Jens Kreitewolf is a Faculty Lecturer in the Departments of Psychology and Mathematics and Statistics at McGill University. He teaches courses in statistics, research methodology, and psychophysics. His research focuses on auditory cognition, speech comprehension, and the neural mechanisms underlying voice perception. Dr. Kreitewolf holds a Ph.D. (Dr. rer. nat.) from Humboldt University of Berlin and completed postdoctoral fellowships at BRAMS and the University of Lübeck. His work combines experimental psychology, neuroimaging, and psychophysics to explore auditory processing challenges in adverse listening conditions. Key interests include how familiarity with a talker’s voice aids comprehension and the impact of hearing impairment on speech perception. Education: M.Sc. in Psychology (Ruhr University Bochum, 2009); Ph.D. in Psychology (Humboldt University of Berlin, 2014). Research Interests: Auditory scene analysis and speech-in-noise processing Voice recognition and familiarity effects Neural correlates of perceptual decision-making Circadian rhythms and perceptual sensitivity Cognitive neuroscience of auditory attention Publications highlight contributions to understanding: Risk factors for depression symptom progression Self-concept clarity in romantic evaluations Neurobiological mechanisms of working memory vulnerability Vestibular symptoms in migraine patients His interdisciplinary approach bridges psychology, statistics, and neuroscience, with applications to clinical populations and sensory processing disorders.
Geoff Pleiss is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC), affiliated with CAIDA's AIM-SI cluster. He is also a Canada CIFAR AI Chair and faculty member at the Vector Institute. His research bridges deep learning and probabilistic modeling, focusing on uncertainty quantification, Bayesian optimization, Gaussian processes, and ensemble methods. Pleiss earned his PhD in Computer Science from Cornell University (2020), followed by a postdoc at Columbia University. He holds multiple awards, including the AISTATS Top Reviewer and NeurIPS recognitions. His work emphasizes scalable algorithms and open-source contributions, such as the GPyTorch library. Pleiss advises students in Computer Science and Statistics, including Donney Fan (PhD), Tim G. Zhou (MSc), and others. He teaches advanced courses like STAT 547U (Deep Learning Theory) and STAT 520P (Bayesian Optimization). Grants include NSERC Discovery and New Frontiers in Research funding. Pleiss collaborates on interdisciplinary projects, such as astrophysical discovery via machine learning, and actively participates in academic service and outreach. Education: PhD in Computer Science, Cornell University (2020) MSc in Computer Science, Cornell University (2018) BSc in Engineering (Computing with Applied Mathematics), Olin College (2013) Key Research Themes: Uncertainty-aware decision-making with neural networks Scalable Gaussian processes and Bayesian optimization Ensemble methods and their theoretical limitations Recent Grants: NSERC Discovery Grant (2024) New Frontiers in Research Fund (2025, co-PI) His publications span foundational theory to applied machine learning, with over 14,500 citations. He actively mentors students through research internships and advises on open-source software development. Pleiss frequently presents at top conferences and collaborates with industry partners like Microsoft and ASAPP.
Ahmed Hammad is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Alberta's Faculty of Engineering, where he also serves as Director of Academic Integrity, ENG WIL, Co-op and Career Connections. With 25 years of industry experience as a Project Planning & Control Manager on global mega-projects (including Oil Sands, LNG, and Infrastructure across Canada, UAE, Australia, and Egypt), he brings extensive practical expertise to academia. Education: Doctorate of Philosophy, Construction Engineering & Management, University of Alberta (2009) Master of Science, Construction Engineering & Management, University of Alberta (1999) Master of Engineering, Construction Engineering and Management, Cairo University (1996) Bachelor of Science, Civil Engineering, Mansoura University (1989) Research Focus: Dr. Hammad's work centers on applying smart tools to achieve sustainable construction through maximizing efficiency and minimizing waste . His research employs Machine Learning , Multi-Criteria Decision Making , Knowledge-Based Decision Support Systems , and digital twin technologies to optimize project planning, resource allocation, and sustainable material selection. He investigates the integration of BIM and Augmented Reality to enhance construction processes while reducing environmental impact. Publication Trends: Recent publications (2023-2025) emphasize sustainable construction methodologies, with 60% focusing on GHG reduction, resource optimization, and decision support systems. Key themes include machine learning for labor estimation, TOPSIS/MCDM for sustainable material selection, and digital twins for production planning, reflecting his NSERC-funded projects on KBDSS and construction-oriented digital twins. Scientific Recognition: Best Paper Award at 8th International Conference on Industrial Engineering and Operations Management (2018) Best Paper Award at HBRC Green Smart Sustainable Buildings Conference (2024) Research Leadership: Dr. Hammad secures major industry-academic partnerships, including NSERC Mission Alliance Grants ($1.2M+) with 16 industry partners for GHG reduction projects and NSERC Alliance Grants with 9 partners for digital twin development. His completed projects include collaborations with the City of Edmonton and Alberta Ministry of Infrastructure on resource allocation models. Research Ecosystem: As leader of the Sustainable Construction Research Group (SCRG), he fosters industry-academia collaboration through regular workshops with construction firms and government agencies, focusing on translating research into practical tools for sustainable project delivery.
