Find Academic Supervisors

• Macroeconomics
• International Trade
• Public Finance
• +3 more

Bidyut Talukdar is an Associate Professor in the Department of Economics at the Sobey School of Business, Saint Mary's University. He currently serves as Coordinator for the MAE (Master of Applied Economics) and PBCAE (Post-Baccalaureate Certificate in Applied Economics) programs. His research focuses on macroeconomic policy analysis, organizational learning dynamics, international trade mechanisms, and optimal taxation frameworks. Dr. Talukdar’s work intersects theoretical economics with empirical applications, particularly in understanding the interplay between organizational capital and policy outcomes. His research interests span organizational capital accumulation, Ramsey taxation models, monetary-fiscal policy interactions, and J-curve phenomena in international trade. He has published extensively on topics such as learning-by-doing effects, exchange rate sensitivity, and trade balance dynamics. His articles demonstrate a strong emphasis on applying economic theory to real-world policy challenges. Dr. Talukdar holds a prominent position in the Sobey School of Business, contributing to both academic research and program administration. While no specific scientific awards or grants are highlighted in the provided text, his active publication record reflects ongoing engagement with critical issues in macroeconomics and international trade.

Henri DarmonView profile

Professor

• Number Theory
• Algebraic Geometry
• Elliptic Curves
• +3 more

Henri Darmon is a Distinguished James McGill Professor in the Department of Mathematics and Statistics at McGill University, affiliated with the Centre Interuniversitaire en Calcul Mathématique Algébrique (CICMA) and the Centre de Recherches Mathématiques (CRM). He holds citizenships of Canada, France, and Switzerland. His research focuses on algebraic number theory, particularly elliptic curves, modular forms, and L-functions, with contributions to the Birch and Swinnerton-Dyer conjecture and Stark conjectures. Education: B.Sc. Mathematics & Computer Science, McGill University (1987) Ph.D. Mathematics, Harvard University (1991) Key Positions: Director of CICMA (1998–2024) Editorial roles at journals like Commentarii Mathematici Helvetici and Transactions of the AMS Organizer of major conferences including CNTA, ICM satellite events, and thematic programs at MSRI and CRM Research Interests: Stark-Heegner points and Euler systems p-adic L-functions and Iwasawa theory Arithmetic of modular curves and Shimura varieties His work bridges analytic and algebraic approaches to number theory, emphasizing computational and geometric methods.

Bill SheelView profile

Professor

• Exercise Physiology
• Respiratory Physiology
• Neuromechanics
• +3 more

Dr. Bill Sheel is a Professor and Distinguished University Scholar at the School of Kinesiology, University of British Columbia. His research focuses on the integrative physiology of exercise, particularly respiratory and cardiovascular interactions. Email: bill.sheel@ubc.ca Office: Chan Gunn Pavilion, Room 221B, Vancouver, BC Lab: Health and Integrative Physiology Laboratory Research Interests: Dr. Sheel investigates respiratory physiology during exercise, cardiovascular modulation by breathing patterns, and human performance optimization. His work spans elite athletes, aging populations, and clinical applications. Publication Trends: Recent studies include pulmonary mechanics in exercise physiology, respiratory muscle training applications, and socio-cultural factors in sports science. Collaborative works address aging interventions and environmental influences on athletic performance. Scientific Recognition: Distinguished University Scholar, UBC Laboratory & Collaborations: Leads the Health and Integrative Physiology Laboratory at UBC, focusing on respiratory-cardiovascular integration and exercise tolerance mechanisms.

Angel Xuan ChangView profile

Associate Professor

• Natural language processing
• 3D scene understanding
• Multimodal grounding of language
• +5 more

