David Yevick is a Professor at the University of Waterloo, affiliated with the Photonics and Atomic, Molecular, & Optical Physics research group. He leads the Advanced Optical Systems Lab and specializes in integrating machine learning with photonics, nonlinear optics, and quantum systems. His work spans optical communication systems, fiber nonlinearity mitigation, and quantum state analysis. Research interests include applying neural networks and deep learning to optical device performance prediction, material science simulations, and signal processing. Recent trends in his publications focus on combining ML techniques with optical systems, such as using random forests for CNT TFET analysis and variational autoencoders for phase transition modeling. His lab explores cutting-edge topics like self-phase modulation compensation in WDM systems and entropy-regulated data balancing. The Advanced Optical Systems Lab also develops novel photonic crystal designs and fiber compensation algorithms.
Glyn Williams-Jones is a Professor and Co-Director of the Centre for Natural Hazards Research at Simon Fraser University's Department of Earth Sciences. His work bridges physical volcanology, glaciovolcanism, and geothermal systems with Indigenous knowledge integration. Current research explores magma dynamics in the Cascade Volcanic Arc Develops machine learning tools for volcano monitoring Integrates Indigenous oral histories with geological analyses Notable projects include studying Canada's deadliest volcanic eruption (Tseax, ~1700 CE) and developing hazard assessments for glacier-capped volcanoes. His lab employs advanced geophysical techniques like gravity modeling and seismic signal analysis. Students under his supervision work on topics spanning structural geology, glaciovolcanic cave systems, and volcanic risk communication. Recent publications highlight applications of machine learning in volcano-seismic data analysis (2025), Indigenous-Western science co-creation (2024), and glaciovolcanic void dynamics (2024). He actively collaborates with institutions like the Instituto Geofísico de la Escuela Politécnica Nacional in Ecuador.
Dr. Lori Ann Vallis is a Professor at the University of Guelph, specializing in biomechanics, motor control, and neurophysiology. Her research focuses on locomotor control in aging populations, children's motor development, and the interplay between cognitive tasks and movement. She holds a B.Sc. in Human Kinetics from the University of Ottawa, an M.Sc. and Ph.D. in Biomechanics/Kinesiology from the University of Waterloo, and a postdoctoral fellowship at Université Laval. Her research explores how vision, cognitive load, and aging affect locomotion strategies. Notable projects include investigating dual-task interference in obstacle avoidance and the biomechanics of aging adults. She leads the Guelph Family Health Study, focusing on childhood obesity prevention. Recent work examines sleep patterns in toddlers, machine learning applications in health monitoring, and exercise interventions for Parkinson’s disease. Her grants include Ontario Neurotrauma Foundation funding and NSERC Discovery Grants. She advises multiple graduate students and collaborates on interdisciplinary projects. Labs/Teams: Guelph Family Health Study, Biomechanics Research Group.
Igor Shinkar is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on theoretical computer science, discrete mathematics, and probability theory, with particular interests in computational complexity, property testing, and local-to-global phenomena in combinatorial objects. He holds a PhD in Mathematics and Computer Science from the Weizmann Institute of Science (2014), an MSc from the same institution (2009), and a BSc in Mathematics and Computer Science from Tel Aviv University (2005). Shinkar has taught numerous courses at SFU, including CMPT 125 (Introduction to Computing Science and Programming II), CMPT 225 (Data Structures and Programming), CMPT 405/705 (Design and Analysis of Algorithms), and CMPT 706 (Design and Analysis of Algorithms for Big Data). He has also co-taught courses at UC Berkeley and NYU, focusing on topics like coding theory and approximation algorithms. His research explores the interplay between theoretical computer science and mathematics, with contributions to areas such as randomized algorithms, approximation algorithms, and probabilistically checkable proofs. He actively seeks MSc and PhD students interested in theoretical computer science.
Laura Sanità is an Associate Professor in the Department of Computing Sciences at Bocconi University, Milan, Italy. Previously, she held positions at TU Eindhoven (2020–2022) and the University of Waterloo, Canada, where she was an Assistant Professor (2012–2017) and later Associate Professor (2017–2020). She earned a Bachelor’s and Master’s in Management Engineering from Università di Roma Tor Vergata (2003–2005), followed by a PhD in Operations Research from Università Sapienza di Roma (2009). Her postdoctoral work (2009–2011) was at EPFL’s Discrete Optimization Group. Her research focuses on Combinatorial Optimization , Approximation Algorithms , Network Design , and Algorithmic Game Theory . Key contributions include advancements in node connectivity augmentation, graph stabilization, and polytope diameter analysis. She has received prestigious awards such as the NWO-VIDI Award (Netherlands), NSERC Discovery Accelerator Supplements, and the Early Researcher Award (Ontario). Laura co-organizes the Bocconi Theory Day (May 2024) and serves on program committees for conferences like SODA, ESA, and IPCO. She is an Associate Editor for Mathematical Programming , Mathematics of Operations Research , and Operations Research Letters . Current advisees include PhD students Sean Kafer, Dylan Hyatt-Denesik, and Lucy Verbeck. Her work bridges theoretical foundations and practical applications, with notable publications in Mathematical Programming , SIAM Journal on Optimization , and Operations Research . Recent projects explore stabilization of capacitated matching games and iterative randomized rounding techniques for combinatorial problems.
