is the Director of the Smart Cities and Communities (SCC) Laboratory at Western Engineering, which houses a wide range of aerial and underwater drones, ground robots, noncontact sensors, cameras, lidars, and mixed reality system for making structural inspection and asset management more accurate, accessible, and sustainable. His research can be followed through Google Scholar or LinkedIn or Research Gate.

Biography

Dr. Ayan Sadhu is an Associate Professor and Canada Research Chair in Smart and Sustainable Infrastructure. He is the Director of Smart Cities and Communities Laboratory in the Department of Civil and Environmental Engineering at Western University.

Collaboration interests

Co-supervision of students in structural and infrastructure engineering, smart cities and AI-enabled smart inspection systems.

Abstract

Large language models (LLMs) are transforming education by making it possible to generate explanations, answer questions, and provide personalized support at an unprecedented scale. Yet, many concepts remain difficult to learn through text alone. Students often struggle to understand processes, systems, causal relationships, and abstract structures when explanations are presented only in words. Educational research has long shown that carefully designed visual representations can improve understanding, reduce cognitive effort, and support deeper learning. Despite recent advances in generative AI, little is known about how AI can automatically create visualizations that are truly aligned with instructional explanations and educational goals.

This PhD project aims to investigate how artificial intelligence can generate visual representations that complement and enhance instructional explanations. The research will explore methods for transforming textual explanations into pedagogically meaningful visual forms, such as concept maps, process diagrams, timelines, system models, and interactive visualizations. Rather than focusing solely on image generation, the project seeks to understand what makes a visualization educationally effective and how visual representations should be selected, designed, and coordinated with explanatory text.

A central objective is to develop a framework that enables AI systems to identify the key ideas, relationships, and structures within an explanation and generate visualizations that support learning. The research will also examine how factors such as learning objectives, content characteristics, and learner needs influence visualization design. Through a series of classroom and experimental studies, the project will evaluate the impact of AI-generated visualizations on comprehension, engagement, cognitive load, and knowledge retention.

The expected outcome is a new generation of AI-powered educational systems capable of producing multimodal explanations that combine text and visualization in ways that support meaningful learning.

Biography

Abdulhadi Shoufan received the Dr.-Ing. degree from Technische Universität Darmstadt, Germany, in 2007. He is currently an Associate Professor of Computer and Information Engineering at Khalifa University, Abu Dhabi. He is interested in drone security and safe operation as well as in embedded security, cryptography hardware, learning analytics, engineering education, and AI literacy.

Collaboration interests

I am looking for a PhD candidate with strong background in AI models especially generative AI.

Abstract

Generative AI (GenAI) refers to a category of tools and algorithms that can create new output based on an extensive set of inputs (‘training data’). Currently, Large Language Models (LLM) tools that are highly relevant to software engineering include Co-Pilot and ChatGPT. This emerging technology has the potential to increase by many folds the productivity of software professionals. The opportunities are immense, but we are just scratching the surface of the impact of this new technology in the software life cycle phases. I am particularly interested in which ways can GenAI and prompt engineering be used effectively to improve software engineering practices? What is the impact of LLM on software engineering training and education? Are there any drawbacks/pitfalls/risks in adopting GenAI in software development?

Biography

Dr. L. F. Capretz has vast experience in Software Engineering (Human Factors in Software Engineering, Generative AI for Software Development, Software Testing, Software Engineering Education) as practitioner, manager, and educator. He has worked, taught and done research on the engineering of software in North and South America (Canada, Brazil and Argentina), Europe (U.K. and Italy), Middle East (United Arab Emirates), and Asia (Japan, Malaysia, and Singapore).

Collaboration interests

GenAI and Large Language Models (LLM) in Software Engineering, Prompt Engineering for Software Engineering, Software Verification and Validation, Software Testing, Human Factors in Software Engineering; Software Engineering Education

Abstract

Autonomous AI agents are moving from decision support into financial action: reading market information, proposing trades, auditing smart contracts, routing liquidity, and interacting with programmable wallets. My research asks how institutions can constrain, measure, and audit such agents before they affect digital markets. The aim is practical: make autonomous finance inspectable before harm occurs. The work combines formal methods, agent-based simulation, blockchain/security measurement, and verifiable evidence systems for digital-asset and financial-market settings.