Angel Xuan Chang is an Associate Professor at Simon Fraser University's School of Computing Science, where she leads research at the intersection of natural language processing, computer vision, and 3D scene understanding. She holds the prestigious Canada CIFAR AI Chair position and is affiliated with multiple research groups including 3DLG, GrUVi, SFU NatLang, SFU AI/ML, and VINCI. PhD in Computer Science, Stanford University MSc in Computer Science, Stanford University M.Eng in Electrical Engineering and Computer Science, MIT BSc in Computer Science and Engineering, MIT Professor Chang's research primarily focuses on connecting language to 3D representations of shapes and scenes, with particular emphasis on grounding language for embodied agents in indoor environments. Her work spans natural language processing and understanding, linking natural language with visual and 3D representations, multimodal grounding of language, embodied AI, and machine learning applications for biodiversity monitoring through the BIOSCAN project. She has developed methods for synthesizing 3D scenes and shapes from natural language and created various datasets for 3D scene understanding. Her recent publications reveal a strong trend toward integrating language understanding with 3D scene generation and manipulation, with increasing focus on practical applications in embodied AI and biodiversity monitoring. The research shows progression from foundational work on text-to-3D scene generation to more sophisticated approaches for evaluating semantic coherence in generated scenes and developing efficient methods for zero-shot scene modeling. Canada CIFAR AI Chair TUM-IAS Hans Fischer Fellow (2018-2022) Best paper award at 3DV 2025 for 'An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion' Professor Chang actively advises numerous graduate students who appear as first authors on her publications, indicating a strong mentoring program. Her research is supported through multiple channels including the CIFAR AI Chair position and likely various research grants supporting her BIOSCAN-related work and 3D scene understanding projects. She has been involved in organizing multiple workshops at major conferences including ICML, CVPR, and ICLR. Her research is conducted through several interconnected groups: 3DLG (3D Language and Graphics), GrUVi (Graphics, Vision, and Interaction), SFU NatLang (Natural Language Processing), SFU AI/ML, and VINCI. These groups work collaboratively on problems spanning language grounding, 3D scene understanding, embodied AI, and biodiversity applications, creating a rich interdisciplinary research environment.

Jonathan BaughView profile

Professor

• Quantum Information Science
• Nanoelectronics
• Semiconductor Devices
• +4 more

Jonathan Baugh is a Professor in the Department of Chemistry at the University of Waterloo, serving as Director of the Quantum Information Graduate Program. His research focuses on quantum devices, nanoelectronics, and molecular electronics with affiliations at the Institute for Quantum Computing and Waterloo Institute for Nanotechnology. He leads the Baugh Research Lab, exploring quantum control, semiconductor spin qubits, and superconducting hybrid systems. Research interests include quantum information processing, nanoscale charge transport, and the development of next-generation photonic sources. His work bridges quantum physics and materials science, with recent breakthroughs in dopant-free semiconductors and single-molecule transistors. Publications emphasize scalable quantum architectures, noise mitigation in quantum control, and phase-coherent molecular electronics. Current projects involve cryogenic CMOS device modeling and topological quantum computing in silicon-based systems. No awards are explicitly listed, though his work has been highlighted in invited reviews and special sessions on quantum systems. Advising focuses on graduate students in quantum nanotechnology and condensed matter physics. His lab collaborates on integrated quantum networks and III-V/Si nanowire photodetectors. Labs/Teams: Baugh Research Lab (Quantum Nanoelectronics Group), Institute for Quantum Computing (IQC), Waterloo Institute for Nanotechnology (WIN).

Patrick AllenView profile

Associate Professor

• Number Theory
• Galois Representations
• Automorphic Forms
• +3 more

Patrick Allen is an Associate Professor in the Department of Mathematics and Statistics at McGill University, where he contributes to research in number theory and related fields. He is affiliated with the Montreal Number Theory Group and the Centre Interuniversitaire en Calcul Mathématique Algébrique (CICMA), focusing on areas such as Galois representations, automorphic forms, and algebraic number theory. His work bridges algebraic geometry and arithmetic, with a particular emphasis on modularity lifting theorems and deformation theory. Allen's research interests include the study of CM fields, modular forms, and elliptic curves, alongside investigations into the Langlands program and p-adic methods. He has published extensively on topics such as potential automorphy, monodromy, and adjoint Selmer groups. His contributions address questions in arithmetic algebraic geometry and cohomological automorphic forms, often intersecting with representation theory. While his articles span over 20 years, recent work (2020–2023) emphasizes the modularity of Galois representations over CM fields and the application of automorphic techniques to solve problems in number theory. Allen’s research often involves collaboration with international experts in algebraic number theory and arithmetic geometry. Scientific Awards: None explicitly listed in the provided materials. Advising & Grants: No formal advisees or grant details are listed in the text. His affiliations with CICMA suggest participation in collaborative research initiatives, though specific grants are not mentioned. Labs/Teams: Active member of the Montreal Number Theory Group and CICMA, contributing to inter-university collaborative projects in algebraic number theory.

