Jasleen is a Lecturer at the Edwards School of Business, part of the University of Saskatchewan. Her work spans interdisciplinary research in machine learning, thermal imaging technologies, and Punjabi language studies. She has contributed to advancements in healthcare technology through studies on lung cancer treatment protocols and facial emotion recognition systems. Her research interests prominently feature applications of artificial intelligence in healthcare diagnostics, development of novel algorithms for material science analysis, and exploration of postcolonial narratives in South Asian literature. Notable work includes innovations in infrared thermography for non-destructive testing and quantum Fourier transform spectroscopy techniques. Jasleen's publications demonstrate a focus on both technical engineering solutions and humanities-based scholarship, reflecting her unique academic positioning at the intersection of business education and cutting-edge scientific inquiry. She has collaborated on projects ranging from spectral domain quantum interferometry to sociological studies of Punjabi immigrant mental health.
Dr. John Pomeroy is a distinguished researcher and academic specializing in hydrology and climate change impacts. He holds the prestigious position of Canada Research Chair in Water Resources and Climate Change and serves as Director of the Centre for Hydrology at the University of Saskatchewan. His research focuses on understanding and predicting hydrological processes in cold regions, with particular expertise in snow physics, water resource management, and climate change impacts on hydrological systems across Canada and globally. Dr. Pomeroy's research interests span multiple critical areas of hydrological science. He has made significant contributions to understanding snow hydrology, climate change impacts on water resources, and the prediction of extreme hydrological events such as floods and droughts. His work integrates field observations, advanced modeling techniques, and innovative measurement technologies to address pressing water resource challenges in cold regions. He has pioneered research on snow physics, ecohydrology, and the complex interactions between land use, climate, and hydrological processes. Dr. Pomeroy's research has direct applications for water resource management, climate adaptation strategies, and improving flood and drought prediction capabilities across diverse landscapes from the Canadian Rockies to the Arctic. The body of Dr. Pomeroy's published work demonstrates consistent focus on cold regions hydrology, with particular emphasis on snow processes, climate change impacts, and water resource prediction. His recent publications show increasing integration of advanced modeling techniques, field measurements, and interdisciplinary approaches. There is a clear trend toward addressing practical water management challenges through scientific research, with growing attention to the societal implications of hydrological changes in a warming climate. His work spans from fundamental snow physics to applied water resource management solutions. Canada Research Chair in Water Resources and Climate Change Global Water Futures Program Leadership International Network for Alpine Research Catchment Hydrology (INARCH) Leadership Extensive research funding for cold regions hydrology studies Dr. Pomeroy has mentored numerous graduate students and early-career researchers, as evidenced by the many co-authored publications with asterisked names (a common notation for students in academic publications). He has received substantial research funding from Canadian federal agencies and international sources to support his work on snow hydrology, climate change impacts, and water resource management. His leadership in the Global Water Futures initiative, Canada's largest university-led research program, demonstrates his commitment to developing practical solutions for water security challenges in a changing climate. As Director of the Centre for Hydrology, Dr. Pomeroy leads a multidisciplinary team conducting cutting-edge research on cold regions hydrology. His work involves extensive field studies in locations including the Canadian Rockies, prairie regions, and Arctic environments. The research group utilizes advanced technologies including remote sensing, unmanned aerial vehicles, and innovative measurement techniques for snow and water monitoring. Dr. Pomeroy collaborates with numerous national and international research teams, contributing to global efforts to understand and address water resource challenges in a changing climate.
Dr. Jyh-Yeuan (Eric) Lee is an Associate Professor in the Department of Biochemistry, Microbiology and Immunology at the University of Ottawa's Faculty of Medicine. He holds a BSc from National Tsing Hua University and a PhD from the University of California, Riverside, followed by postdoctoral training at Texas Tech University Health Sciences Center and the University of Texas Southwestern Medical Center. Education: BSc, National Tsing Hua University PhD, University of California, Riverside Postdoctoral Fellow, Texas Tech University Health Sciences Center Postdoctoral Fellow, University of Texas Southwestern Medical Center Dr. Lee's research focuses on structural biology of membrane proteins, particularly lipid-transport membrane proteins involved in cholesterol and phospholipid homeostasis. His work investigates the structure-function relationships of these proteins using integrative approaches combining X-ray crystallography, cryo-electron microscopy, and computational biology to understand their role in cardiovascular disorders, cancer, and infectious diseases. His recent publications highlight structural advances in P4-ATPase flippases (2024), crystallization protocols for ABCG5/G8 (2023), and mechanistic insights into sterol transporters (2023-2016). The articles span disciplines including biochemistry, structural biology, membrane protein biophysics, and computational modeling, with specific emphasis on cholesterol binding, transmembrane dynamics, and protein-ligand interactions. Dr. Lee's lab employs cutting-edge techniques in protein engineering, synthetic biology, and biophysical analysis to study membrane protein function and its implications in human health.
Marcin Wierzbicki serves as an Assistant Professor in Interdisciplinary Science at McMaster University, with primary appointments in the Department of Medical Physics and Radiation Sciences within the Faculty of Science. His research bridges medical physics and radiation oncology, focusing on optimizing radiotherapy treatments for cancer patients, particularly those with lung cancer. Dr. Wierzbicki plays a key role in major clinical trials including the LUSTRE trial (comparing SBRT versus conventionally hypofractionated radiotherapy) and the OCOG-ALMERA trial (investigating metformin as a radiosensitizer). Dr. Wierzbicki's scholarly work demonstrates a progression from earlier contributions in cardiac modeling and image-guided surgery toward his current specialization in radiation oncology physics. His research addresses critical challenges in treatment planning accuracy, quality assurance protocols, and toxicity management for stereotactic body radiotherapy (SBRT), particularly for challenging tumor locations like ultracentral lung tumors. His publications reveal a strong emphasis on clinical trial methodology, credentialing standards for multi-institutional studies, and the application of advanced computational techniques to improve treatment precision. Analysis of his 15 most recent publications shows consistent focus on optimizing lung cancer radiotherapy, with particular attention to planning target volume margins, dose reconstruction methods, and the relationship between treatment parameters and clinical outcomes. His work on the LUSTRE trial credentialing process has established important standards for SBRT delivery across multiple institutions, while his recent exploration of AI-based dose prediction systems represents cutting-edge integration of machine learning in radiation oncology. Growth differentiation factor 15 (GDF15) as a biomarker for treatment response prediction (2025) LUSTRE Phase 3 trial comparing SBRT versus conventional radiotherapy (2024) Long-term toxicity analysis for ultracentral lung tumors (2023-2024) AI-based dose prediction systems for adaptive radiotherapy (2023) Dr. Wierzbicki contributes significantly to graduate education through courses including 'Anatomy for Medical Physicists' (MEDPHYS 783) and 'Radiation Oncology Physics I' (MEDPHYS 778), demonstrating his commitment to training the next generation of medical physicists. His consistent teaching record since 2018 and active research program position him as an important contributor to translational research in cancer treatment.
Pengfei Li is an Assistant Professor in the School of Information at the Rochester Institute of Technology (RIT), where he leads research at the intersection of machine learning, sustainability, and social equity. Previously, he completed his Ph.D. in Computer Science at the University of California, Riverside under Prof. Shaolei Ren, with additional collaborations at Caltech with Adam Wierman and an internship at Nokia Bell Labs. His educational background includes an M.S.E. in Robotics from Johns Hopkins University and a B.E. in Electrical Engineering from Zhejiang University. Dr. Li's research focuses on three interconnected pillars: developing trustworthy online algorithms with strict robustness guarantees, creating sustainable AI systems that minimize environmental impact, and addressing environmental and social inequities through algorithmic solutions. Analysis of his recent publications reveals a strong emphasis on the environmental consequences of AI systems, particularly water consumption ('Making AI Less 'Thirsty'') and geographical distribution of environmental burdens ('Towards Environmentally Equitable AI'). His work bridges theoretical computer science with practical sustainability challenges, often incorporating learning-augmented approaches to traditional online optimization problems. Scientific Recognition: Dissertation Completion Fellowship Award (DCFA) from the Graduate Program in Computer Science (February 2025) 'Making AI Less 'Thirsty'' in Communications of the ACM has received 15 citations and over 17,000 downloads Organizer of the workshop on learning-augmented algorithms at SIGMETRICS 2025 Dr. Li actively seeks to build a research group focused on societal fairness, reliable generative AI, and decision-focused learning. His work has established important connections between theoretical computer science and critical societal challenges, particularly around AI's environmental footprint and equitable resource distribution. Current research directions include developing algorithmic solutions for environmental and social fairness, with applications in water infrastructure, energy systems, and equitable AI deployment.
Dr. Anne-Julie Tessier is an Assistant Professor at the Université de Montréal (Faculty of Medicine, Department of Nutrition) , a Researcher at the Montreal Heart Institute (ÉPIC Centre) , and a Visiting Scientist at the Harvard T.H. Chan School of Public Health (Department of Nutrition) . As a Registered Dietitian and co-founder of Keenoa (AI-driven dietary assessment platform), she bridges clinical practice with cutting-edge research in nutrition and aging. PhD in Human Nutrition (McGill University, 2021) Postdoctoral Fellowship (Harvard, 2022-2024) NIH-funded co-investigator on the MOSAAIC study Her research explores how nutrition influences healthy longevity , focusing on muscle and brain health through metabolomics, proteomics, and mobile health technologies. She identifies nutritional biomarkers to enable personalized strategies against aging-related diseases via epidemiological studies, intervention trials, and multi-omics integration. Recent work spans plant-based diets (Eur J Clin Nutr, 2025), olive oil and dementia risk (JAMA Netw Open, 2024), and protein metabolite longevity associations (Med, 2024). Collaborations include Harvard , McGill , and Montreal Heart Institute teams. 2022 Governor General's Gold Medal 2022 Canadian Nutrition Society Thesis Award 2022 CIHR Postdoctoral Fellowship 2019 FRQS Doctoral Training Award Currently developing AI-based dietary tools and leading the CAN-THUMBS UP study on multidomain dementia prevention. Her work has been cited in 301+ news outlets, including The Wall Street Journal and BBC Health .
Steven B. Garland is a Part-time Professor at the Faculty of Law, University of Ottawa, teaching patent law with over 30 years of high-stakes intellectual property litigation experience. He represents global clients including Amazon, Dow Chemical, Bayer, and Shell in complex IP matters across Canadian federal and provincial courts. His academic and professional credentials include a B.Eng. in Chemical-Biochemical Engineering from the University of Western Ontario (1985) and an LL.B. from the University of Ottawa (1990). He is dual-qualified as a barrister (Ontario Bar 1992, Alberta Bar 2017) and Professional Engineer (PEO), with registrations as a Patent Agent, Trademark Agent, and practitioner before the United States Patent and Trademark Office. Garland's practice focuses on cutting-edge intellectual property disputes involving chemical, pharmaceutical, biotechnological, and computer/internet technologies. He has secured landmark victories including the largest reported damages award in Canadian patent history ($1B+ for Dow Chemical) and specializes in pharmaceutical patent litigation under the Patented Medicines (Notice of Compliance) Regulations. His professional honors include: Lexology Client Choice Award (2018) for exceptional client service IAM Patents 1000 top-tier recognition as "one of the most versatile trial lawyers in the Canadian IP community" (2019) IAM Patents 1000 acclaim as "a stellar litigator" and "courtroom ace" (2018) Garland maintains active leadership roles as Director of the College of Patent Agents and Trademark Agents (CPATA) Board (elected 2024), Past President of both the Intellectual Property Institute of Canada (IPIC) and AIPPI Canadian Group, and editorial board member of Intellectual Property Magazine. His expertise is sought globally through frequent international lectures and publications on IP strategy.
Diane Wowk is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the Royal Military College of Canada (RMC), where she has served since September 2008. Her expertise spans structural analysis, finite element methods, and nuclear fuel mechanics with a focus on practical aerospace and defense applications. Prior to RMC, she spent five years as an Engineering Analyst in industry. Her research interests emphasize design methodology using finite element analysis , simulation of impact events , fatigue and crack growth , and mechanical behavior of nuclear fuel bundles . She investigates structural integrity through advanced computational modeling, particularly in aerospace components and nuclear systems, with significant work on composite materials and high-rate deformation phenomena. Her scientific contributions include: Canadian Council of Professional Engineers - Manulife Financial Scholarship (2006) National Science and Engineering Research Council (PhD) funding (2004-2006) Canadian Urban Transportation Authority's Award for Technical Advancement (2001) Professor Wowk teaches core mechanical engineering courses including Finite Element Methods (MEE407), Stress Analysis (MEE431), and Advanced Finite Element Methods (ME547). She directs the Numerical Lab Mech Dome, utilizing ANSYS for finite element simulations. Her industry background informs practical research approaches, particularly in predicting residual strength in impact-damaged aerospace panels and nuclear fuel bundle deformation.
Professor Christian Riegel serves as Professor of Health and Medical Humanities and English at the University of Regina, where he co-directs the IMPACT Lab (Interactive Media, Psychology, Art Creation, and Technology). His institutional affiliations extend to the International Institute for Critical Studies in Improvisation as a Team Member on the SSHRC Partnership Grant 'Improvising Futures'. Riegel's research synthesizes Health and Medical Humanities, Disability Studies, and technological innovation, with particular focus on ableism, eye tracking applications, point-of-care ultrasound education, and Canadian literary traditions. His interdisciplinary approach bridges humanities scholarship with clinical practice, creating novel methodologies for examining medical education and disability representation through transdisciplinary collaboration. Current research initiatives demonstrate strong funding support, including SSHRC Insight Grants as Principal Investigator for 'Disrupt/ability: Disability, Ableism, Eye Tracking Technology and Art Creation' and as Co-Investigator for SHRF-funded ultrasound education research. These projects reflect his commitment to translating humanities insights into practical healthcare applications while maintaining active literary scholarship. Recognized as a Fellow of the Royal Society of Arts (FRSA), Riegel's scholarly impact spans decades with significant contributions to Canadian literature studies and emerging leadership in medical humanities. His recent publications (2023-2025) show increasing focus on health technology applications while maintaining connections to literary and cultural analysis. As a supervisor, Riegel offers graduate students unique opportunities through the IMPACT Lab to engage in cutting-edge research at the humanities-technology-health intersection. His lab environment emphasizes collaborative creation, methodological innovation, and real-world application of research in medical education and disability justice contexts.
Dr. Tri Nhu Do is an Assistant Professor in the Department of Electrical Engineering at Polytechnique Montréal, where he conducts cutting-edge research at the intersection of wireless communications and artificial intelligence. His academic journey spans institutions across Vietnam, South Korea, the United States, and Canada, bringing a global perspective to his work. Dr. Do is affiliated with the Advanced Microwave and Space Electronics Research Center (POLY-GRAMES) and contributes to the 'New Frontiers in Information and Communications Technologies' center of excellence. Dr. Do's research focuses on wireless communications systems, artificial intelligence applications in telecommunications, and integrated sensing and communication technologies. His work addresses critical challenges in next-generation wireless networks, particularly in security, resource allocation, and performance optimization. Recent research demonstrates a strong emphasis on applying deep learning, generative AI, and federated learning techniques to solve longstanding problems in wireless communications. His publication record shows remarkable productivity and impact, with numerous articles in top IEEE journals including IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, and IEEE Communications Letters. The research trends indicate a strategic shift toward integrating AI with traditional communication theory, particularly in security applications, reconfigurable intelligent surfaces, and UAV communications. Dr. Do teaches advanced courses in signal detection and estimation, communication theory, and digital transmission, sharing his expertise with the next generation of electrical engineers. His teaching reflects his research interests, providing students with both theoretical foundations and exposure to cutting-edge developments in the field.
Professor Steven Dufour is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. With a career spanning over two decades since completing his Ph.D. in 1999, he has established himself as an expert in numerical modeling, particularly in the areas of finite element methods and multiphase systems. His academic journey began with B.Sc. and M.Sc. degrees from the University of Montreal, followed by a Ph.D. from Polytechnique Montréal. Professor Dufour's research focuses on the numerical modeling of free surface flows in industrial processes, with expertise recognized by NSERC in modeling, simulation, and finite element methods (topic 2107) and polyphase systems (topic 2202). Professor Dufour's research interests span computational fluid dynamics, finite element analysis, and more recently, the integration of machine learning techniques with traditional numerical methods. His work has evolved from foundational research in adaptive finite element methods for multiphase flows to cutting-edge applications combining physics-informed neural networks with computational fluid dynamics, electromagnetic field analysis, and millimeter-wave sensing. His publication record demonstrates consistent research productivity, with 23 publications documented across computational mathematics and engineering applications. The most recent publications from 2024 show his adaptation to emerging methodologies in computational science, particularly the application of physics-informed neural networks to solve complex fluid dynamics problems. NSERC Expertise: Modeling, simulation and finite element methods (2107) NSERC Expertise: Polyphase Systems (2202) Supervised 7 doctoral students to completion Supervised 13 master's students to completion Professor Dufour has maintained active research funding and supervision throughout his career, mentoring students in both theoretical numerical methods and practical engineering applications. His collaborative work spans multiple engineering disciplines, connecting computational mathematics with real-world industrial and biomedical problems.
Serge Prudhomme is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. With a background including a Dipl. Eng. from École Centrale de Lille, an M.Sc. from the University of Virginia, and a Ph.D. from UT Austin, he has established himself as a leading researcher in computational mathematics and mechanics. His primary research interests span scientific computing, finite element methods, error estimation and adaptive methods, multi-scale modeling, verification and validation of numerical calculations, uncertainty quantification, and model reduction. Professor Prudhomme's work demonstrates a consistent focus on developing mathematically rigorous computational frameworks with practical engineering applications. His recent publications show an expanding interest in integrating machine learning techniques with traditional numerical methods. Analysis of his recent publications reveals a strong trend toward interdisciplinary research that bridges computational mathematics with mechanics and increasingly machine learning. His work on peridynamics, proper generalized decomposition, and neural network applications to scientific computing represents cutting-edge developments in computational methods. The publications demonstrate a consistent focus on improving the accuracy, efficiency, and reliability of computational models. Professor Prudhomme has successfully supervised 10 graduate students to completion (4 doctoral theses and 6 master's theses), indicating a strong commitment to student mentorship. His supervision record spans topics including wave equation approximations, goal-oriented calibration, contact detection for ellipsoids, and PGD reduced-order modeling. His research is supported by active collaborations with institutions worldwide, as evidenced by his co-authorship patterns and conference participation across North America and Europe. The breadth of his work, from fundamental mathematical developments to practical engineering applications, positions him as a significant contributor to the field of computational science and engineering.
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.
Felipe Gohring de Magalhaes is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he has been working since July 2018 and assumed his current position in October 2024. He holds a dual doctorate from PUC-RS (Brazil) and Polytechnique Montréal (2016), with degrees in both computer science and computer engineering. His research spans embedded system architectures, real-time systems, avionics, and cybersecurity for emerging technologies. He is affiliated with the Microelectronics and Microsystems Research Group and the Multidisciplinary Institute for Cybersecurity and Cyber Resilience, reflecting his focus on integrated circuits, microelectronics, and system security. Professor Gohring de Magalhaes has published over 40 articles in international journals and conferences, with recent work focusing on photonic integrated circuits security, optical neural networks, and post-quantum cryptography for avionic systems. His publication trend shows consistent output with increasing focus on security aspects of emerging computing technologies. He has supervised at least one PhD student to completion and teaches courses including Introduction to Programming and Operating System Kernel. His research interests align with NSERC topics in integrated circuits, microelectronics, computer systems organization, and VLSI systems.
Dariush Ebrahimi serves as an Assistant Professor in the Department of Physics and Computer Science within the Faculty of Science at Wilfrid Laurier University. His academic trajectory includes prior positions as Assistant Professor at Thompson Rivers University (2021-2022) and Lakehead University (2019-2021), followed by postdoctoral research fellowships at the University of Waterloo (2017-2019) and UQAM (2016-2017). His educational background: PhD in Computer Science, Concordia University, Montreal, Quebec, Canada (2016) Dr. Ebrahimi's research centers on Intelligent Transportation Systems (ITS), with specific investigations into data communications, traffic management, routing algorithms, and electric vehicle charging infrastructure. His broader expertise spans Internet of Things (IoT) architectures, wireless communication protocols, cloud/edge computing frameworks, and optimization techniques for big data applications. Current projects integrate algorithmic design with real-world transportation challenges. He actively seeks graduate students for supervision in his core research domains. While institutional records indicate grant activity through his faculty profile, specific funding details and project names remain undisclosed in publicly available materials.
