Find Academic Supervisors

Juan Carlos HatchondoView profile

Professor

• Macroeconomics
• International Finance
• Sovereign Debt
• +2 more

Juan Carlos Hatchondo is Professor and Bank of Montreal Professor (2020-2025) at the University of Western Ontario, where he serves as Director of the Graduate Program in the Department of Economics. He holds a Ph.D. from the University of Rochester (2005) and previously held positions at Indiana University and the Federal Reserve Bank of Richmond. His research explores Macroeconomics , International Finance , and Sovereign Debt , with particular focus on: Credit risk modeling in sovereign debt markets Fiscal policy frameworks for economic stability Rollover risks in international reserves management Mortgage default dynamics and financial regulation His publication record demonstrates consistent focus on sovereign default mechanisms, with recent work emphasizing debt restructuring innovations and fiscal rule design for economic unions. The research employs advanced quantitative methods to address policy-relevant questions in financial stability. As Director of Graduate Studies, he oversees doctoral training in Economics and maintains collaborations with institutions including the IMF. His work has been published in leading journals including American Economic Review , Journal of Political Economy , and Journal of Monetary Economics .

• Smart Grids
• Power Distribution Networks
• Renewable Energy Systems
• +3 more

Ayman Eltantawy is an Adjunct Assistant Professor at the University of Waterloo. His affiliations include membership in the Adjunct faculty and Faculty groups. His research focuses on advancing smart grid technologies, particularly in hybrid AC-DC distribution systems, renewable energy integration, and energy storage solutions. Key areas of interest include stochastic planning frameworks, load flow analysis, and optimization algorithms for improving grid reliability and efficiency. His work emphasizes practical applications such as PATREM (Performance Assessment Tool for Remote Electrical Microgrids), flywheel storage enhancement, and online DG connection assessments. The articles highlight trends in hybrid system configurations, renewable resource management, and grid modernization strategies. Notably, his research bridges theoretical models with real-world implementation challenges in smart grid infrastructure. While no specific advising or grant information is provided, his contributions to grid optimization and energy storage indicate active engagement in collaborative academic and industrial projects. His publications span from 2011 to 2019, reflecting sustained focus on evolving smart grid challenges.

Luke SchaefferView profile

Assistant Professor

• Quantum Computing
• Theoretical Computer Science
• Combinatorial Game Theory
• +4 more

Luke Schaeffer is an Assistant Professor at the University of Waterloo, affiliated with the Cheriton School of Computer Science and the Institute for Quantum Computing (IQC). His academic journey includes a BMath and MMath from Waterloo, a PhD from MIT under Scott Aaronson, and postdoctoral positions at Waterloo and UMD. His research bridges quantum computing and theoretical computer science, with focuses on quantum circuit complexity, classical simulation of quantum systems, and combinatorics on words. Notable projects include studying the Clifford group's structure, low-depth quantum vs. classical circuits, query complexity of regular languages, and fermion-to-qubit encodings. He also explores non-quantum areas like combinatorial game theory and cellular automata. His work on interactive quantum advantage protocols demonstrated separations between quantum and classical circuit models, including results against AC⁰[p] and NC¹ classes. He co-developed sample-optimal classical shadow algorithms for pure states and classified Clifford gates over qubits into 57 distinct classes. Education: BMath and MMath, University of Waterloo PhD in Computer Science, MIT (advisor: Scott Aaronson) Key Research Themes: Quantum advantage and circuit complexity Automata and combinatorics on words Classical simulation techniques for quantum systems Algorithmic foundations of quantum computing He advises students in the Cheriton School of Computer Science, emphasizing quantum computing and theoretical computer science. His contributions include foundational results in quantum-classical separations and decidability of word properties via first-order logic.

• Conflict Resolution
• Environmental Policy
• Climate Change
• +7 more

Keith Hipel is an Adjunct Professor at the University of Waterloo's Department of Systems Design Engineering, within the Faculty of Engineering. His work focuses on systems analysis, conflict resolution methodologies, and environmental policy. He specializes in applying the Graph Model for Conflict Resolution to real-world disputes involving water resources, climate change impacts, and resource management. Research interests include: Development of conflict resolution frameworks Climate change adaptation strategies Multi-stakeholder decision-making systems Environmental policy design Systems thinking applications Recent publications emphasize: strategic analysis of water allocation disputes, climate policy equity, wildfire management, and aggregate mining conflicts. His work often combines game theory, multi-criteria decision analysis, and fuzzy logic to address complex socio-technical systems. Notable projects include analyzing the Cauvery River dispute, Alberta wildfire management strategies, and Ontario's aggregate mining controversies. Hipel collaborates internationally on issues like Himalayan glacier melt impacts and transboundary water governance. His methodologies bridge engineering and social sciences, focusing on creating practical tools for resolving conflicts in environmental and infrastructure domains through structured decision-making processes.

