Dr. Thalia Field is an Associate Professor in the Division of Neurology at the University of British Columbia and holds the Sauder Family/Heart and Stroke Foundation Professorship of Stroke Research. She is a clinician-researcher specializing in stroke neurology, with a focus on clinical trials and stroke in younger adults, particularly cerebral venous thrombosis (CVT) and congenital heart disease. Her research includes leading the SECRET trial on CVT treatment strategies and a national longitudinal study on brain health in congenital heart disease patients. She has been principal investigator on multiple high-impact studies, including the ESCAPE-NA1 trial evaluating NA-1 in stroke and the TEMPO-2 trial on tenecteplase for minor ischemic stroke. Her research interests span anticoagulation protocols, stroke prevention, neuroimaging advancements, and healthcare equity in stroke care. Awards include the 2025 Innovation and Translational Research Award and the 2020 Excellence in Patient-Oriented Research Award. Dr. Field actively contributes to clinical guidelines, including the Canadian Stroke Best Practice Recommendations and American Heart Association scientific statements on cerebral venous thrombosis and cervical artery dissection. Key ongoing projects include the ACTION-CVT study on direct oral anticoagulants and the STOP-CAD study on antithrombotic therapies for cervical artery dissection. Her work emphasizes translational research bridging clinical practice and population health, with a focus on improving outcomes for underserved stroke populations.
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.
Blair Welsh is an Assistant Professor in the Department of Political Science at the University of Western Ontario within the Faculty of Social Science, affiliated with the Africa Institute and Centre for Transitional Justice and Post-Conflict Reconstruction. His research examines armed violence, displacement, and post-conflict recovery with a regional focus on sub-Saharan Africa. His educational background includes a PhD in Government from the University of Essex (2023). Professor Welsh's work centers on three interconnected themes: causes/consequences of armed violence, conflict-induced displacement, and post-conflict recovery processes. His field research spans Ghana, Nigeria, Somalia, and South Sudan, employing survey experiments, spatial econometrics, and machine learning to analyze militant behavior, hostage dynamics, and gendered violence patterns. Methodological innovation remains central to his approach in understanding conflict escalation and resolution. Recent publications reveal a trajectory toward granular analysis of terrorist tactics and spatial violence patterns, with increasing emphasis on experimental evidence from conflict zones. His work bridges theoretical debates in political violence with actionable insights for policymakers addressing displacement and recovery in fragile states. His contributions have been recognized through prestigious awards: Stuart A. Bremer Award (Network of European Peace Scientists, 2022 and 2023) Service Recognition Award (International Studies Association, 2022) Eric Tanenbaum Prize (University of Essex, 2020) Fellowships from Gothenburg, George Mason, Mitacs, and Montréal institutions Research funding includes major grants from J-PAL, IPA, ESRC, and IKEA Foundation supporting field collaborations with UNDP, IOM, and Somali ministries on displaced livelihoods and post-conflict governance, reflecting strong policy engagement in active conflict zones. Through institutional affiliations and UN partnerships, Welsh maintains active research teams in Somalia and South Sudan focused on experimental evaluations of reintegration programs and legal pluralism in post-conflict settings.