Yong Gao is a Professor of Computer Science, Data Science, and Mathematics at the University of British Columbia (UBC) Okanagan, affiliated with the Irving K. Barber Faculty of Science. He holds a PhD from the University of Alberta and leads research in algorithmic and computational problems in artificial intelligence, network science, and computational biology. His work emphasizes graph theory, probabilistic methods, and applications in social media and biological systems. Educational Background : PhD in Computer Science, University of Alberta Research Interests : Algorithmic foundations of AI and network science Graph-based methods for computational biology and social media analysis Probabilistic modeling of complex systems Awards & Grants : Recipient of multiple NSERC Discovery Grants (2006–2019) UBC Okanagan Startup Grant (2005–2008) Senior Member, Association for the Advancement of AI (AAAI) Professional Roles : Member, Centre for Optimization, Convex Analysis and Nonsmooth Analysis Graduate student supervisor Teaching : Courses in algorithm design, artificial intelligence, discrete mathematics, and network science.
Joel Friedman is a Professor in the Department of Computer Science at the University of British Columbia (UBC), Faculty of Science. His research focuses on Algebraic Graph Theory, Combinatorics, and Theoretical Computer Science, with significant contributions to spectral graph theory, sheaf theory, and computational complexity. He has supervised numerous PhD and Master's students in these areas. Friedman teaches courses such as CPSC 421 (Introduction to the Theory of Computing) and CPSC 531F (Discrete Hodge Theory and Topological Data Analysis). His work bridges pure mathematics and computer science, including studies on Riemann-Roch theorems for graphs, eigenvalue conjectures, and applications of linear algebra in discrete structures. He has authored over 50 publications, with recent contributions on topological data analysis, graph expansions, and algorithmic methods. Awards and honors include the Izaak Walton Killam Memorial Faculty Research Fellowship. Friedman actively engages in graduate supervision, advising students on topics like sparsifier constructions and coded caching problems. His research is affiliated with the Institute of Applied Mathematics at UBC. He has held academic positions since the 1980s, with a consistent record of contributions to theoretical computer science and discrete mathematics.
Margarida Carvalho is an Associate Professor in the Department of Computer Science and Operations Research at Université de Montréal, where she holds the FRQ-IVADO Research Chair in Data Science for Combinatorial Game Theory. She is also an Associate Academic Member at Mila (Quebec AI Institute), contributing to their research in AI for Humanity. Her academic journey spans from Portugal to Canada, where she has established herself as a leading researcher at the intersection of operations research and game theory. Carvalho earned her bachelor's and master's degrees in mathematics from the Faculty of Sciences of the University of Porto (FCUP), followed by a PhD in Computer Science from the same institution in 2016. Her doctoral work, which focused on game theory applications for kidney exchange programs, earned her the prestigious 2018 EURO Doctoral Dissertation Award, making her the first Portuguese woman to receive this honor. After completing her PhD, she worked as an IVADO Postdoctoral Fellow at Polytechnique Montréal before joining Université de Montréal as an Assistant Professor in 2018. Her research focuses on combinatorial optimization and algorithmic game theory, with applications spanning healthcare (kidney exchange programs, hospital operations), sustainable development (electric vehicle infrastructure, urban planning), and education (school choice systems). She develops novel mathematical programming approaches to model and solve problems involving multiple decision-makers with potentially conflicting objectives. Her work bridges theoretical advances in optimization with practical implementations that address real-world challenges in resource allocation and decision-making under uncertainty. Notably, her research on fairness in kidney exchange programs has contributed to more equitable organ allocation policies. Her 15 most recent publications reveal a strong trend toward integrating game-theoretic concepts with practical optimization challenges, particularly in healthcare and sustainable infrastructure. She has pioneered approaches that balance utilitarian objectives with fairness considerations, developed novel formulations for bilevel and multilevel optimization problems, and created learning-based frameworks for complex decision environments. Her work consistently demonstrates how mathematical rigor can inform practical policy decisions in critical domains. 2018 EURO Doctoral Dissertation Award for her PhD thesis on game theory applications for kidney exchange programs Mathematical Programming 2024 Meritorious Service Award Teaching Excellence Award from Université de Montréal Supervised student Maria Bazotte receiving the Dupačová-Prékopa Best Student Paper Prize in Stochastic Programming Carvalho actively advises graduate students, with Marylou Fauchard (Master's) and William St-Arnaud (PhD) among her current advisees. Her research is supported by grants from Hydro-Québec, the Natural Sciences and Engineering Research Council of Canada (Discovery grant 2017-06054 and Collaborative Research and Development Grant CRDPJ 536757–19), and FRQ-IVADO. She serves as an associate editor for INFORMS Journal on Computing, OR Spectrum, and Dynamic Games and Applications, and is a founding board member and treasurer of the Bilevel Optimization Society. She teaches courses in Mathematical Programming, Operational Research Models, and Discrete Mathematics at Université de Montréal. Carvalho is affiliated with Mila (Quebec AI Institute), where she contributes to research initiatives focused on AI for Humanity, particularly in the areas of algorithmic fairness and sustainable development. Her FRQ-IVADO Research Chair supports her work on combinatorial game theory applications, and she collaborates with researchers across disciplines through the IVADO research community. She has been instrumental in establishing the Bilevel Optimization Society, creating a dedicated forum for researchers working on hierarchical decision-making problems.