The first strand turns human mandates, delegated authority, and policy limits into machine-checkable rules for agent actions. We use typed temporal logic, policy/action schemas, runtime monitors, and pre-execution control planes to check issuer, sector, asset, borrowing, oracle, liquidity, inclusion-risk, and loyalty constraints before execution. The second strand studies agent populations under stress. We build sandboxed DeFi and market scenarios with LLM/DRL agents, hybrid smart-contract auditors, adversarial exploit agents, and market-response agents, then measure vulnerability detection, patch quality, compliance friction, execution-ordering exposure, liquidity response, and harmful coordination. The third strand creates audit evidence: signed logs, selective-disclosure evidence packs, Authorization Evidence Cards, and Agent Trust Rating Cards that allow researchers, institutions, and supervisors to replay decisions without exposing sensitive strategy or portfolio data.

This program suits doctoral co-supervision because it offers distinct but connected projects: formal runtime verification, secure AI-agent architectures, smart-contract security, bilingual financial-event simulation, market microstructure, trustworthy AI evaluation, and digital governance. A Western-KU student could contribute theory, systems prototypes, empirical benchmarks, or human-readable supervisory evidence while working with reproducible public datasets, sandboxed blockchain environments, and open-source releases rather than live funds or customer data.

Biography

Davor Svetinovic is an associate professor in the Department of Computer Science and Associate Chair for Graduate Studies at Khalifa University of Science and Technology, and an ADIA Lab Visiting Fellow. His research focuses on blockchain systems, cybersecurity, software engineering, and secure AI, with recent work on trustworthy autonomous agents, digital assets, and verifiable financial infrastructure.

Collaboration interests

I am interested in Western collaborators and doctoral students working on AI-agent security, formal/runtime verification, trustworthy AI for financial systems, blockchain/DeFi measurement, smart-contract analysis, or agent-based market simulation. Strong student profiles include computer science or engineering backgrounds in AI, cybersecurity, formal methods, distributed systems, financial technology, or empirical software engineering.

Abstract

My research focuses on advancing counterfactual reasoning in artificial intelligence (AI) to improve the transparency, interpretability, and effectiveness of AI-driven decision-making. At its core, we investigates how “what-if” scenarios—known as counterfactuals—can be systematically designed, generated, and integrated into AI systems to better explain and guide their behaviour. Counterfactual explanations describe how small, meaningful changes to input data can alter an AI system’s output and can reveal the underlying decision boundaries. We are developing principled methods for generating high-quality counterfactual explanations, studying how various design choices affect their usefulness, and creating frameworks that allow counterfactual reasoning to be embedded into AI systems across different domains. A key highlight of my research is not just using counterfactual reasoning as an explanation tool, but as a guiding mechanism for detecting previously unseen anomalies. My research aims to address following research challenges: • Designing algorithms to generate realistic, actionable, and diverse counterfactual scenarios • Understanding how different design choices affect the interpretability and usefulness of counterfactual explanations. • Using counterfactuals to detect anomalies that are previously not seen. • Developing visualization tools that allow users to explore and interact with “what-if” scenarios effectively. • Modelling complex relationships between data features to ensure plausibility and consistency in generated counterfactuals. Within the application domain of intrusion detection, these advances will enable systems that go beyond classification to provide actionable insight. The broader impact of this program extends to any context where understanding and explaining anomalous behaviour is critical in domains such as fraud detection in financial systems, fault diagnosis in industrial processes, and health monitoring in medical applications.

Biography

Jagath Samarabandu is a Professor in the Department of Electrical and Computer Engineering and his research spans the use of AI and machine learning in computer vision, medical imaging and cybersecurity.

Collaboration interests

Application of counterfactual explainers for robust and trustworthy AI models in any domain.

Abstract

The rapid emergence of large language models (LLMs) and foundation models is fundamentally reshaping how intelligent systems perceive, reason, and act in the world. My research program sits at the intersection of agentic AI, multi-agent systems, and trustworthy foundation models, with a focus on building autonomous systems that are not only capable but verifiable, secure, and deployable in high-stakes real-world environments.

A core thrust of my work is LLM-guided multi-agent orchestration, mainly designing systems where specialized agents, powered by foundation models, collaborate, negotiate, and adapt dynamically. This includes frameworks for intelligent SLM/LLM routing and coordination (AAMC), LLM-native architectures for network management and metaverse service deployment, and agentic federated learning systems resilient to adversarial manipulation. A unifying challenge across these settings is trust: how do we ensure that autonomous agents behave reliably, explain their decisions, and remain robust under uncertainty and attack?