Guillaume Lajoie is an Associate Professor in the Department of Mathematics and Statistics at Université de Montréal and a Core Academic Member of Mila – Quebec Artificial Intelligence Institute. He holds a Canada CIFAR AI Research Chair and a Canada Research Chair in Neural Computation and Interfacing. His research focuses on the intersection of AI and neuroscience, particularly in understanding neural network dynamics and developing brain-machine interfaces for clinical and scientific applications. He is affiliated with the Centre de recherches mathématiques (CRM), the Interdisciplinary Center for Research on the Brain and Learning (CIRCA), and the UNIQUE initiative. Education: PhD in Applied Mathematics from the University of Washington (Seattle), postdoctoral fellowships at the Max Planck Institute for Dynamics and the University of Washington Institute for Neuroengineering. Awards include the FRQS Scholar designation and leadership roles in strategic research initiatives like UNIQUE and CIRCA. Research interests include neural computations, recurrent neural networks, neurotechnology, and responsible AI development. Supervised students include François Paugam (PhD), Giancarlo Kerg (PhD), and others. Key grants include projects on adaptive neuroprosthetics, neural decoding, and Canada Research Chairs funding.
Olga Veksler is a Professor at the University of Waterloo's Department of Computer Science, part of the Faculty of Mathematics. She holds a Ph.D. and M.Sc. from Cornell University (1999) and a B.A. from New York University (1995). Her research focuses on computer vision, machine learning, and discrete optimization, with notable contributions to image segmentation, graph algorithms, and deep learning integration. Her work emphasizes semantic segmentation, salient object detection, and efficient optimization techniques for graphical models. Education: Ph.D. in Computer Science, Cornell University, 1999 M.Sc. in Computer Science, Cornell University, 1999 B.A. in Computer Science, New York University, 1995 Her research explores intersections between machine learning and traditional computer vision challenges, particularly leveraging graph-based optimization and CRF models. Recent trends in her work include weakly supervised learning, sparse non-local CRF applications, and test-time adaptation strategies for salient object detection. She has pioneered methods for shape priors in multi-object segmentation and efficient graph-cut algorithms. Her advising and grant activities are foundational to her research, though specific grant details are not listed here. She maintains a lab focused on advancing computer vision through algorithmic innovation, with contributions to both theoretical frameworks and practical applications in medical imaging and scene understanding.
Brett Gilley is an Associate Professor of Teaching in the Department of Earth, Ocean and Atmospheric Sciences at The University of British Columbia (UBC), Vancouver Campus. His primary affiliation is within the Faculty of Science. He specializes in geoscience education with a focus on inclusive fieldwork practices, collaborative learning strategies, and accessibility in STEM education. He is currently not accepting students for advising. His research interests span geology and geoscience education, with a strong emphasis on making field experiences accessible for students with disabilities, developing innovative teaching methodologies, and fostering inclusive learning environments. Gilley has contributed to projects like the Earth Science Experiential and Indigenous Learning (EASEIL) initiative, which integrates Indigenous knowledge and community partnerships into curriculum design. Notable trends in his publications include advancing universal design in geoscience field courses, evaluating collaborative testing strategies, and exploring the impact of technology (e.g., ChatGPT) on student research practices. His work often bridges pedagogical innovation with practical implementation in large undergraduate science programs. While no formal awards are listed, his contributions to inclusive education and curriculum development reflect a commitment to equitable STEM education. His professional activities include mentoring faculty through UBC’s evidence-based pedagogy graduate course and participating in interdisciplinary curriculum development teams.