Dr. William Leslie is a Professor of Medicine and Radiology at the University of Manitoba , affiliated with the Max Rady College of Medicine . He serves as Founding Director of the Manitoba Bone Mineral Density (BMD) Program and holds cross-appointments in Radiology (Nuclear Medicine) and the Manitoba Centre for Health Policy . His research spans osteoporosis , fracture risk assessment , and artificial intelligence in diagnostics . Doctor of Medicine (University of Manitoba) MSc in Computer Science (University of Manitoba) BSc (Double Honours in Mathematics and Computer Science, University of Manitoba) Residencies in General Internal Medicine and Nuclear Medicine Research Interests Dr. Leslie's work focuses on bone densitometry , nuclear diagnostic techniques , and machine learning applications for fracture risk prediction. He has pioneered the integration of administrative health data with clinical outcomes in the Manitoba BMD program. Scientific Awards International Society for Clinical Densitometry Researcher of the Year (2020) John P. Bilezikian ISCD Global Leadership Award (2017) ASBMR Most Outstanding Clinical Abstract Award (2011) Lindy Fraser Memorial Award (2008)
Moulay Akhloufi is an Associate Professor affiliated with the Department of Electrical Engineering and Computer Science at Laval University, within the College of Engineering. He also teaches courses at École Polytechnique de Montréal, indicating a collaborative academic presence in Quebec's engineering education landscape. His educational background includes advanced degrees in both engineering and management: Ph.D. in Electrical Engineering, Laval University, Canada (2013) MBA, Laval University, Canada (2006) MScA in Electrical Engineering, École Polytechnique de Montréal, Canada (1999) Dr. Akhloufi's research is centered on computer vision and intelligent systems, with a strong emphasis on biometric identification, multispectral face recognition, and real-time processing using GPGPU. His work extends into 3D vision, stereo imaging, augmented reality, and optical character recognition, reflecting a multidisciplinary approach to visual data analysis and machine intelligence. These interests align closely with applications in surveillance, robotics, and industrial automation. His teaching portfolio includes key courses such as Calcul matriciel at Laval University and Traitement et analyse d’images and Intelligence artificielle at École Polytechnique de Montréal, demonstrating expertise across foundational mathematics, image processing, and AI. While no specific scientific awards are listed, his active participation in major professional societies underscores his engagement with the global research community: IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence (PAMI) IEEE Biometrics Council Committee for IEEE P1876™ Standard on Networked Smart Learning Objects Society of Photo-Optical Instrumentation Engineers (SPIE) Society of Automotive Engineers (SAE) He has directed research and development at the Centre de Robotique et de Vision Industrielles since 2008 and has supervised research projects, indicating mentorship and leadership in applied research. His career trajectory combines academic rigor with industrial application, having previously served as a Machine Vision Specialist at Matrox and Project Manager in industrial robotics. He is actively engaged in collaborative research and continues to contribute to the advancement of computer vision systems. Dr. Akhloufi is associated with the Computer Vision and Systems Laboratory, where he contributes to ongoing research initiatives involving smart vision systems, GPU-accelerated processing, and intelligent automation solutions.
Audrey Durand is an Assistant Professor at Université Laval, affiliated with the Department of Electrical Engineering and Computer Engineering within the College of Engineering. She is actively engaged in research in computer vision and machine learning, and maintains a personal academic website for her scholarly work. Education: Ph.D. in Electrical Engineering, Université Laval, 2018 M.Sc. in Electrical Engineering, Université Laval, 2011 B.Eng. in Computer Engineering, Université Laval, 2009 Her research focuses on computer vision and systems, with strong ties to artificial intelligence and reinforcement learning. Her work is associated with the Computer Vision and Systems Laboratory, indicating a focus on both theoretical and applied aspects of intelligent systems. The available publications suggest a consistent output in AI and engineering, particularly in vision and learning systems, with a likely emphasis on algorithmic development and system integration. Scientific Awards: No awards listed in the provided text. She has advised students and secured research grants, though specific names and details are not provided in the current text. She is involved in academic advising and ongoing research projects. She is affiliated with the Computer Vision and Systems Laboratory, which appears to be a key research group supporting her work in intelligent systems and computer vision.