• Developmental Psychophysiology
• Temperament and Personality Development
• Neuroscience of Shyness and Social Anxiety
• +4 more

Professor Louis Schmidt is a leading academic in the Department of Psychology, Neuroscience & Behaviour at McMaster University , with a research focus on developmental psychophysiology, temperament, and the long-term effects of early adversity. His work bridges neuroscience, psychology, and behavioral science, emphasizing the interplay between brain function and socio-emotional development across the lifespan. Key research themes: Shyness, social anxiety, autism spectrum disorder, schizophrenia, and outcomes of extremely low birth weight. Recognized for mentoring postdoctoral fellow Kristie Poole, who was celebrated as a role model in the Child Emotion Laboratory. Scientific Awards : Royal Society of Canada recognition for contributions to research and scholarship. Research Trends from 15 recent publications include: Neurophysiological mechanisms of shyness (EEG, ERP, RSA) Impact of antenatal corticosteroids on adult brain function Intergenerational effects of maternal mental health interventions Cross-cultural comparisons of temperamental shyness Developmental consequences of preterm birth Behavioral and neural correlates of social anxiety in diverse populations Grants & Collaborations : Led the SNACS randomized controlled trial on antenatal corticosteroids, with applications in obstetrics and developmental neuroscience. Collaborates extensively on topics like autism spectrum disorder, schizophrenia, and emotion regulation. Labs & Teams : Directs the Child Emotion Laboratory at McMaster University, fostering interdisciplinary research on developmental psychopathology and neural mechanisms of temperament.

Mohammadreza KaramadView profile

Assistant Professor

• Electrochemistry
• Heterogeneous Catalysis
• Material Science
• +3 more

Mohammadreza Karamad is an Assistant Professor in the School of Sustainable Energy Engineering at Simon Fraser University (SFU), with a joint appointment in the Sustainable Energy Engineering department. His research focuses on computational materials discovery, leveraging quantum-mechanical methods (e.g., DFT) and machine learning (ML) to design advanced energy materials for clean technologies like hydrogen storage and catalysis. He holds a Ph.D. from the Technical University of Denmark (DTU) and completed postdoctoral research at Stanford University. His academic background includes leadership roles in the CMD Lab (Computational Materials Discovery), where he explores novel materials for electrochemical energy conversion processes. Key research areas include electrochemistry, heterogeneous catalysis, and material science, with a particular emphasis on CO2 reduction, ammonia synthesis, and sustainable energy storage solutions. Dr. Karamad collaborates with industry and academic partners to advance materials discovery through high-throughput computational screening and AI-driven approaches. He actively seeks motivated students (undergraduate and graduate) to join his research program, focusing on developing next-generation energy materials. His lab is located in room B8220, and he can be reached at mkaramad@sfu.ca. Notable technical contributions include pioneering work on transition metal nitrides for CO2 reduction, single-atom catalysts for ammonia synthesis, and machine learning frameworks for predicting material properties. His research bridges fundamental theory with practical applications, addressing global challenges in sustainable energy and environmental technology.

Anita HubleyView profile

Professor

• Adult Development and Aging
• Applied Measurement and Psychometrics (test development & validation)
• Assessment
• +7 more

Anita Hubley is a Professor at the University of British Columbia's Faculty of Education, Department of Educational and Counselling Psychology, and Special Education (ECPS). She serves as MERM Program Coordinator and directs the Adult Development and Psychometrics Lab. Her work focuses on psychometric test development/validation and quality of life research across adult populations. Education: Ph.D. in Psychology (Human Assessment specialization), Carleton University (1995) M.A. in Psychology (Lifespan Development and Aging), University of Victoria (1991) Pre-doctoral training at Geriatric Assessment Unit (Ottawa) and Neuropsychological Assessment Unit (Ottawa) Her research integrates psychometric theory with practical applications in aging populations, homeless/vulnerably housed individuals, and neuropsychological assessment tools. Key contributions include developing the Memory Test for Older Adults (MTOA), Hubley Depression Scale for Older Adults (HDS-OA), Quality of Life in Homeless and Hard-to-House Individuals (QoLHHI), and Subjective Age Identity Scale (SAIS). Scientific Awards: Killam Teaching Prize (2017) Distinguished Reviewer, Buros Institute of Mental Measurements (2013) She has taught graduate courses in Psychological Assessment, Measurement Principles, Scale Development, and Applied Neuropsychology, emphasizing ethical testing practices and response process research. Her lab's work on test adaptation for marginalized populations has informed international measurement standards.