In healthcare, I apply this agentic paradigm to autonomous robotic echocardiography in collaboration with Cleveland Clinic Abu Dhabi, where foundation model-driven systems control ultrasound robotics in real time for cardiac assessment, a domain where trustworthiness and precision are non-negotiable. Related projects extend agentic AI to CPR optimization and AI-assisted clinical decision-making.

Across all domains, my program integrates formal verification, explainability, and security as first-class properties of agentic systems, moving beyond benchmark performance toward AI that can be audited, certified, and trusted in deployment.

I am actively seeking co-supervision partnerships with Western University faculty working on foundation models, autonomous systems, AI safety, and healthcare AI, toward Dual Degree PhD agreements.

Biography

Jamal Bentahar is a Professor at Khalifa University, Department of Computer Science. His research focuses on trustworthy agentic AI, multi-agent deep reinforcement learning, and the integration of large language models and foundation models for autonomous systems operating in complex, high-stakes environments. His work spans AI-native 6G networks, federated learning, security and formal verification of multi-agent systems, and autonomous healthcare AI.

Abstract

Autonomous systems are increasingly expected to operate safely and reliably in complex, dynamic, and uncertain environments. Achieving this vision requires more than accurate perception and demands AI systems that can understand their surroundings, reason about uncertainty, interpret human behaviour, and adapt their decisions in real time. My research focuses on developing trustworthy and explainable multimodal AI for autonomous systems by integrating sensing, perception, reasoning, and decision-making within a unified framework. A major research theme is multimodal perception, where information from complementary sensing modalities, including multi-camera system and multiple modalities (RGB, thermal, depth), LiDAR, and contextual data, is fused to achieve robust scene understanding under adverse conditions such as poor illumination, weather degradation, occlusion, and sensor failures. We develop lightweight/deployable and uncertainty-aware models that operate on resource-constrained platforms while maintaining reliability and transparency. A second focus is human-centred and conditional autonomy. We investigate camera-based monitoring of human attention, vigilance, cognitive state, trust, and intent to enable safer and more effective collaboration between humans and intelligent systems. This includes driver monitoring, human-in-the-loop autonomy, adaptive control, and explainable decision-making for safety-critical applications. More recently, our research has expanded toward Vision-Language-Action (VLA) models for end-to-end autonomous navigation and embodied intelligence. We build multimodal foundation models that can be combined with reinforcement learning and uncertainty-aware reasoning to create autonomous agents capable of perceiving, reasoning, planning, and acting in complex environments while providing interpretable explanations for their decisions. Research outcomes are validated using autonomous robotic platforms, driving simulators, multimodal sensing systems, and real-world datasets through collaborations with industry and government partners. This research offers strong opportunities for dual-degree PhD collaborations in autonomous systems, robotics, multimodal AI, embodied intelligence, intelligent sensing, and trustworthy machine learning.

Biography

Soodeh Nikan is an Assistant Professor in the Department of Electrical and Computer Engineering at Western University and Director of the AiX Lab (AI for Every Application). Prior to joining Western, she worked as an AI/ML Research Engineer at Ford Motor Company’s R&A division in the US, where she contributed to perception and driver-monitoring technologies for intelligent vehicles. She has secured competitive research grants from NSERC, Mitacs, the Ontario Ministry of Transportation, and many industry collaborations. Her work has established collaborations with organizations including the National Research Council of Canada (NRC), General Dynamics Land Systems (GDLS), and international partners. She has supervised more than 50+ highly qualified personnel and published in leading AI and computer vision venues, including IEEE Transactions journals, CVPR, and NeurIPS. She is an IEEE Region7 counsellor and an ICF liaison, actively serves the research community through editorial, conference, curriculum, and IEEE leadership roles, and has delivered invited international keynote presentations on AI and autonomous systems.

Collaboration interests

I am interested in co-supervising PhD students and collaborating with faculty working in autonomous systems, multimodal AI, computer vision, Vision-Language and Vision-Language-Action models, reinforcement learning, intelligent sensing, healthcare AI, and trustworthy machine learning.