Michael O. Wood is an Associate Professor in the School of Environment, Enterprise and Development (SEED) at the University of Waterloo and currently serves as the Associate Dean of Work-Integrated Learning in the Faculty of Environment. He holds a Ph.D. in Strategy and Sustainability from the Ivey Business School at Western University, an M.A. in Environmental Studies in Sustainability Management from Western University, and a B.A. in Science from the University of Guelph. Ph.D. in Strategy and Sustainability, Ivey Business School, Western University M.A. in Environmental Studies in Sustainability Management, Western University B.A. in Science, University of Guelph Dr. Wood's research focuses on organizational responses to sustainability challenges through the lenses of space, time, scale, and social license to operate. His work spans industries like insurance, mining, carbon management, waste management, and global security, with a particular emphasis on the Blue Economy and climate change. He has contributed to frameworks for water risk assessment, sustainable packaging practices, and the role of governance in sustainability-financial performance linkages. His recent publications address transdisciplinary tools for water risk management, workforce practices' impact on sustainability, ETS policy effects, and disaster response patterns in multinational corporations. While no scientific awards are explicitly mentioned, his work has been published in journals such as the International Journal of Disaster Risk Reduction and Economic Systems Research . Dr. Wood teaches courses like Introduction to Environment and Business and Environment and Business Project , and he collaborates with researchers like Sandhu, Weber, and Bansal on sustainability-related projects. His studies often incorporate mixed-methods analysis and policy reviews for sustainable resource management in Ontario and Canada.
Gautam Kamath is an Assistant Professor at the University of Waterloo's Cheriton School of Computer Science, a Faculty Member at the Vector Institute, and a Canada CIFAR AI Chair. He leads The Salon, a research group focused on statistics, algorithms, machine learning, and optimization. His work bridges theoretical foundations with practical applications in data privacy and robustness, contributing to both academic research and real-world deployments that impact millions of users. Dr. Kamath earned his PhD and SM degrees in Electrical Engineering and Computer Science at MIT, where he was advised by Costis Daskalakis. Prior to MIT, he graduated from Cornell University in May 2012 with a degree in Computer Science and Electrical and Computer Engineering, where he worked with Bobby Kleinberg. His academic journey reflects a strong foundation in both theoretical computer science and practical applications. His research focuses on developing solutions for trustworthy and reliable machine learning and statistics, with particular emphasis on data privacy and robustness. He addresses fundamental problems in these areas, revitalizing statistical toolkits for the modern data era where privacy preservation is paramount. His work spans theoretical foundations of differential privacy to practical applications that have been deployed at scale, including contributions to systems that protect the sensitive information of hundreds of millions of individuals. Analysis of his recent publications reveals a strong trend toward addressing the interplay between privacy, robustness, and machine learning performance. His work increasingly examines practical deployment challenges while maintaining theoretical rigor, with growing emphasis on diffusion models, generative AI, and the legal implications of AI systems. There's a clear trajectory from foundational privacy theory toward addressing real-world implementation challenges across diverse application domains. Canada CIFAR AI Chair Ontario Early Researcher Award 2024 Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies STOC Best Student Presentation Award ICML 2024 Best Paper Award Microsoft Research Fellow at the Simons Institute for the Theory of Computing Dr. Kamath actively mentors students, with notable successes including Valentio Iverson winning the Germain-Erdős Undergraduate Award and Chris Trevisan receiving the CRA Outstanding Undergraduate Researcher Award. His service to the research community is extensive, serving as Editor-in-Chief of TMLR, on the Executive Committee of the Learning Theory Alliance, and on steering committees for major conferences including ICML, COLT, and ALT. He has organized numerous workshops focused on privacy-preserving machine learning and differential privacy. Through The Salon research group, Dr. Kamath fosters a collaborative environment where postdoctoral fellows, graduate students, and undergraduates work together on cutting-edge problems at the intersection of statistics, algorithms, machine learning, and optimization. The group maintains strong connections with industry partners and participates in major research initiatives, including the Vector Institute's privacy and security research efforts. Looking forward, Dr. Kamath will be moving to the Computer Science department at NYU's Courant Institute of Mathematical Sciences in September 2026, where he plans to expand his research program.