Alexei Efros is a Professor of Electrical Engineering and Computer Science at UC Berkeley , affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) . Previously, he was a faculty member at the Robotics Institute, Carnegie Mellon University , and a postdoc at Oxford University with Andrew Zisserman. His work spans data-driven computer vision , self-supervised learning , and applications to computer graphics , computational photography , and human-AI interaction . Research Themes : Self-supervised visual learning 3D scene understanding Vision-language multimodal systems Teaching : CS 180/280A: Intro to Computer Vision CS 280: Graduate Computer Vision CS 294-192: Visual Scene Understanding Recent Publication Trends : Focus on diffusion models and self-guidance 3D perception and rendering Interpretability of vision-language models Temporal and sequential learning Scientific Collaborations : Extensive partnerships with institutions like MIT, CMU, Stanford, and NVIDIA Mentorship of PhD students now at TTIC, OpenAI, Anthropic, and academia Labs & Teams : BAIR Lab (UC Berkeley) Collaborations with Adobe Research, Google, and NVIDIA
Sven Dickinson is a Professor in the Department of Computer Science at the University of Toronto, where he has held roles such as Chair (2010-2015) and Acting Chair (2008-2009). He also served as Vice President and inaugural Head of the Samsung Artificial Intelligence Research Center in Toronto (2018-2024). His academic journey includes positions at Rutgers University and affiliations with MIT, University of Maryland, and others. Education: B.A.Sc. in Systems Design Engineering, University of Waterloo (1983) M.S. and Ph.D. in Computer Science, University of Maryland (1988, 1991) Research Interests: Focuses on object recognition, shape perception, and the integration of human and computer vision. Key areas include generic object recognition, shape abstraction, symmetry detection, and multiscale part-based representations. His work bridges low-level image features and high-level shape models, emphasizing mid-level shape priors and perceptual grouping. Awards and Honors: NSF CAREER Award (1996) Ontario Premiere's Research Excellence Award (2002) Lifetime Research Achievement Award from CIPPRS (2012) Fellow of IEEE and IAPR Contributions: Co-edited influential volumes like Object Categorization: Computer and Human Vision Perspectives (2009) and Shape Perception in Human and Computer Vision (2013). Served as Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence (2017–2021), and on multiple editorial boards. Active in organizing workshops and conferences, including CVPR 2014 and WACV 2019. Labs and Collaborations: Involved with the Vector Institute for Artificial Intelligence, and has collaborated on projects in artificial intelligence, robotics, and vision-based applications for accessibility and navigation.
Dr. Vincent Taschereau-Dumouchel is an Associate Professor in the Department of Psychiatry and Addictology at the Faculty of Medicine, University of Montreal. His research focuses on integrating real-time neuroimaging with machine learning to develop novel interventions in mental health, particularly targeting anxiety and fear-related disorders. He pioneered the use of decoded neurofeedback (DecNef), a method combining machine learning with fMRI to enable voluntary control over specific brain activity patterns. His work explores the neural basis of emotional experiences, aiming to create personalized mental health treatments through closed-loop systems. Key affiliations include the Centre de recherche de l'Institut universitaire en santé mentale de Montréal (CR-IUSMM) and the UNIQUE initiative, a Quebec-based neuroscience-AI collaboration. Taschereau-Dumouchel leads grants from agencies like FRQNT, CRSNG, and FRSQ, focusing on AI-driven brain activity modulation and mental health innovation. Research highlights include studies on fear dissociation mechanisms, anhedonia in anxiety, and the development of neurofeedback tools for PTSD and specific phobias. He actively collaborates with institutions like CIUSSS de l'Est-de-l'Île-de-Montréal and has supervised students such as Shawn Manuel in applying AI to predict psychiatric symptoms through individual differences analysis. His lab's innovations include a real-time fMRI platform (CLIP), generative AI for neuroimaging feedback, and longitudinal biopsychosocial studies of psychiatric emergencies (Signature Biobank). Taschereau-Dumouchel maintains an active presence in academic networks, bridging clinical psychiatry with cutting-edge neurotechnology.
Mina Hoorfar is the Dean of Engineering and Computer Science and a Professor in the Mechanical Engineering Department at the University of Victoria (UVic). She holds a BASc from the University of Tehran, MASc and PhD from the University of Toronto (U of T), and is a Professional Engineer (P.Eng). Her research focuses on advancing surface science and microfluidic technologies, with specialization in nanostructured sensors for gas monitoring, hydrogen detection in natural gas, wearable health diagnostics, and micro/nano-encapsulation for pharmaceutical applications. Her research areas include: Nanostructured microfluidic artificial olfaction for real-time gas monitoring Hydrogen monitoring in hydrogen-blended natural gas Breath analysis for health and safety applications Wearable microfluidic sensing for personal health Micro/nano-encapsulation in food and pharmaceuticals Current team members: 13 researchers. Lab spaces: Two specialized labs focusing on Advanced Sensors and Advanced Drug Delivery. The MiNa Lab collaborates with industry partners to develop innovative solutions in sensing technologies and biomedical engineering. Advising and grants: While specific grants are not listed, her work reflects significant industry and academic collaboration. The lab emphasizes high-throughput microfluidic synthesis, 3D bioprinting for neural models, and sensor integration with machine learning. Labs/Teams: The MiNa Lab at UVic is a leader in microfluidic sensor development, with dual lab spaces dedicated to advancing sensor technologies and drug delivery systems through cutting-edge research and industry partnerships.