Abstract

Abstract (250–300 words) — Our research encompasses applications of machine learning in different domains such as medical imaging, cybersecurity, and environmental monitoring. One of our recent research topics is on “Metal Artifact Reduction (MAR)” in CT images. In this work, we investigate a soft artifact map feature-guided Swin-UNet for image-domain CT-MAR. A continuous artifact guidance map is constructed from the discrepancy between the metal-affected input and its linear interpolation (LI) reconstruction. This map is used as a bottleneck feature-gating signal, allowing artifact severity information to modulate the learned representation without requiring raw projection data or adversarial training. Experiments on the AAPM CT-MAR dataset show that the Swin-UNet with MA+LI input provides the strongest global fidelity in terms of PSNR, masked SSIM, and RMSE, while artifact-map concatenation and feature gating provide insight into how LI-derived priors affect local restoration. We are open to collaborations with Western University’s faculty and students on any topics related to machine learning in medical imaging.

Biography

Dr. Zeyar Aung is currently an Associate Professor in the Department of Computer Science, Khalifa University. His research interests include Machine Learning, Data Science, and Artificial Intelligence.

Collaboration interests

I am open to collaborate with students and co-supervisors who are interested in Machine Learning applications in Medical Imaging.

Abstract

My research focuses on developing trustworthy agentic medical AI systems that can reason over heterogeneous clinical data while remaining reliable, uncertainty-aware, explainable, and safe for deployment. Modern healthcare increasingly depends on multimodal information, including medical images, radiology and pathology reports, clinical notes, laboratory values, and longitudinal patient records. My work aims to build AI models and clinical agents that can integrate these sources to support screening, diagnosis, triage, risk prediction, report generation, and follow-up planning.

Methodologically, my research spans medical vision-language models, multimodal foundation models, conformal prediction, uncertainty estimation, hallucination mitigation, domain generalization, active learning, self-supervised learning, and explainable medical imaging. A central theme is to move beyond accuracy-only evaluation toward systems that can express calibrated uncertainty, provide evidence-grounded outputs, remain robust under hospital and population shifts, and identify when human expert review is needed. Recent directions include uncertainty-aware medical VLMs, reliable prediction sets for clinical decision support, multimodal reasoning over images and text, and safe adaptation of AI models across clinical environments.

This research offers strong potential for Dual PhD student collaboration between Khalifa University and Western University. Possible joint PhD topics include trustworthy medical foundation models, EHR-image fusion, uncertainty-aware clinical prediction, medical VLM evaluation, hallucination-safe clinical AI agents, multimodal learning for radiology, pathology and ophthalmology, and deployment-oriented AI validation. Students would gain experience in both advanced AI methodology and clinically relevant health technologies, with opportunities to work on algorithm development, benchmark design, model validation, and translational healthcare applications. The long-term goal is to train researchers capable of bridging foundation-model AI with safe, reliable, and human-centered clinical decision support.

Biography

Dr. Dwarikanath Mahapatra is an Assistant Professor in the Department of Computer Science at Khalifa University, UAE. His research focuses on trustworthy medical AI, medical vision-language models, multimodal foundation models, uncertainty estimation, hallucination mitigation, and safe clinical deployment. He has prior research and leadership experience at IBM Research, ETH Zurich, Inception AI, LocAI, and Yotta Data Services.

Collaboration interests

I am interested in co-supervising PhD students working at the intersection of AI methodology and translational healthcare applications. Suitable student profiles include backgrounds in machine learning, computer vision, medical image analysis, multimodal learning, natural language processing, foundation models, or biomedical/clinical engineering, with interest in trustworthy AI, uncertainty estimation, explainability, and clinical decision support.

I am particularly interested in Western University co-supervisors working on medical imaging, biomedical engineering, health informatics, clinical AI, multimodal learning, EHR-based prediction, uncertainty-aware AI, or AI safety/evaluation for healthcare. Potential collaboration topics include trustworthy medical foundation models, EHR-image fusion, medical vision-language models, hallucination-safe clinical agents, and deployable AI systems for radiology, pathology, ophthalmology, and longitudinal patient management.

Abstract

Modern engineering systems increasingly rely on AI for modelling, monitoring, optimization, and decision-making. However, many real-world engineering problems involve limited or noisy data, complex physical constraints, uncertain operating conditions, and the need for reliable and interpretable predictions. My research program addresses this challenge by developing domain-informed AI methods that integrate modern machine learning with engineering knowledge, mechanistic models, process constraints, and uncertainty-aware optimization. Rather than treating AI as a black-box predictor, our goal is to build models that are physically meaningful, transferable, and useful for real engineering decisions.