• Memristor Technology
• Nanomaterials
• Neuromorphic Computing
• +3 more

Sun Sun is an Adjunct Professor at the University of Waterloo, specializing in advanced memristor technologies and their applications in neuromorphic computing, biomedical engineering, and artificial intelligence. Their research focuses on developing novel memristor-based devices for sensor integration, energy harvesting, and bionic systems. Key research areas include organic nanomaterials, hybrid heterostructures (e.g., TiO2@MoO3, HfOx/FeOx), and biomaterials such as silk fibroin for implantable devices. Sun Sun explores memristor behaviors like negative differential resistance, resistive switching, and self-rectifying effects to enhance device functionality and scalability. Applications span healthcare (e.g., rapid tumor cell detection, follicular fluid monitoring), neuromorphic systems (e.g., artificial synapses, emotional perception circuits), and energy systems (soil-based energy harvesting). Their work often emphasizes multi-factor regulation of memristive properties and interdisciplinary integration with photonics, robotics, and sustainable materials. Collaborative projects include designing flexible and transparent memristor-based systems for wearable electronics and neuromorphic chips. Sun Sun’s contributions bridge fundamental device physics with real-world biomedical and AI applications, leveraging both organic and inorganic materials for next-generation smart technologies.

Michael CollinsView profile

Professor

• Renewable Energy Systems
• Thermal Engineering
• Building Energy Efficiency
• +5 more

Michael R. Collins is a Professor and Department Chair at the University of Waterloo. His expertise spans renewable energy systems, thermal engineering, and sustainable building technologies. He focuses on advancing energy efficiency in HVAC systems, solar thermal applications, and fenestration heat transfer analysis. His research includes optimizing drain water heat recovery systems, solar-assisted heat pumps, and complex window systems with shading devices. Key contributions include experimental and numerical studies on thermal performance, system modeling, and validation. Recent work explores multi-mode energy systems, control strategies for heat pumps, and material properties for energy applications. Collins' research bridges theoretical models with practical implementations, addressing critical challenges in building energy efficiency and sustainable technologies. His publications span over two decades, with a focus on solar thermal systems, fluid dynamics in energy recovery, and optical properties of building materials. He has contributed to international collaborations and standards in photovoltaic/thermal systems through IEA initiatives.

Jan HuissoonView profile

Professor

• Mechatronics Engineering
• Robotics
• Autonomous Vehicle Control
• +4 more

Jan Huissoon is a Professor in the Mechanical and Mechatronics Engineering Department at the University of Waterloo, where he previously served as Chair (2013–2020) and founded Canada's first Mechatronics Engineering program. He holds a Ph.D. and Bachelor's in Engineering Science from Trinity College, Dublin. His research focuses on mechatronic design, sensor-based robotics, autonomous vehicles, and microfluidic control, with notable contributions to robotic welding and intelligent transportation systems. Education: 1983: Doctorate in Mechanical Engineering, Trinity College Dublin 1979: Bachelor of Engineering Science, Trinity College Dublin Research Interests: Autonomous vehicle navigation using neural networks Sensor-based control for high-speed robotic welding Microfluidic device development and fluid dynamics Assistive robotics and human motion modeling Intelligent sensing systems (ultrasonic/vision-based) Industrial Experience: Consultant for Paradigm Products Inc. (2005–2007) Expert witness for patent infringement cases (2005–2007) Contract work in mechatronic and machine design (2005–2007) Labs/Teams: While no specific lab names are listed, his work involves collaboration with interdisciplinary teams focusing on robotics, microfluidics, and autonomous systems. He has developed open-source hardware like the µPump for microfluidic applications. Grants/Advising: Currently not accepting graduate students but holds Sole-Supervisory Privilege Status (SSPS). His research has produced over 100 publications and multiple patents, including foundational work in robotic calibration and welding automation.

• Human-Technology Teaming
• Motor Control
• Cognitive Function and Performance
• +3 more

Heather Neyedli, PhD, is an Associate Professor in the School of Health and Human Performance at Dalhousie University. Her research focuses on human-technology interaction in military and sports contexts, motor learning, and decision-making processes. She leads the Cognitive and Motor Performance Lab and coaches the Dalhousie Women’s Rugby Team. Education: Postdoctoral Fellowship: Nuffield Department of Clinical Neurosciences, University of Oxford PhD: Kinesiology and Physical Education, University of Toronto M.A.Sc: Mechanical and Industrial Engineering, University of Toronto BSc (Honours): Kinesiology, Dalhousie University Research Interests: Dr. Neyedli explores how humans interact with AI, automated systems, and technology. Key areas include: Trust in automation (e.g., military systems, sports technology) Cognitive and motor performance adaptation Error correction and skill acquisition Human decision-making under uncertainty Recent Publications: Her work spans VR training efficacy, trust calibration in automation, and team performance in complex missions. Recent studies examine navigation performance with haptic feedback and the impact of automated planning aids on situation awareness. Labs/Teams: Directs the Cognitive and Motor Performance Lab, collaborating on projects involving neurorehabilitation, sports biomechanics, and human-technology interfaces.