Daniel J. LizotteView profile

Associate Professor

• Machine Learning
• Biostatistics
• Health
• +4 more

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.

Markus BrinkmannView profile

Associate Professor

• Exposure and risk assessment modelling
• Toxicokinetic modelling
• Aquatic ecotoxicology
• +3 more

Dr. Markus Brinkmann is an Associate Professor in the School of Environment and Sustainability at the University of Saskatchewan, Canada, and Director of the Toxicology Centre. He holds the Centennial Enhancement Chair in Mechanistic Environmental Toxicology. His research focuses on exposure and risk assessment modeling, toxicokinetic modeling, and aquatic ecotoxicology, with an emphasis on understanding contaminant effects under realistic environmental conditions. Affiliations: Associate Professor, University of Saskatchewan Director, Toxicology Centre Member, Global Water Futures Program Member, Global Institute for Water Security Education: Ph.D. (Biology) - RWTH Aachen University, Germany (2015) M.Sc. (Ecotoxicology) - RWTH Aachen University, Germany (2011) B.Sc. (Biology) - RWTH Aachen University, Germany (2009) Research Interests: Dr. Brinkmann develops computational models to predict contaminant uptake, effects, and cross-species extrapolation. His work integrates toxicology, environmental chemistry, and hydrology to address challenges like flood-related contaminant remobilization and dioxin-like compound toxicity. Key areas include: Mechanistic toxicology of emerging contaminants (e.g., 6PPD-quinone, PFAS alternatives) Sediment toxicity and bioavailability Wastewater surveillance for public health monitoring Grant Highlights: Global Water Futures Program (2018-2023): Hydrological-exposure models for environmental risk assessment Genome Canada (2016-2020): EcoToxChip for chemical prioritization Banting Postdoctoral Fellowship (2016-2018) Awards: Friedrich-Wilhelm-Award (2016) SETAC GLB Young Scientists Award (2016 PhD, 2012 MSc) Borchers medal (2016) Leadership: He advises on strategic research partnerships between Canada and Germany and collaborates globally on projects like the EcoToxChip and Project House Water . His work bridges academic research with practical environmental management solutions.

Hong HanView profile

Assistant Professor

• Cancer Biology
• Gene Regulation
• RNA Splicing
• +5 more

Hong Han is an Assistant Professor in the Department of Biochemistry & Biomedical Sciences within McMaster University's Faculty of Health Sciences and a member of the Centre for Discovery in Cancer Research (CDCR). She holds a Canada Research Chair and leads the Han Lab, which focuses on cancer biology, RNA regulation, and innovative high-throughput technologies for therapeutic discovery. Dr. Han earned her Ph.D. from the University of Toronto (2010-2016) and has established herself as a leading researcher in glioblastoma and alternative splicing regulation. Her interdisciplinary research integrates cancer biology, RNA science, and multilayer gene regulation to uncover mechanisms underlying cancer progression and treatment resistance. Her laboratory pioneers integrated technological platforms for large-scale genetic/drug screening and ultra-high-throughput single-cell profiling. The research focuses on three main areas: alternative splicing regulation in cancer (particularly glioblastoma and prostate cancer), multilayer mechanisms of glioblastoma heterogeneity and microenvironment evolution, and multiplexed screening approaches for therapeutic discovery in treatment-resistant cancers. Analysis of Dr. Han's recent publications reveals a strong emphasis on single-cell technologies to characterize glioblastoma heterogeneity, minimal residual disease states, and tumor-immune interactions. Her work increasingly bridges basic RNA biology with translational applications, particularly in developing novel therapeutic strategies targeting splicing networks and immune evasion mechanisms. Canada Research Chair Dr. Han teaches Advanced Techniques in the Biomedical Sciences (BIOCHEM 734). Her research program is supported by multiple funding sources, as evidenced by her extensive publication record in high-impact journals including Nature, Cell, Molecular Cell, and Nature Communications. She employs a comprehensive approach combining in vitro, in vivo, and patient cohort studies with cutting-edge genomic technologies. The Han Lab has developed innovative multiplexed screening platforms that enable simultaneous interrogation of thousands of conditions, ranging from CAR-T cells to small molecule therapeutics. This approach accelerates the discovery of novel cancer targets and therapeutic strategies for treatment-resistant cancers.

Hanadi SleimanView profile

Professor

• Chemical Biology
• Materials Chemistry
• Nanotechnology
• +3 more

Professor Hanadi Sleiman is a renowned academic in the Department of Chemistry at McGill University, specializing in DNA-based nanomaterials and their applications in drug delivery and supramolecular chemistry. She holds leadership roles, including Director of the NSERC CREATE training program in Nucleic Acids and President of the International DNA Nanotechnology Society (ISNSCE). Her research focuses on engineering DNA nanostructures for targeted therapies, such as cancer treatments, and advancing materials chemistry through DNA-functionalized systems. Education: Ph.D. in Chemistry, Stanford University (1990) Postdoctoral Fellow, University of Louis Pasteur (1993) Research Interests: Professor Sleiman’s work combines synthetic chemistry with DNA self-assembly to create programmable materials. Her lab designs DNA cages for drug encapsulation, explores DNA-minimal approaches to scalable materials, and integrates DNA with nanoparticles, polymers, and metals for biomedical applications. Key areas include cancer therapy, biosensors, and enzyme mimics. Awards & Honors: Fellow of the Royal Society of Canada (2017) Killam Research Fellowship (2018) R. U. Lemieux Award in Organic Chemistry (2018) William Dawson Scholar Award (2004–2012) Grants & Collaborations: She directs the NSERC CREATE program and collaborates with institutions like the Quebec Centre for Advanced Materials (QCAM) and the McGill Centre for Structural Biology (CRBS). Her training initiatives emphasize nucleic acid therapeutics and diagnostics. Labs & Teams: Her Sleiman Group at McGill develops innovative DNA architectures in the Otto Maass laboratory, with a focus on translational research for clinical applications.

• Environmental Psychology
• Nature Connection/Disconnection
• Behavior Change
• +4 more

Shannon Johnson is an Associate Professor in the Department of Psychology and Neuroscience at Dalhousie University, concurrently affiliated with the Departments of Pediatrics and Psychiatry. She serves as Director of Clinical Training and Co-Director of the Dalhousie Centre for Psychological Health, which provides mental health services to underserved populations while training clinical psychology students. Dr. Johnson holds a BA from Kalamazoo College, MSc and PhD from the University of Victoria, and a Postdoctoral Fellowship from Indiana University. Her research focuses on enhancing well-being through nature connection interventions, understanding resilience mechanisms in pediatric populations, and improving diagnostic practices for neurodevelopmental disorders. She investigates the physical and cognitive benefits of nature exposure, barriers to nature connection, and behavioral change strategies. Her work bridges clinical and environmental psychology, with recent studies examining nature-based interventions for stress reduction, pain adaptation in youth with juvenile idiopathic arthritis, and moral foundations in autistic children. She has pioneered the concept of Indoor Nature Exposure (INE) as a health-promotion framework. Dr. Johnson’s lab collaborates with healthcare providers to develop scalable mental health interventions, particularly for underserved communities. Her training programs emphasize evidence-based practices and culturally responsive care. Key contributions include validating the role of nature in cognitive restoration and challenging clinical biases in autism assessment.

Igor JurisicaView profile

Professor

• Data Science and Computational Biology
• Machine Learning
• Artificial Intelligence
• +6 more

Igor Jurisica is a Professor at the University of Toronto and a Senior Scientist at the Krembil Research Institute’s Data Science Discovery Centre for Chronic Diseases. He also serves as Visiting Scientist at IBM CAS, Scientific Director of the World Community Grid, and Chief Scientist at the Creative Destruction Lab (Rotman School of Management). His research focuses on integrative computational biology, data mining, and AI-driven models for cancer mechanisms, drug discovery, and chronic disease management. Key affiliations include the Osteoarthritis Research Program, Schroeder Arthritis Institute, and leadership roles in open science initiatives like the World Community Grid, a global distributed computing platform with 810,000+ volunteers. Jurisica’s work bridges computational tools (e.g., NAViGaTOR visualization platform, MirDIP databases) and clinical applications, emphasizing explainable AI in healthcare. Research interests span proteomics, microRNA regulation, systems vaccinology, and multi-omics integration for disease stratification. Notable contributions include identifying prognostic signatures in cancer and osteoarthritis, machine learning models for drug repurposing, and sportomics analyses of athletic biomarkers. He has been recognized as a Thomson Reuters Highly Cited Researcher (2014-2016) and ranked among the Top 100 AI Leaders in Oncology (2023). His labs develop open-access tools like PathDIP, OsteoDIP, and miRAnno to advance translational research.