The methodological core of my group’s work includes physics-informed machine learning, neural ordinary differential equations, hybrid models, Gaussian process surrogates, Bayesian and multi-objective optimization, uncertainty quantification, interpretable learning, and AI-assisted engineering knowledge extraction. These methods are primarily applied to sustainable process and environmental systems, including hydroprocessing, wastewater treatment, predictive maintenance, and industrial process monitoring. Across these applications, the common research question is how to adapt, validate and deploy AI/ML tools for systems where physical consistency, limited data, and engineering constraints matter.

This research would create opportunities for PhD co-supervision between Khalifa and Western. Potential research or projects scopes could include Physics informed AI for energy and environmental systems, trustworthy AI for engineering monitoring and control, edge-AI models for real-time sensing, hybrid modeling of industrial processes, or AI-enabled optimization of low-carbon and resource-recovery technologies. These projects would suit students from chemical, environmental, mechanical, petroleum, electrical, computer, or information engineering backgrounds who are interested in combining machine learning, domain knowledge, optimization, and experimental or industrial data.

Biography

Dr. Tianlong (Taylor) Liu is an Assistant Professor in the Department of Chemical and Biochemical Engineering at Western University and PI of the SustainAI Group. His research develops domain-informed AI, physics-informed machine learning, optimization, and decision-support methods for complex engineering systems. His recent work spans hydroprocessing, wastewater treatment, self-driving labs, predictive maintenance, environmental forecasting, and AI-assisted engineering knowledge automation.

Collaboration interests

I am interested in co-supervising dual-degree PhD students with Khalifa University working at the intersection of AI, engineering systems, sustainable energy, environmental technologies, edge intelligence, process modelling, and trustworthy decision support. Potential student profiles could be chemical, environmental, petroleum, mechanical, electrical, computer, or information engineering backgrounds, with interests in physics-informed machine learning, hybrid modelling, digital twins, optimization/control, uncertainty quantification, edge AI, and experimental or industrial data-driven applications.

Abstract

The proposed research focuses on developing LLM-driven industrial agents that integrate large language models (LLMs), domain knowledge, physics-based models, and machine learning to enable intelligent process optimization across manufacturing, energy, and sustainability applications. Traditional optimization approaches often rely on isolated datasets and predefined models, limiting their ability to adapt to evolving process conditions and leverage the rapidly growing body of scientific and operational knowledge. This research aims to create autonomous AI agents capable of understanding complex industrial processes, reasoning over multimodal data, interacting with digital twins, and providing actionable recommendations for process improvement.

The research will develop a unified framework that combines LLMs with advanced sensing systems, process simulation tools, optimization algorithms, and retrieval-augmented knowledge bases. These industrial agents will continuously acquire information from sensor networks, operational databases, technical reports, and scientific literature to support decision-making. By integrating physics-informed machine learning and digital twin technologies, the agents will generate interpretable insights, identify process bottlenecks, predict system performance, and recommend optimal operating conditions in real time.

The framework will be demonstrated in several high-impact application domains. In biomass pyrolysis, LLM-driven agents will analyze experimental and literature data to optimize conversion efficiency, product yield, and process sustainability. In battery design and energy storage systems, the agents will assist in materials selection, cell design optimization, degradation analysis, and state-of-health management. In smart nutrition, AI agents will synthesize nutritional knowledge, user preferences, and health objectives to generate personalized dietary recommendations and support sustainable food system design.

The project offers significant opportunities for student collaboration. Students will gain hands-on experience in LLM development, AI agents, machine learning, optimization, digital twins, scientific computing, and data analytics. Working closely with industrial and academic partners, trainees will contribute to cutting-edge interdisciplinary research while developing the technical and leadership skills required to advance the next generation of intelligent engineering and decision-support systems.

Biography

Dr. Min Xia is an Associate Professor in the Department of Mechanical and Materials Engineering at Western University and Director of the MIN Lab. His research focuses on industrial artificial intelligence, large language model (LLM)-driven agents, digital twins, machine condition monitoring, smart manufacturing, sustainable energy systems, battery intelligence, and smart nutrition.