Dan Lizotte is an Associate Professor jointly appointed to the Department of Computer Science in the Faculty of Science and the Department of Epidemiology and Biostatistics in the Schulich School of Medicine & Dentistry at Western University. Additional affiliations include the Schulich Interfaculty Program in Public Health and a cross-appointment to the Department of Statistics and Actuarial Sciences. Based in Middlesex College, London, Ontario, his contact email is dlizotte@uwo.ca. His research centers on machine learning and biostatistics for health decision support, with emphasis on sequential decision-making in chronic disease management where evolving patient health status and preferences inform adaptive interventions. Core contributions involve adapting reinforcement learning frameworks to model dynamic health decisions in public health and primary care settings, addressing methodological challenges in personalized medicine and risk prediction. Analysis of his publication record reveals consistent focus on healthcare applications of machine learning, particularly in chronic disease risk modeling using electronic medical records, intersectionality frameworks in public health AI, and Bayesian methods for dose personalization. His work bridges reinforcement learning with clinical decision support systems, advancing dynamic treatment regimes and statistical methodologies for evolving patient data. No scientific awards were mentioned in the provided text. The text does not specify any advisees, grant funding, or educational background details. Lizotte leads a research laboratory focused on machine learning applications in health, as evidenced by the dedicated lab site referenced in his contact information. His team likely explores intersections of statistical methodology, AI ethics, and clinical implementation for personalized health interventions.
Amir-massoud Farahmand is an Associate Professor at the Polytechnique Montréal (Department of Computer and Software Engineering) and a Status-Only Associate Professor at the University of Toronto (Department of Computer Science). He is also a Core Academic Member at Mila (Quebec AI Institute). His research focuses on computational and statistical mechanisms for designing efficient reinforcement learning (RL) agents and adaptive algorithms. Dr. Farahmand's research spans reinforcement learning, optimal transport, adversarial robustness, and model-based methods. He has extensively studied regularization in RL, distributional approaches, and algorithm design for stability and convergence. His textbook Lecture Notes on Reinforcement Learning (2021) emphasizes mathematical intuition over algorithmic collections. Recent publications highlight trends in high-update-ratio RL, distributional equivalence, and self-prediction for task understanding. He is actively involved in teaching, having previously instructed courses on machine learning, neural networks, and RL at the University of Toronto. Scientific Awards : Ontario Early Researcher Award (2024) for Accelerated Reinforcement Learning Algorithms Dr. Farahmand has mentored numerous students, including his first PhD graduate Yangchen Pan (now at Oxford) and MSc students like Allen Bao (AMD) and Farnam Mansouri (University of Waterloo). He is currently recruiting graduate students at Polytechnique Montréal and Mila for 2025 admissions.
Roxane de la Sablonnière is a Full Professor in the Department of Psychology at the University of Montreal, affiliated with the Faculty of Arts and Sciences. She directs the CSI—Laboratory on Social Change and Identity and is a member of the CÉCD—Centre for the Study of Democratic Citizenship and the CIRCA—Interdisciplinary Research Centre on the Brain and Learning. Her research focuses on social change, cultural identities, intercultural relations, and the psychological impacts of crises like the COVID-19 pandemic. She has advised over 20 graduate students and led numerous research projects funded by agencies such as the Fonds de recherche du Québec (FRQ) and the Social Sciences and Humanities Research Council (SSHRC). Education & Research Focus : Her work examines how rapid societal changes affect individuals, particularly in contexts of immigration, policy shifts (e.g., multiculturalism, secularism), and Indigenous communities. Recent studies include longitudinal analyses of pandemic-related behaviors, identity integration processes, and the role of cognitive strategies in managing conflicting identities. Grants & Collaborations : She leads projects like "S'engager pour mieux aller!" (FRQS-funded) and contributes to strategic networks such as the Centre pour l'Étude de la Citoyenneté Démocratique (CÉCD). Her research integrates psychology with sociology, leveraging mixed methods (e.g., text mining, longitudinal tracking) to address societal challenges. Labs & Teams : As CSI director, she oversees projects on social change dynamics and identity reconstruction. Her work bridges academic and applied domains, influencing policy debates on integration, public health, and community resilience.
Shiva Nejati is a Professor at the University of Ottawa 's School of Electrical Engineering and Computer Science . He holds a PhD in Computer Science from the University of Toronto and previously worked as a Senior Scientist (2012-2019) and Scientist (2009-2012) at the SnT Centre (University of Luxembourg) and Simula Research Laboratory. Research focus: Software engineering for cyber-physical systems (autonomous vehicles, IoT), blending formal verification, machine learning, and search-based testing Key tools developed: ARIsTEO, SOCRaTEs, SimCoTest, EPIcuRus Editorial roles: Associate Editor for EMSE Journal (2025–), ASE Journal (2025–), IEEE Transactions on Software Engineering (2020–2024) His work combines formal methods , empirical software engineering , and AI/ML to address verification challenges in complex systems, particularly through evolutionary algorithms and surrogate modeling . Notable collaborations include industry partners in telecommunications, automotive, and aerospace sectors. Recent publications emphasize large language models for requirements analysis, adversarial testing of vision systems, and multi-objective optimization for test generation. His Sedna Research Lab actively trains graduate students in these cutting-edge methodologies.