Marie-Pierre Dubé is an Accredited Professor at the University of Montreal , affiliated with the Department of Nutrition and the Montreal Heart Institute . Her research focuses on genetic epidemiology , pharmacogenomics , and statistical genetics , with a particular emphasis on cardiovascular diseases and drug response variability. She leads studies on statins, anticoagulants, and gene-environment interactions. Recent work explores sex/gender effects on neurocognitive impairment and APOE genetics. Her lab collaborates on meta-analyses of colchicine efficacy and genetic risk scores for myocardial infarction. Publications span clinical trials, AI-driven ECG analysis, and genomic studies of heart failure subtypes. Key themes include: Pharmacogenetics of statins, metoprolol, and colchicine Sex-based medicine in drug response and cardiovascular risk Genetic architecture of cardiomyopathies and arrhythmias Population stratification in epidemiological studies Her work bridges molecular mechanisms and clinical outcomes, with translational focus on precision cardiovascular care. No awards are explicitly listed, but her high-impact publications reflect her scientific contributions.
Karen Gunderson is an Associate Professor in the Department of Mathematics at the University of Manitoba's Faculty of Science. Her research spans graph theory, combinatorics, random graphs, percolation, hypergraphs, and extremal combinatorics. Research Focus : Graph theory, combinatorics, random graphs, percolation, hypergraphs, extremal combinatorics Academic Role : Associate Professor, Acting Associate Head Graduate Contact : Karen.Gunderson@umanitoba.ca , karen.gunderson@umanitoba.ca Her work includes bootstrap percolation , random geometric graphs , and extremal hypergraph problems , with applications in network modeling and probabilistic combinatorics. Recent publications focus on adversarial burning densities, Erdos-Ko-Rado robustness, and Turán numbers in switching contexts. Academic Leadership : Co-organizer of the University of Manitoba Combinatorics Seminar and key organizer for the 2023 CanaDAM conference and Movement & Symmetry in Graphs retreat.
Dr. Robert Fahed serves as Assistant Professor in the Faculty of Medicine at the University of Ottawa, with dual clinical appointments as Staff Interventional Neuroradiologist in the Department of Medical Imaging and Staff Neurologist in the Department of Medicine, Division of Neurology at The Ottawa Hospital. He additionally holds an Associate Scientist position in the Neuroscience Program at the Ottawa Hospital Research Institute since November 2019. His academic credentials include: Doctor in Medicine (Neurology) from Université de Paris VI (P & M Curie), September 2016 Magister Scientiae (Vascular Biology) from Université de Paris VII (Denis Diderot), October 2015 Diploma in Diagnostic and Therapeutic Neuroradiology from Université de Paris VI (P & M Curie), September 2015 Diploma in Expertise in Cerebrovascular Diseases from Université de Paris VII (Denis Diderot), October 2014 Dr. Fahed's research centers on improving stroke intervention accessibility through thrombectomy procedure optimization, algorithm validation for patient eligibility determination, and novel approaches to vascular malformation treatment. His work integrates clinical practice with translational research, including animal studies for endovascular device development and randomized trials comparing arterial versus venous delivery methods for arteriovenous malformations. His scholarly recognition includes: Department of Medicine Research Chair Richard Clinical Research Fellowship With over 130 scholarly contributions (128 publications and 4 books), Dr. Fahed actively advances stroke care through clinical innovation, carotid stenting procedures, and collaborative research initiatives with basic science teams at The Ottawa Hospital.