• Quantum Information
• Quantum Optics
• Atom-Light Interactions
• +3 more

Daniel Higginbottom is an Assistant Professor in the Department of Physics at Simon Fraser University. His research focuses on quantum information, quantum optics, and silicon photonics, with particular emphasis on silicon-based quantum technologies such as spin qubits and long-distance quantum communication. He leads the Silicon Quantum Technology Group, supervising multiple PhD and MSc candidates. His work involves experimental and theoretical exploration of atom-light interactions in silicon materials, aiming to develop scalable quantum devices. Education: PhD in Physics from Australian National University (2019), and a PhB in Science from ANU (2011). He teaches PHYS 455 D200 and PHYS 855 G200 courses on Modern Optics in Fall 2025. Research interests span quantum optics, silicon photonics, and condensed matter spin qubits. His group develops silicon T-centers for single-photon emission, nanophotonic cavities, and integrated quantum systems. Recent work includes electrically-triggered spin-photon devices and efficient photon collection from silicon defects. Publications highlight advancements in quantum networking, single-spin detection, and photonic integration. His team collaborates on silicon quantum technology platforms and explores applications in distributed quantum computing. Current lab members include PhD candidates Michael Dobinson, Austin Wolverton, and others. He is affiliated with the Silicon Quantum Technology Group and maintains an active Google Scholar profile.

• Computer Vision
• Artificial Intelligence
• Neuroscience
• +5 more

John K Tsotsos is a Distinguished Research Professor and Director of the Centre for Innovation in Computing at the Lassonde School of Engineering, York University. He also holds adjunct professorships in Computer Science and Ophthalmology & Vision Sciences at the University of Toronto. With a PhD in Computer Science from the University of Toronto (1980), his work bridges computer vision, neuroscience, and autonomous systems. His research focuses on visual attention mechanisms, active vision, and their applications in driver behavior analysis, autonomous vehicles, and cognitive modeling. Notable projects include SCOUT+ for drivers’ gaze prediction and studies on 3D visuospatial problem-solving. His publications span over 400 articles, emphasizing interdisciplinary approaches to understanding human and machine vision. Recent work explores data limitations in vision datasets, perceptual grouping in neural networks, and temporal attention effects. While no specific awards are highlighted, his leadership roles and prolific output reflect significant contributions to the field. His research group at York University drives innovation in AI-driven perception systems and human-centric autonomous technologies.

Wael H. AhmedView profile

Professor

• Multiphase Flow Analysis
• Thermo-Fluid Dynamics
• Energy Systems
• +4 more

Dr. Wael H. Ahmed is a Professor at the University of Guelph's Department of Mechanical Engineering. His research focuses on multiphase flow analysis for energy, water, and food systems, including thermo-fluid dynamics of mechanical systems. He teaches undergraduate courses such as Fluid Mechanics, Thermodynamics, and Heat Transfer, and graduate courses on phase change heat transfer and two-phase flow. His research lab, the Flow, Energy and Dynamics Solution (FEADS) lab, collaborates with industry to develop sustainable solutions for mechanical systems, addressing fluid-structure interaction challenges. Current projects involve optimizing airlift pumps for aquaculture, biogas production, and CO2 capture. Dr. Ahmed emphasizes hands-on learning, engaging students in real-world engineering problems through 'Learning by Doing' initiatives. Key research areas include multiphase flow dynamics, two-phase flow instabilities, and energy-efficient technologies. He has developed innovative pumping systems and sensors for industrial applications, such as the FloNergia airlift pump commercialized with academic-industry partnerships. His work integrates computational fluid dynamics (CFD), experimental validation, and machine learning for flow pattern identification. Recent projects explore applications in nuclear power plant safety, catalytic converter flow analysis, and sustainable energy systems design.

