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Laurent MydlarskiView profile

Professor

• Fluid Mechanics
• Turbulence
• Heat Transfer
• +4 more

Laurent Mydlarski is a Professor in the Department of Mechanical Engineering at McGill University, affiliated with the Faculty of Engineering. His research focuses on experimental fluid mechanics, particularly turbulent flows and scalar mixing. He holds a Ph.D. from Cornell University and B.A.Sc. from the University of Waterloo. Research interests include turbulence statistics, scalar dispersion, differential diffusion, and industrial cooling applications such as hydroelectric generators and microelectronics. His work combines experimental methods like hot-wire anemometry, laser-induced fluorescence, and particle-tracking velocimetry. Key contributions include studies on multi-scalar mixing in jets, wall shear stress in turbulent flows, and thermal anemometry probe design. His Mydlarski Lab at McGill explores both fundamental fluid dynamics and practical engineering solutions. Recent publications (2023-2025) address multi-scalar mixing metrics, electronic cooling innovations, and drag reduction on porous cylinders. Collaborations with industry focus on applying fluid mechanics principles to real-world thermal management challenges.

• Storage Systems
• Persistent Memory
• Key-Value Stores
• +4 more

Oana Balmau is an Assistant Professor in the School of Computer Science at McGill University, where she leads the Data-Intensive Storage and Computer Systems Laboratory (DISCS Lab). She also holds a status-only appointment at the University of Toronto and serves as a working group chair for MLPerf Storage. Her research focuses on creating storage infrastructure that enables fast and energy-conscious insights from data, with particular emphasis on storage and persistent memory technologies for machine learning, data science, and edge computing workloads. Dr. Balmau's research interests span computer systems, with specific focus on: Design and implementation of efficient key-value stores Storage systems for machine learning workloads Edge computing infrastructure Persistent memory technologies Performance optimization of data-intensive systems Her recent work has led to significant contributions in storage benchmarking through the MLPerf Storage benchmark and in edge computing frameworks. The MLPerf Storage benchmark has become an industry standard for evaluating storage performance in machine learning environments, while her work on hierarchical edge computing addresses security and performance challenges in distributed edge environments. Her publications show consistent high-impact contributions to top systems venues, with recent work focusing on processing-in-memory virtualization, stream processing reconfiguration, and efficient data preprocessing pipelines. Dr. Balmau has received numerous awards for her research, including: SEC 2024 Best Paper Award for "Falcon: Live Reconfiguration for Stateful Stream Processing on the Edge" MLCommons Hero Award 2023 for leadership as MLPerf Storage working group chair ACM SIGOPS Dennis M. Ritchie Doctoral Dissertation Award 2021 Honorable Mention CORE John Makepeace Bennett Award 2021 for the best Computer Science doctoral dissertation in Australia and New Zealand USENIX ATC 2019 Best Paper Award for "SILK: Preventing Latency Spikes in Log-Structured Merge Key-Value Stores" As an educator, Dr. Balmau teaches courses on advanced computer systems, operating systems, and principles of computer systems design at McGill University. She has served on program committees for top systems conferences including SOSP, SIGMOD, FAST, and EuroSys, and has co-organized workshops on resource-efficient machine learning and edge computing. She leads the DISCS Lab, which focuses on two main research directions: Systems for ML (including the MLPerf Storage benchmark) and Edge computing (including frameworks for fast and secure edge computing in hierarchical edge environments).

Wenhu ChenView profile

Assistant Professor

• Natural Language Processing
• Deep Learning
• Multimodal Learning
• +5 more

Wenhu Chen is an Assistant Professor at the University of Waterloo's Computer Science Department and a CIFAR AI Chair at the Vector Institute. He also holds a part-time role as a Senior Research Scientist at Google DeepMind (20% allocation). His research focuses on natural language processing, deep learning, and multimodal reasoning, with contributions to models like MAmmoTH, OpenCoderInterpreter, and VISTA. He received awards including the Canada CIFAR AI Chair (2022) and the UCSB CS Outstanding Dissertation Award (2021). Education: PhD in Computer Science from the University of California, Santa Barbara (under William Wang and Xifeng Yan). Research interests include complex reasoning, controllable GenAI, and multimodal benchmarks like MEGABench and MMMU. Grants include CIFAR AI Chair Funding (2022-2027), NSERC Discovery Fund (2023-2028), and multiple NRC Canada grants. He directs the TIGER Lab, advancing generative models in text, images, videos, and music. Recent talks include presentations on multimodal reasoning at Apple and NeurIPS workshops.