Rafik Tadros is an Associate Professor in the Department of Medicine and Department of Pharmacology and Physiology at the University of Montreal , Faculty of Medicine. He serves as a Regular Researcher at the Montreal Heart Institute (MHI), focusing on the Electrophysiological Axis . His work bridges Genomics , Physiology , and Cardiology with a translational approach to cardiovascular diseases. Research Interests: Translational Cardiovascular Genetics Genomics and Polygenic Scores for Cardiomyopathies Arrhythmia Mechanisms ECG Analysis Cardiac Arrest Genetic Testing in Clinical Settings Publications highlight trends in: Translational genomics of cardiomyopathies (HCM/DCM) Polygenic risk prediction in cardiovascular diseases Clinical guidelines for genetic testing Comparative outcomes of anesthesia in geriatric surgery Scientific Awards: Young Researcher Award, University of Montreal Department of Medicine Martial Bourassa Prize As Principal Investigator in multiple CIHR Project Grants , he leads the Multicenter Hypertrophic Cardiomyopathy Registry and Biobank , advancing precision medicine. His collaborations span international consortia like the HCMR Investigators.
Naimul Khan is an Associate Professor and Associate Chair in Graduate Studies at Toronto Metropolitan University's Department of Electrical, Computer, and Biomedical Engineering. His research focuses on practical applications of machine learning, medical imaging, computer vision, and augmented/virtual reality technologies to solve real-world healthcare and multimedia challenges. PhD (Toronto Metropolitan University, 2014) MSc (University of Windsor, 2010) BSc (Bangladesh University of Engineering and Technology, 2008) His research interests span medical imaging automation, virtual reality systems for healthcare applications, and machine learning techniques for biomedical signal processing. He has developed innovative algorithms for ultrasound analysis, emotion recognition systems using EEG data, and segmentation approaches for diabetic foot ulcers. Publications demonstrate consistent focus on practical implementations across computer vision, medical diagnostics, and extended reality (XR) technologies. Scientific contributions include a Best Paper Award at the IEEE International Symposium on Multimedia (2017) and prestigious postdoctoral and graduate scholarships. He actively collaborates with industry partners through the TMU Multimedia Research Laboratory, teaching graduate courses like ELE 725 (Basics of Multimedia Systems) and COE 318 (Software Systems). Best Paper Award, IEEE International Symposium on Multimedia (2017) OCE TalentEdge Postdoctoral Fellowship (2014-2016) Ontario Graduate Scholarship (2013-2014) Queen Elizabeth II Scholarship in Science & Technology (2012-2013) As co-director of the TMU Multimedia Research Laboratory, Khan bridges academic research with industry applications through practical implementations of machine learning and extended reality technologies in healthcare domains.
Dr. Claude Gravel is an Assistant Professor in the Department of Computer Science at Toronto Metropolitan University. His academic work bridges theoretical computer science and advanced mathematics, with active teaching responsibilities in both foundational and specialized computer science courses. Research Focus: Dr. Gravel's research spans four interconnected domains: Computer Algebra: Developing symbolic computation methods Mathematical Cryptography: Creating theoretical security frameworks Probabilistic Algorithms: Designing randomized computational solutions Quantum Algorithms: Exploring quantum computing approaches Instructional Activities: He teaches undergraduate and graduate courses including: CPS 420: Discrete Structures CPS 688: Advanced Algorithms Contact: Professional inquiries can be directed to gravel@torontomu.ca . Additional information may be available through his academic webpage .
Marcelo Reggio is a Full Professor in the Department of Mechanical Engineering at Polytechnique Montréal, where he has established a significant research career spanning fluid dynamics and computational methods. He is an active member of the Fluid Dynamics Laboratory (LADYF), focusing on cutting-edge simulations of complex fluid phenomena. His educational background includes: Baccalaureate from Chile Master of Applied Science (M.Sc.A.) from Polytechnique Montréal Ph.D. from Polytechnique Montréal Research Focus: Professor Reggio's work centers on Computational Fluid Dynamics (CFD) with specialization in the Lattice Boltzmann Method (LBM). His research addresses fundamental and applied challenges in: Turbomachinery design optimization Wind turbine aerodynamics and icing effects Rarefied gas flows through porous media Multicomponent and multiphase flow modeling River current dynamics This work bridges theoretical fluid mechanics with industrial applications in energy and manufacturing. Publication Trends: Analysis of his 15 most recent articles (2016-2024) reveals dominant themes: advanced LBM techniques for non-equilibrium gas flows, validation of open-source CFD codes, multiphase interactions, and stochastic modeling of porous materials. His methodological innovations consistently target improved accuracy in complex physical scenarios. Academic Supervision: Professor Reggio maintains an active research group, having supervised: 12 doctoral students 20 master's students Student theses frequently explore LBM applications, turbomachinery, and numerical method development. Laboratory Leadership: At the Fluid Dynamics Laboratory (LADYF), he contributes to developing computational frameworks for fluid simulation, emphasizing GPU acceleration and industrial problem-solving.