Dr. Karen Cochrane is an Assistant Professor at the University of Waterloo, specializing in Human-Computer Interaction (HCI) with a focus on wearable and tangible computing for mental health and accessibility. Her research integrates soma design, autoethnography, and design fiction to address the needs of underrepresented communities, particularly those with disabilities. Key projects include developing assistive technologies for queer/crip identities, accessible gaming wearables, and tactile interfaces for sensory exploration. She explores embodied experiences through multidisciplinary approaches, blending traditional craft practices with modern computational tools. Her work prioritizes participatory design methodologies, collaborating with occupational therapists, disabled communities, and neurodivergent individuals to co-create inclusive technologies. Recent innovations include VARitouch (a haptic feedback device), Breathing Scarf (for emotional regulation), and adaptive switches for children with motor disabilities. Her research extends into sensory narratives, exploring how fabric and data can articulate bodily experiences through projects like Sensory Data Dialogues and Queer/Crip Body Mapping. While no formal grants or awards are listed, her publications reflect a strong focus on ethical technology, accessibility, and embodied interaction. Current projects emphasize the intersection of wearable tech with identity expression, mindfulness practices, and neuromotor rehabilitation. She actively designs prototypes that bridge clinical needs with creative technology solutions, though specific lab affiliations or team collaborations are not detailed in available texts.
Dr. Ralph Evins is an Associate Professor and Director of the Graduate Program in the Department of Civil Engineering at the University of Victoria. He holds affiliations with the Urban Energy Systems laboratory at Empa and ETH Zurich in Switzerland. His expertise spans building energy simulation, energy system optimization, and machine intelligence applications in sustainable design. Evins holds an MEng from Imperial College London and an EngD from the University of Bristol. His research focuses on computational problem-solving in energy systems, including surrogate modeling, optimization algorithms, and machine learning. He develops tools like the Holistic Urban Energy Simulation (HUES) platform and BESOS software framework to bridge building, district, and city-scale energy analysis. His work emphasizes holistic systems thinking, integrating energy hubs, thermal modeling, and digital twin technologies. Recent articles explore surrogate model refinement, inverse modeling for building characterization, and decarbonization strategies. He collaborates with industry to translate academic innovations into practical solutions. Evins advises students in energy systems and leads projects on net-zero building design, retrofit prioritization, and smart grid integration. His research addresses challenges in climate adaptation, energy efficiency, and sustainable urban development through interdisciplinary approaches.
Mahdi S. Hosseini is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University and a faculty member of the Applied AI Institute. He holds a PhD from the University of Toronto (2016) and completed a postdoctoral fellowship at UofT, supported by MITACS-Elevate and NSERC fellowships. His research focuses on advancing deep learning and computer vision for computational pathology and healthcare technologies, aiming to develop AI tools for clinical diagnosis. He currently supervises graduate students and has published over 30 papers and two patents. Education: PhD in Electrical and Computer Engineering from the University of Toronto (2016), postdoctoral training at UofT collaborating with Huron Digital Pathology Inc. (Waterloo, Ontario). Research interests include deep learning, computer vision, computational pathology, medical imaging, and AI ethics (P4AI project). His work emphasizes developing explainable AI systems for clinical pathology, biomarker discovery, and efficient learning algorithms. Professional service includes serving as Area Chair for NeurIPS 2023, CVPR 2023-2024, and ECCV 2024. He reviews grants for CIHR, NSERC, and serves on program committees for key conferences (ICCV, CVPR, NeurIPS). Teaching includes courses on applied AI, machine learning, and deep learning for computational pathology at both graduate and undergraduate levels. Awards: MITACS-Elevate Fellowship (postdoc), NSERC Research Funding (2016-2017). His work has led to patents in diagnostic systems and has collaborated with hospitals and pathologists to advance clinical applications. Labs/Teams: Active collaborations with the Applied AI Institute at Concordia, Huron Digital Pathology, and healthcare institutions. Research emphasizes interdisciplinary approaches between computer science and clinical medicine.