David VocadloView profile

Professor

• Bioorganic Chemistry
• Chemical Biology
• Glycobiology
• +3 more

David Vocadlo is a Distinguished Professor of Chemistry and Molecular Biology & Biochemistry at Simon Fraser University (SFU), holding the Canada Research Chair in Chemical Biology. His research focuses on Chemical Glycobiology, investigating carbohydrate-processing enzymes and developing chemical tools to study glycan roles in health and disease. His lab explores O-GlcNAc signaling, neurodegenerative disorders (e.g., Alzheimer’s, Parkinson’s), and enzyme inhibitors for therapeutic applications. Education: PhD from University of British Columbia (UBC), followed by a CIHR postdoctoral fellowship at UC Berkeley. Key roles include E.W.R. Steacie Memorial Fellow and Royal Society Fellow. Research highlights include O-GlcNAcase inhibitors for neuroprotection, glycan structure-function relationships, and enzyme activity imaging tools. Collaborates globally with experts in glycobiology and employs cutting-edge techniques like chemical synthesis, mass spectrometry, and live-cell imaging. Awards: Distinguished Professor title, Canada Research Chair, Royal Society Fellowship. Active in training researchers through SFU’s graduate programs, emphasizing interdisciplinary approaches. Lab members work on topics ranging from enzyme mechanisms to disease modeling.

Sharan VaswaniView profile

Assistant Professor

• Reinforcement Learning
• Optimization
• Machine Learning
• +3 more

Sharan Vaswani is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on designing algorithms for sequential decision-making under uncertainty, stochastic optimization, and their interplay with machine learning generalization. He holds a PhD from the University of British Columbia (2019) and postdoctoral experiences at the University of Alberta and Mila. His academic journey includes MSc (UBC, 2015) and BTech (BITS Pilani, 2012) degrees. Education: PhD (UBC, 2019), MSc (UBC, 2015), BTech (BITS Pilani, 2012) Postdoctoral Work: University of Alberta (2020-2021), Mila (2019-2020) Teaching includes courses on Probability and Computing (CMPT 210), Optimization for Machine Learning (CMPT 409/981), and Theoretical Foundations of Reinforcement Learning (CMPT 419/983). His research group focuses on developing scalable optimization algorithms with theoretical guarantees. He advises multiple PhD and MSc students, contributing to areas like constrained MDPs, adaptive learning rates, and reinforcement learning theory. Research Highlights: Contributions to bandit algorithms, stochastic gradient methods, and reinforcement learning theory. Notable work includes global convergence analysis of policy gradients and variance-reduced optimization frameworks.

Arash AramiView profile

Associate Professor

• Assistive Robotics
• Human-Robot Interaction
• Machine Learning
• +6 more

Arash Arami is an Associate Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, cross-appointed in Systems Design Engineering. He directs the Neuromechanics and Assistive Robotics Laboratory and maintains affiliations with Waterloo Robohub, the Centre for Bioengineering and Biotechnology, Waterloo AI institute, and KITE institute at Toronto Rehab Institute. He earned his Doctorate in Electrical Engineering from EPFL (2014), Master of Science from University of Tehran (2009), and Bachelor of Science from University of Tabriz (2006), all in Control Engineering. His research in Assistive Robotics and Rehabilitation Engineering integrates Machine Learning with Neuromechanics to develop intelligent systems for human movement analysis. Key focus areas include exoskeleton control algorithms, wearable sensor systems, and neural control modeling for rehabilitation applications. Recent publications demonstrate interdisciplinary work spanning robotics, biomedical engineering, and materials science, with emphasis on real-time human locomotion prediction, exoskeleton-human interaction, and data-driven health monitoring solutions. Dr. Arami serves as Chair of the NSERC Scholarship Committee (2021-2023) and mentors graduate students through the Mechatronics Exchange Study program. His teaching includes core courses in control systems, robot manipulators, and biomechanical engineering. The Neuromechanics and Assistive Robotics Laboratory fosters collaborations with clinical partners at Toronto Rehab Institute, focusing on translating robotic innovations into practical rehabilitation tools through interdisciplinary teamwork.