Nikola GradojevicView profile

Professor

• Empirical Asset Pricing
• Market Microstructure
• High-Frequency Finance/Big Data
• +11 more

Dr. Nikola Gradojevic is a Professor of Finance at the Department of Economics and Finance, University of Guelph, affiliated with the Gordon S. Lang School of Business and Economics. He holds dual appointments as a Senior Fellow at the Rimini Center for Economic Analysis (RCEA) and the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI). His career includes roles at institutions such as the Bank of Canada, Federal Reserve Bank of St. Louis, and IÉSEG School of Management, where he served as Head of Finance, Audit & Control Department and Associate Dean (Academic). Education: Ph.D. in Financial Economics (University of British Columbia), M.A. in Economics (University of Essex and Central European University), and B.Sc./M.Sc. in Electrical Engineering (University of Novi Sad). Research focuses on empirical asset pricing, market microstructure, high-frequency finance, AI applications in finance, and energy commodities. He has pioneered work on non-linear forecasting models using deep learning and fuzzy logic, including patents on AI validation and model risk management systems. Key contributions include analyzing cryptocurrency volatility cascades, natural gas price dynamics, and the impact of crises like Brexit and the 2020 pandemic on financial markets. Publications appear in leading journals like IEEE Transactions on Neural Networks , Journal of Banking and Finance , and Annals of Operations Research . He frequently advises on model risk management and monetary policy, contributing to media outlets such as CBC News and Finder.com. Awards: Paul MacPherson Teaching Award (2020), CARE-AI Seed Fund (2022), SSHRC Grant (2008–2011). Funding: Institute for Sustainable Commerce, RCEA grants, and multiple institutional research awards. His research bridges finance, engineering, and AI, emphasizing ethical applications of machine learning in financial decision-making. Current work explores AI explainability in volatile markets and energy commodity forecasting under geopolitical instability.

Stephen ChenView profile

Associate Professor

• Evolutionary Computation
• Swarm Intelligence
• Combinatorial Optimization
• +2 more

Stephen Chen is an Associate Professor in York University's School of Information Technology, specializing in metaheuristic optimization techniques. His research investigates particle swarm optimization, evolutionary computation, and hybrid search algorithms for complex problems. Research Focus: Algorithm behavior analysis, dimensionality challenges in optimization, swarm intelligence frameworks, and real-world applications in energy/scheduling systems. Education: PhD in Robotics (Carnegie Mellon), BASc (Toronto). Grants & Projects: NSERC-funded research on selection-based metaheuristics; Mitacs collaborations on healthcare/energy optimization; Industry partnerships developing optimization platforms. Student Advising: Supervises graduate students in metaheuristics applications across healthcare, energy systems, and computational intelligence.

Cedric DawkinsView profile

Associate Professor

• Corporate Social Responsibility
• Labor Rights
• Business Ethics
• +2 more

Cedric Dawkins is an Associate Professor of Management in the School of Administrative Studies at York University. His research critically examines business ethics, stakeholder engagement, workers' rights, corporate social responsibility, and labor governance in global supply chains. Primary research areas include multi-stakeholder initiatives for labor governance, normative business ethics frameworks, and critical analyses of equality assumptions in organizational justice. His work appears in leading journals including Business & Society and Journal of Business Ethics. Recent publications investigate artificial intelligence in people management, deliberative approaches to political CSR, and localized perspectives on corporate responsibility in the Bangladesh garment industry. His scholarship combines ethical theory with empirical analysis of labor rights enforcement mechanisms.

Dar DowlatshahiView profile

Professor

• Intracerebral Hemorrhage (ICH)
• Neuroimaging
• Acute Stroke Therapy
• +3 more

Dr. Dar Dowlatshahi is a Full Professor in the Department of Medicine and Senior Scientist at the Ottawa Hospital Research Institute (OHRI). He holds cross-appointments as Associate Professor in the School of Epidemiology and Public Health at the University of Ottawa and is Vice Chair of Research in the Department of Medicine. His clinical expertise focuses on stroke neurology, with a primary research interest in acute stroke therapy, intracerebral hemorrhage (ICH), and neuroimaging applications in hyper-acute stroke care. Education: MD and PhD from McMaster University (1997–2003), Neurology residency at the University of Ottawa (2008), and Stroke Fellowships at the University of Calgary and Ottawa (2009–2010). Research Interests: 1) ICH pathophysiology and clinical outcomes; 2) Multi-modal neuroimaging (CTA/CTP) for treatment decisions; 3) Clinical trials for novel stroke therapies. He leads studies on hematoma expansion, stroke treatment windows, and ICH biomarkers. Publications: Over 300 peer-reviewed articles, including high-impact studies in Lancet Neurology , Stroke , and PLOS One . Recent work emphasizes synthetic data applications and stroke-cancer comorbidity analysis. Awards: 2014 Department of Medicine Clinician-Scientist Chair Award and Heart & Stroke Foundation New Investigator Award. Affiliations: Ottawa Stroke Program (Scientific Director until 2019), OHRI Neuroscience Program, and member of the University of Ottawa’s Faculty of Graduate Studies.