Aaron Tabor is an Assistant Professor at the University of New Brunswick . His research focuses on bridging Human-Computer Interaction (HCI) with Biomedical Engineering and Health Informatics to develop innovative rehabilitation technologies and wellness-oriented systems. Email: q4k8p@unb.ca Office: Room GE109A His work emphasizes inbodied interaction design , which integrates internal physiological processes into technology development. Key areas include breathing exercise systems for ADHD and chronic disease management, EMG feedback for spinal cord injury rehabilitation, and gamification in therapeutic interventions. Research Trends : The provided articles highlight his focus on three domains: (1) gait analysis using underfoot pressure sensors and deep learning, (2) respiratory therapy through biofeedback and idle games, and (3) inbodied interaction frameworks that leverage neuro-physiological pathways for self-tuning systems. These works often intersect machine learning , gamification , and non-invasive physiological monitoring .
Robert Gens is a researcher at the University of Washington , affiliated with the College of Engineering and the Department of Computer Science and Engineering . His work focuses on advancing machine learning architectures, particularly Sum-Product Networks (SPNs) , with applications in computer vision and deep learning. Education: S.B. in Electrical Engineering and Computer Science from MIT (2009) , Ph.D. in Computer Science and Engineering from the University of Washington ( 2016 ). His research integrates insights from neuroscience, graphics, and mathematics to develop algorithms capable of modeling infinite visual data as stable concepts. Key contributions include structural learning, discriminative training, and computational efficiency in SPNs. Notable publications span NIPS , ICML , and ICLR venues, with a focus on SPN optimization and compositional modeling. Trends in his work emphasize tractable probabilistic models , neural network efficiency , and cross-disciplinary algorithm design . Awards include the Google PhD Fellowship in Deep Learning and an NIPS 2012 Outstanding Student Paper Award . Current research involves Deep Symmetry Networks at the Seattle Laboratory of Robotics.
Tian Hong is an Associate Professor in the Department of Biological Sciences at The University of Texas at Dallas and holds a joint Research Associate Professor position in the Department of Biochemistry & Cellular and Molecular Biology at The University of Tennessee, Knoxville. His research focuses on computational biology, systems biology, and mathematical modeling of gene regulatory networks, particularly in the context of epithelial-mesenchymal transition (EMT), immune cell differentiation, and cancer progression. Hong’s research has produced significant work on EMT dynamics, RNA degradation mechanisms, and pattern formation in developmental biology. His recent publications explore Turing patterns, noncoding RNA oscillations, and multi-step cellular transitions, often employing single-cell transcriptomics and nonlinear dynamics. He has received recognitions such as Editor’s choice Cover story for his publications. His lab mentors graduate and postdoctoral researchers, including Daniel Lopez, Haimei Wen, and Shibashis Paul, while former members like Andrew Willems and Ben Nordick have transitioned to postdoctoral and industry roles.
Dr. Yunyan Zhang is a Research Associate Professor at the University of Calgary's Cumming School of Medicine with dual appointments in the Department of Radiology and Department of Clinical Neurosciences. She holds additional affiliations as Full Member of the Hotchkiss Brain Institute and Child Health & Wellness Researcher at Alberta Children's Hospital Research Institute. Dr. Zhang obtained her MD in radiology in China and PhD in Imaging Informatics from the University of Calgary, completing postdoctoral training through research and clinical fellowships in neurology and neuroradiology at the Universities of British Columbia and Calgary. Her research program develops novel AI methodologies for advancing healthcare, with specialization in: Machine/deep learning for medical image pattern recognition Natural language processing for clinical data analysis Neuroimaging biomarker discovery for multiple sclerosis Histopathological validation of computational models Tissue integrity and repair assessment techniques The core objective is creating personalized diagnostic and treatment evaluation frameworks through computational innovation. Dr. Zhang has secured significant research funding including: Discovery Grant (NSERC) Project Grant (CIHR) Discovery Research Grant (MS Society of Canada) AICE Concepts Program (Alberta Innovates) She actively mentors students in AI/imaging research and contributes to institutional brain health and child wellness initiatives.
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.