Hassan RivazView profile

Professor

• Medical image analysis
• Deep learning
• Machine learning
• +5 more

Professor Hassan Rivaz is a Full Professor and Concordia University Research Chair in Medical Imaging with Deep Learning at Concordia University's Gina Cody School of Engineering and Computer Science. He holds appointments in the Department of Electrical and Computer Engineering and is cross-appointed to the Department of Computer Science & Software Engineering. Dr. Rivaz serves as the Founding Director of the IMPACT Lab and actively supervises PhD students in Electrical and Computer Engineering and Computer Science programs. Dr. Rivaz received his PhD from Johns Hopkins University in 2011, Master's degree from the University of British Columbia, and Bachelor's degree from Sharif University, followed by postdoctoral training at McGill University. His academic journey includes prestigious awards such as the NSERC Post-Doctoral Fellowship and Jeanne Timmins Costello Post-Doctoral Award. His research focuses on advancing medical image analysis through deep learning techniques, particularly in ultrasound imaging applications. Dr. Rivaz has made significant contributions to quantitative ultrasound, cancer detection, lymphedema assessment, and ultrasound elastography. His work bridges theoretical algorithm development with practical clinical applications, addressing challenges in medical image denoising, segmentation, registration, and tissue characterization. The IMPACT Lab under his direction develops innovative solutions for medical imaging problems with direct clinical relevance. Analysis of his recent publications reveals a strong emphasis on deep learning applications for ultrasound image processing, with particular focus on denoising techniques, elastography improvements, and segmentation algorithms. His work consistently addresses the challenge of working with real clinical data rather than simulated environments, contributing to more practical medical imaging solutions. Dr. Rivaz has received numerous prestigious awards including: Concordia University Research Chair in Medical Imaging with Deep Learning (2023–2028) QBIN/RBIQ Rising Star in Bio-Imaging in Quebec (2022) Concordia University Research Chair in Medical Image Analysis (2018-2023) Petro-Canada Young Innovator Award (2016–2018) He actively mentors graduate students, with many recipients of competitive scholarships including NSERC CGS, FRQNT, and FRQS awards. Dr. Rivaz serves on editorial boards for top journals including IEEE Transactions on Medical Imaging (since 2017), Medical Image Analysis (since 2025), and IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control (since 2018). He has organized major conferences including IEEE EMBC 2020, ISBI 2021, and IEEE IUS 2023, and served as Area Chair for MICCAI from 2017 to 2024. As Founding Director of the IMPACT Lab, Dr. Rivaz leads a multidisciplinary research team focused on innovative medical imaging solutions. The lab maintains strong collaborations with hospitals and research institutions to translate imaging technologies into clinical practice. Current projects include developing AI-powered ultrasound analysis tools, quantitative imaging biomarkers for cancer diagnosis, and advanced techniques for ultrasound elastography with applications in tissue characterization and disease detection.

Abbas MilaniView profile

Professor

• Textile Composites
• Biocomposites
• Materials Constitutive Relations
• +10 more

Abbas Milani is a tenured Professor of Mechanical Engineering at the University of British Columbia's Okanagan campus, where he holds the Tier 1 Principal's Research Chair in Sustainable & Smart Manufacturing and serves as Director of the Materials and Manufacturing Research Institute (MMRI). He also serves as Technical Director of the Composites Research Network (CRN), Lead of the Canadian-International Biocomposites Research Network, and leads multiple major initiatives including the UBC-Pacific Economic Development Canada-Advancing Circular Economy (ACE) program and the UBC-NRC IRAP National Circular Economy CtO Program. Dr. Milani's primary research focuses on advanced modeling, simulation, and multi-criteria design optimization of composite and biocomposite materials, structures, and manufacturing processes. His expertise spans Textile Composites/Biocomposites, Materials Constitutive Relations, Finite Element Modeling, Robust Inverse Methods, Material Selection for End-of-Life Design Strategies, Multiple Criteria Decision Making, and Industry 5.0 applications. His interdisciplinary research bridges mechanical engineering, sustainable materials science, and smart manufacturing technologies. Analysis of his recent publication record reveals a strong emphasis on sustainable materials development, with particular focus on biocomposites, life cycle assessment methodologies, and optimization of manufacturing processes. His work integrates computational modeling with experimental validation across diverse application areas including medical devices, sustainable packaging, and circular economy strategies. The publications demonstrate increasing integration of artificial intelligence and machine learning approaches with traditional engineering methods. 2015 UBC Okanagan Researcher of the Year Award Killam Faculty Research Award (2016) Inducted into Royal Society of Canada - College of New Scholars (2020) Gold Medal Service Contribution Award by Academics World Reviewer Contribution Award by ASM International Multiple teaching excellence awards from UBC Dr. Milani has successfully mentored over 100 students and postdoctoral fellows who have secured positions in both industry and academia. His research program has been supported by more than $15 million in funding from government and industrial organizations. He leads the NSERC CREATE in Immersive Technologies (CITech) program and co-leads the Advanced Materials and Fabrication Core Competency within the Survive and Thrive Applied Research (STAR) program, demonstrating his commitment to training the next generation of engineers and advancing applied research.

Furkan AlacaView profile

Assistant Professor

• Computer Security
• User Authentication
• Usable Security
• +4 more

Furkan Alaca is an Assistant Professor at Queen's University's School of Computing, part of the Faculty of Arts and Science. His research focuses on user authentication systems, addressing security and usability challenges. He holds a Ph.D. (2018) in Computer Science from Carleton University, an M.A.Sc. (2012) in Electrical and Computer Engineering, and a B.Eng. (2010) in Communications Engineering, all from Carleton University. His academic career includes teaching roles at Queen's University and the University of Toronto Mississauga, where he taught courses such as Cryptography, Cybersecurity, and Discrete Mathematics. He is affiliated with Queen's Security Research Group and Computer Security Research Lab. Research interests include computer and internet security, usable security, authentication mechanisms, and systems security. He has contributed to advancements in web authentication frameworks, malware analysis, and privacy-preserving technologies. His work spans conferences like IEEE and ACM, with notable publications in cybersecurity, machine learning, and network efficiency. Current teaching includes CISC 447 (Introduction to Cybersecurity) and CISC 468 (Cryptography). He has advised on courses ranging from undergraduate programming to graduate-level security topics.

Frances ChenView profile

Professor

• Health Psychology
• Social Psychology
• Neuroendocrinology
• +6 more

Dr. Frances Chen is a Professor and Area Coordinator in the Department of Psychology at the University of British Columbia (UBC), located on the traditional, ancestral, and unceded territory of the Musqueam People. She holds a PhD from Stanford University (2009). Her research integrates health psychology, social psychology, and neuroendocrinology to explore how social experiences influence mental and physical health. Key areas include the physiological impacts of loneliness, social support, and hormonal changes during puberty on adolescent development. Education: PhD, Psychology, Stanford University, 2009 Research Focus: Dr. Chen investigates how social interactions 'get under the skin' through studies on loneliness, stress, conflict negotiation, and hormonal mechanisms. Her work emphasizes interventions to enhance social connection and reduce health disparities. Recent Article Trends: Recent publications highlight interdisciplinary approaches, including genetic influences on depression, effects of near-infrared lighting on cognition, and longitudinal studies on adolescent hormonal contraceptive use. Her work bridges basic science and applied health outcomes. Awards & Grants: Michael Smith Health Research BC C2 Award (2022) Killam Faculty Research Fellowship (2019) Teaching & Learning Enhancement Fund Grant (2025) SSHRC Prosociality Project Funding (2023) Lab & Mentorship: Director of the Social Health Lab, she mentors graduate and undergraduate students, prioritizing equity and inclusion. Recent lab achievements include studies on teen health development and interventions to improve student success in psychology programs. Lab Initiatives: UBC Teen Health and Development Study (longitudinal hormonal/mental health tracking) NIR lighting health impact research (collaborative interdisciplinary project) Prosociality 'in the Wild' SSHRC project

Magdy M. A. SalamaView profile

Professor

• Energy Systems
• Power Quality Analysis
• Smart Grid Analysis
• +5 more

Magdy M. A. Salama is a Professor and University Research Chair at the University of Waterloo's Department of Electrical and Computer Engineering, Faculty of Engineering. He holds a P.Eng. license and is a Fellow of the IEEE. His research spans Energy Systems (Power Quality, Smart Grids, Renewable Energy) and Biomedical Engineering (Medical Imaging, Sleep Analysis). He has authored/co-authored over 460 publications and supervised numerous graduate students. Education: PhD (University of Waterloo), M.Sc. and B.Sc. (Cairo University). Awards include the IEEE Fellow distinction, University Research Chair, and multiple teaching/research awards from the University of Waterloo. Research trends in his articles focus on Smart Grid resiliency, renewable integration, cyber-physical security, and biomedical applications of AI. Notable projects include voltage sag mitigation, EV fleet electrification, and blockchain-based energy trading platforms. Scientific Awards: IEEE Fellow, University Research Chair, Teaching Excellence Award (2000) Grants/Consultation: Extensive industry and institutional collaborations on power systems and biomedical tech. Labs/Teams: Active in High Voltage Lab, Smart Grids Research Group, and Medical Image Processing Lab.

Frank RudziczView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Healthcare
• +12 more

Dr. Frank Rudzicz is an Associate Professor in the Faculty of Computer Science at Dalhousie University. His research lies at the intersection of artificial intelligence, natural language processing, and healthcare, with a focus on developing machine learning systems that improve clinical decision-making, patient outcomes, and accessibility in medicine. He holds a BSc from Concordia University (2004), an MEng from McGill University (2006), and a PhD from the University of Toronto (2011). His research interests include Natural Language Processing, Machine Learning, Healthcare, Speech Technologies, Explainable AI, and Fairness in ML. Dr. Rudzicz's recent publications span a wide range of topics, including Alzheimer's detection through speech analysis, surgical outcome prediction, mental health monitoring, privacy in AI, and the application of large language models in clinical settings. His work consistently emphasizes ethical AI, patient privacy, and real-world clinical integration. He has received several awards, including a Best Paper award at EMNLP 2020, a Best Student Paper award at ICASSP 2021, and the ISCA Best Student Paper award in 2013. His research has been published in top-tier journals such as Nature Scientific Reports , JAMA Network Open , IEEE Access , and Frontiers in Human Neuroscience , as well as leading conferences including NeurIPS, ACL, ICML, and Interspeech. Dr. Rudzicz supervises a dynamic research group working on AI for health, with active projects in voice-based diagnostics, ambient clinical documentation, explainable AI for surgery, and wearable-based monitoring for chronic diseases. He collaborates widely across disciplines, including with clinicians, neuroscientists, ethicists, and public health experts. He is also involved in major initiatives such as the Genetics Navigator study and Bridge2AI-Voice, aiming to build ethically sourced, diverse biomedical datasets. His lab actively explores the societal implications of AI in healthcare, including fairness, trust, and resistance to malicious fine-tuning.

Sarah TurnbullView profile

Associate Professor

• Border criminology
• Immigration detention and deportation
• Punishment and incarceration
• +4 more

Sarah Turnbull is an Associate Professor at the University of Waterloo, affiliated with the Balsillie School for International Affairs. Her work intersects criminology, sociolegal studies, and critical border/migration studies, focusing on immigration detention, deportation, punishment, and social justice through postcolonial, antiracist, and feminist lenses. Education: PhD and MA in Criminology and Socio-Legal Studies from the University of Toronto; BA from Simon Fraser University. Her current research includes: (1) a SSHRC-funded study on the National Immigration Detention Framework in Canada using Foucauldian genealogical methods; (2) the Prison Transparency Project , a comparative analysis of carceral transparency in Canada, Spain, and Argentina; and (3) a transatlantic study on pandemic impacts in prisons. She employs ethnographic and qualitative methods in her research. Selected Grants: 2023–2030: Co-Investigator, Prison Transparency Project (SSHRC Partnership Grant). 2022–2025: Principal Investigator, Reforming Detention (SSHRC Insight Grant). 2022–2023: Co-Principal Investigator, COVID-19 Justice as Penal Justice (Strathclyde-Waterloo Transatlantic Award). Turnbull’s publications and collaborations emphasize systemic inequalities, carceral mobilities, and ethical research practices. She co-founded the Prison Transparency Project and has contributed to dialogues on art as resistance in detention settings.

Xianguo LiView profile

Professor

• Fuel Cells
• Energy Systems
• Thermal Fluid Science
• +7 more

Xianguo Li is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, Canada. He holds prestigious fellowships including Fellow of the Canadian Academy of Engineering (FCAE), Fellow of the Engineering Institute of Canada (FEIC), and Fellow of the Canadian Society for Mechanical Engineering (CSME). His primary research focuses on thermal fluid science, energy systems, and fuel cell technology, with a strong emphasis on green energy solutions. **Education**: 1989: Doctorate in Mechanical Engineering, Northwestern University, USA 1986: Master's in Mechanical Engineering, Northwestern University, USA 1982: Bachelor's in Thermal Energy Engineering, Tianjin University, China **Research Interests**: His work spans fuel cells, spray dynamics, fluid dynamics, heat and mass transfer, power generation, and renewable energy systems. He leads the Fuel Cell and Green Energy Lab, advancing innovations in energy storage, propulsion systems, and sustainable technologies. **Awards**: Outstanding Performance Award (University of Waterloo, 2007) Frank Walk Service Award (2001) Best Paper Award (2003) Recipient of the Simpson Fellowship (1988) **Advising & Grants**: He supervises graduate students and research associates in projects funded by NSERC, Auto 21, CFI, and industry partners. His lab collaborates on fuel cell durability, thermal management, and green energy policy initiatives. **Editorial Roles**: Founding Editor-in-Chief of the International Journal of Green Energy , Field Chief Editor of Frontiers in Thermal Engineering , and serves on dozens of editorial boards. He chairs major conferences like the International Green Energy Conference and the World Fuel Cell Conference series.

Qingguo LiView profile

Professor

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +4 more

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .