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Melanie DirksView profile

Professor

• Clinical Psychology
• Developmental Science
• Peer Relationships
• +6 more

Professor Melanie Dirks is the Department Chair of Psychology at McGill University, specializing in Clinical Psychology and Developmental Science . Her research focuses on mapping social and emotional skills across childhood, adolescence, and young adulthood, with emphasis on peer/sibling relationships and their impact on psychological symptoms. Uses multimodal methodologies: observational studies, daily diaries, emotion recognition tasks Current projects: friendship quality maintenance, interpersonal victimization in youth, emotional communication dynamics Key research themes from publications include: Peer Relationships : Dissolution patterns, prosocial behavior, bullying, and dating aggression Emotional Processing : Vocal emotion recognition, neural reward responses, stress-neuroimaging links Contextual Influences : Family dynamics, socioeconomic status, post-pandemic social changes Her lab ( CASC Lab ) trains students in advanced social-emotional assessment techniques, with notable advisees including *Morningstar, *Gilbert, and *Santucci. Google Scholar articles (2014-2025) demonstrate expertise in developmental psychopathology, social neuroscience, and cross-cultural mental health determinants.

Luke SchaefferView profile

Assistant Professor

• Quantum Computing
• Theoretical Computer Science
• Combinatorial Game Theory
• +4 more

Luke Schaeffer is an Assistant Professor at the University of Waterloo, affiliated with the Cheriton School of Computer Science and the Institute for Quantum Computing (IQC). His academic journey includes a BMath and MMath from Waterloo, a PhD from MIT under Scott Aaronson, and postdoctoral positions at Waterloo and UMD. His research bridges quantum computing and theoretical computer science, with focuses on quantum circuit complexity, classical simulation of quantum systems, and combinatorics on words. Notable projects include studying the Clifford group's structure, low-depth quantum vs. classical circuits, query complexity of regular languages, and fermion-to-qubit encodings. He also explores non-quantum areas like combinatorial game theory and cellular automata. His work on interactive quantum advantage protocols demonstrated separations between quantum and classical circuit models, including results against AC⁰[p] and NC¹ classes. He co-developed sample-optimal classical shadow algorithms for pure states and classified Clifford gates over qubits into 57 distinct classes. Education: BMath and MMath, University of Waterloo PhD in Computer Science, MIT (advisor: Scott Aaronson) Key Research Themes: Quantum advantage and circuit complexity Automata and combinatorics on words Classical simulation techniques for quantum systems Algorithmic foundations of quantum computing He advises students in the Cheriton School of Computer Science, emphasizing quantum computing and theoretical computer science. His contributions include foundational results in quantum-classical separations and decidability of word properties via first-order logic.

• Differential Equations
• Nonlinear Analysis
• Fixed Point Theory
• +3 more

Marlene Frigon is a Full Professor in the Department of Mathematics and Statistics at the University of Montreal. Her research focuses on nonlinear analysis, differential equations, and fixed point theory, with significant contributions to differential inclusions, boundary value problems on time scales, and critical point theory for multivalued functionals. She has taught undergraduate and graduate courses including Analysis, Differential Equations, and Functional Analysis. Her publications span topics such as Ekeland's principle, Sturm-Liouville problems, and Hamiltonian systems, often applying topological methods to solve nonlinear equations. Her work includes existence theorems for dynamic equations on time scales, fixed point results for contractions in Fréchet spaces, and studies on singular Poisson equations in unbounded domains. She collaborates extensively with mathematicians like D. O'Regan and H. Gilbert.

• Theoretical Computer Science
• Machine Learning
• Algorithms
• +4 more

Nicholas J. A. Harvey is a Professor of Computer Science at the University of British Columbia. His research spans multiple areas of theoretical computer science with significant contributions to algorithms, machine learning theory, and network coding. Harvey's research focuses on the theoretical foundations of computer science with particular emphasis on: Theory and analysis of randomized algorithms Machine learning theory, including online learning and neural networks Network coding and information theory Combinatorial optimization and graph algorithms High-dimensional probability and statistics His work combines deep mathematical insights with practical applications in computer systems and networking. Analysis of Harvey's recent publications (2022-2025) reveals a strong focus on theoretical aspects of machine learning and optimization. His work spans continuous prediction with experts' advice, tight analyses of optimization algorithms like subgradient descent, and fundamental questions in randomized algorithms. A significant portion of his recent research addresses challenges in high-dimensional statistics, privacy-preserving estimation, and graph neural networks. His publications demonstrate a consistent pattern of addressing foundational theoretical questions with implications for practical machine learning systems. Harvey has maintained a strong publication record with an h-index of 35 and over 6,000 citations according to Google Scholar metrics. His influential papers include 'SkipNet: A Scalable Overlay Network with Practical Locality Properties' (2003), 'On the capacity of information networks' (2006), and 'Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks' (2019). Harvey has supervised numerous graduate students and postdoctoral researchers, contributing to the development of the next generation of theoretical computer scientists. His research has been supported by various grants from funding agencies including the Natural Sciences and Engineering Research Council of Canada (NSERC) and collaborations with industry partners. While specific details about laboratory facilities are not available in the provided information, Professor Harvey appears to be part of the theoretical computer science research group at the University of British Columbia, collaborating with researchers across multiple institutions on fundamental problems in algorithms and machine learning theory.

Donghyun KimView profile

Assistant Professor

• Psycholinguistics
• Speech Perception
• Phonetics
• +3 more

Donghyun Kim is an Assistant Professor at Kyungpook National University's Department of English Language and Literature. He holds a PhD in Linguistics from McGill University and conducted postdoctoral research in Cognitive Psychology at the University of Exeter with Nicolas Dumay and Arthur Samuel. His research focuses on psycholinguistics, speech perception, phonetics, applied linguistics, and second language acquisition, with particular emphasis on individual differences in speech perception under cognitive load, perceptual adaptation to variability, and the perception-production link in language learning. Education: PhD in Linguistics, McGill University Postdoctoral Research Fellow, Cognitive Psychology, University of Exeter Research Projects: Plasticity in Speech Perception under Cognitive Load Time Course of Novel Word Integration Longitudinal Acquisition of Novel Vowel Contrasts Perception-Production Link in Phonetic Imitation Courses Taught: Psycholinguistics 1 & 2 Language and Cognition Applied English Linguistics Workshops Conducted: Data Visualization for Linguistics with R Introduction to Data Analysis Using R Data Analysis in Linguistic Research His publications demonstrate trends in individual differences in phonetic adaptation, cognitive load effects on perception, and longitudinal studies of L2 vowel learning. Current teaching includes workshops on R-based data analysis methods for linguistics research.

Jean-Philippe HarveyView profile

Associate Professor

• Multiscale thermodynamics
• Materials science
• Computational thermochemistry
• +5 more

Jean-Philippe Harvey is an Associate Professor in the Department of Chemical Engineering at Polytechnique Montréal . His research focuses on computational thermochemistry, multiscale thermodynamics, and optimization of high-temperature metallurgical processes. Education: PhD and MSc in Metallurgical Engineering from Polytechnique Montréal Affiliation: Thermochemical Computing Research Center (CRCT) Research areas include atomistic simulations of metallic systems, green metallurgy for decarbonization, life cycle analysis of industrial processes, and development of thermodynamic databases for alloy design. His 2025 work emphasizes microwave-assisted reactors for syngas production and green hydrogen integration in pyrometallurgy. Awards: Philanthropic Chair in Materials Engineering (2023) Canada Foundation for Innovation (CFI) grant (2019) Harvey has supervised 14 graduate students since 2018, including 3 PhD candidates and 11 Master’s students. His publications span journals like Acta Materialia , Calphad , and Physical Chemistry Chemical Physics , covering topics from high-entropy alloys to plasma-based metal recycling.

Manar AmayriView profile

Researcher

• Machine Learning
• Data Science
• Explainable AI
• +5 more

Dr. Manar Amayri is a researcher affiliated with the CIISE (College/Institution name not explicitly provided), focusing on machine learning, data science, and their applications in energy management systems. Her work emphasizes smart buildings, smart cities, and human-in-the-loop AI systems. She explores advanced clustering techniques, domain adaptation, and anomaly detection in smart environments. Research Interests: Machine Learning, Explainable AI, Energy Management Systems, Smart Buildings, Data Clustering, Domain Adaptation, and Anomaly Detection. Her studies address challenges in load forecasting, occupancy estimation, and energy disaggregation using deep learning, Bayesian methods, and probabilistic models. Publications highlight innovations in hybrid models (e.g., Transformer-BiLSTM networks), data imputation for electric vehicle charging, and unsupervised learning techniques for smart grid applications. Recent work emphasizes interpretability and robustness in AI systems. No scientific awards or grants were explicitly mentioned in the provided texts. No student advisees or lab affiliations were listed.

Denis TherienView profile

Professor

• Complexity Theory
• Algebraic Characterizations of Complexity Classes
• Logical Tools
• +5 more

Prof. Denis Therien is a Professor at the School of Computer Science, McGill University. His research focuses on complexity theory , particularly on algebraic characterizations of complexity classes, logical tools, combinatorics, and probabilistic methods. Research Interests Thérien's work bridges algebra and computational complexity , with significant contributions to the understanding of finite semigroups, quantum automata, and temporal logic hierarchies. He has explored connections between algebraic structures and computational models, including circuits and communication complexity. Notable Publications Recent publications (2005-2006) include studies on the Crane Beach Conjecture, quantum automata, equation solvability in semigroups, and logical characterizations of computational models. Scientific Awards Humboldt-Forschungsstipendien (2000-01) James McGill Professor (2002-09)

Richard TreflerView profile

Associate Professor

• Formal Verification
• Model Checking
• Reactive Systems
• +6 more

Richard Trefler is an Associate Professor at the David R. Cheriton School of Computer Science, University of Waterloo. He specializes in formal verification, model checking, and the analysis of reactive and distributed systems. His work focuses on compositional reasoning, abstraction techniques, and parameterized systems to address state explosion challenges. Trefler has contributed to the synthesis of protocols, smart contract verification, and temporal logic applications. Research interests include automated reasoning tools, parameterized systems, communication protocols, and visual specifications for system design. His publications span conferences like VMCAI, ECOOP, and TACAS, with notable work on symmetry reduction and compositional verification. He received the Best Paper Award at the 22nd IFIP FORTE Conference for his contributions to modular reasoning in asynchronous systems. Current Student: Ruoxi Zhang Former Students: Zarrin Langari (2011), Shoham Ben-David (2009), Naghmeh Ghafari (2009), Jane D. Thi Tang (2006) His research explores cutting-edge topics such as protocol synthesis using temporal specifications and formal analysis of smart contracts. Trefler’s work bridges theoretical foundations with practical applications in distributed computing and cybersecurity.

• Artificial Intelligence
• Statistical Relational Learning
• Probabilistic Reasoning
• +4 more

David Poole is a Professor of Computer Science at the University of British Columbia, where he has established himself as a leading researcher in Artificial Intelligence with an h-index of 52 and over 19,000 total citations. His academic career spans more than three decades, with publications dating from 1985 to 2025, demonstrating sustained research productivity and relevance in the field. Professor Poole's research focuses on the integration of logical and probabilistic approaches to artificial intelligence. His work bridges symbolic AI and statistical methods, creating frameworks that handle uncertainty while maintaining logical expressiveness. He has made seminal contributions to probabilistic Horn abduction, Bayesian networks, and statistical relational artificial intelligence. His theoretical work on the Independent Choice Logic (ICL) has provided foundational frameworks for representing and reasoning with uncertainty in complex domains. His applied research spans geological analysis, educational technology, and mineral exploration. Professor Poole has authored influential textbooks including 'Computational Intelligence: A Logical Approach' (1998, 1,915 citations) and 'Artificial Intelligence: Foundations of Computational Agents' (2010, 1,750 citations), which have shaped AI education globally. His recent publications (2020-2025) demonstrate continued innovation in knowledge hypergraphs, relational neural networks, and geological hazard mapping applications. His most impactful research areas include: Statistical Relational Learning (as evidenced by the 2016 book 'Statistical Relational Artificial Intelligence') Knowledge Graph Embeddings and Hypergraphs CP-nets for preference representation (1,293 citations) Lifted probabilistic inference techniques Applications of AI in geological hazard prediction Professor Poole maintains active research collaborations with institutions worldwide, including Google DeepMind, universities in Hong Kong, Germany, and the United States. His work continues to influence both theoretical foundations and practical applications of artificial intelligence.

Liam Midzain-GobinView profile

Associate Professor

• Indigenous politics
• Settler colonialism
• Coloniality
• +5 more

Liam Midzain-Gobin is an Associate Professor in the Department of Political Science at Brock University , where he researches settler colonialism, Indigenous governance, and decolonial political thought. His work examines the persistence of settler colonial sovereignty and Indigenous strategies for self-determination. Ph.D. in Political Science from McMaster University (2020) M.A. in Political Science from University of Ottawa (2015) B.Soc.Sci. from University of Ottawa (2012) Midzain-Gobin’s scholarship bridges critical international relations theory and decolonial frameworks, focusing on Indigenous-settler relationships as a form of international politics. His research explores cosmological assumptions in settler sovereignty, the role of land in Indigenous nationhood, and reconstructive decolonization efforts. His 2025 book and recent publications highlight the logic of improvement in settler colonialism, Indigenous energy infrastructure resistance, and the politicization of data collection practices like the Aboriginal Peoples Survey. He employs community-engaged methods to support Indigenous sovereignty through projects like Building Inter-National Sovereignty and Indigenous Visions for Making Home . As Associate Editor of Critical Studies on Security , he contributes to critical security dialogues. His teaching includes courses on Indigenous politics, settler colonialism, and research methods.

• Mathematical Foundations of Machine Learning
• Explainable Artificial Intelligence
• Online Convex Optimization
• +4 more

Tim van Erven is an Associate Professor of Machine Learning at the Korteweg-de Vries Institute for Mathematics within the Faculty of Science at the University of Amsterdam. His research focuses on the mathematical foundations of machine learning, with particular expertise in online convex optimization, statistical learning theory, and explainable AI. He leads a research group dedicated to developing mathematically rigorous machine learning methods that work effectively without manual fine-tuning. His research interests span the mathematical foundations of machine learning, with emphasis on explainable machine learning, adaptive methods in online convex optimization, faster-than-minimax rates for 'easy data' in statistical learning, PAC-Bayesian concentration inequalities, and statistical learning theory with frequentist analysis of Bayesian methods. His work bridges theoretical guarantees with practical applications, recently shifting toward formal mathematical analysis of explainability methods for black-box AI systems. His recent publications reveal a clear evolution from foundational work in online learning and statistical theory toward explainable AI, with increasing focus on theoretical guarantees for concept learning and algorithmic recourse. The publications demonstrate strong methodological rigor while addressing practical challenges in interpretability and robustness of machine learning systems. Scientific Awards: VICI grant by the Dutch Research Council (2025) VIDI grant by the Dutch Research Council (2019) TOP grant by the Dutch Research Council (2016) NIPS 2014 outstanding reviewer award Rubicon grant by the Dutch Research Council (2011) Van Erven serves in significant academic leadership roles including as a member of the board of directors for COLT, co-chair for the AI & Mathematics initiative, and organizer of the thematic seminar on machine learning. He has successfully secured multiple competitive research grants and leads a research group working on the Mathematical Foundations for Explainable AI project, with several PhD and Postdoc positions currently open.

Neerusha Baurhoo GokoolView profile

Assistant Professor

• Higher education management
• Pedagogical systems
• University and college teaching methods
• +7 more

Neerusha Baurhoo Gokool is an Assistant Professor in the Department of Psychopedagogy and Andragogy at the Faculty of Education Sciences, University of Montreal. She serves as the Director of LIRES (Laboratoire interdisciplinaire de recherche sur l'enseignement supérieur) and teaches courses including EDU6646 Stage en enseignement collégial and PPA6617 Gestion de classe en enseignement supérieur. Her academic journey includes a PhD in Education from McGill University where she was awarded the Governor General's Gold Medal. Dr. Baurhoo Gokool's research focuses on higher education pedagogy, classroom management practices, and inclusive teaching strategies for students with learning disabilities. Her expertise spans educational technologies, motivation in science and technology education, open and distance learning, and the implementation of Universal Design for Learning frameworks. She examines barriers faced by both students with learning disabilities and their instructors in science education contexts. Her recent publications demonstrate a clear research trajectory focused on disability discourses in education, with an emphasis on moving beyond the limitations of both medical and social models of disability through Bronfenbrenner's ecological model. Current projects include an AI-based support system for first-year CEGEP students (2023-2025), development of gamified web applications for transversal skill development (2023-2024), and a living laboratory for innovative learning technologies in higher education (2024-2030). CRSH Partnership Grant (2024-2030): Un laboratoire vivant sur les technologies d'apprentissage innovantes FRQSC Research Team Support (2022-2027): Regards interdisciplinaires sur les missions de l'enseignement supérieur PIA Grant (2023-2025): AI-based support system for first-year students Ministère de l'Enseignement supérieur Grant (2023-2024): Developing transversal skills through gamified web applications As Director of LIRES, Dr. Baurhoo Gokool leads interdisciplinary research on higher education, supervises graduate students across multiple programs including the Master's in Education (Higher Education option) and the DESS in Higher Education, and contributes to microprograms in postsecondary and higher education teaching. Her work bridges theory and practice, aiming to transform educational experiences for diverse learners while addressing systemic barriers in science education.

Ivar MendezView profile

Professor

• Neurosurgery
• Remote Robotics
• Stem Cell Therapy
• +2 more

Dr. Ivar Mendez is Professor Emeritus in the Department of Surgery at the University of Saskatchewan and Director of Virtual Care and Remote Presence Robotics Programs. A neurosurgeon and neuroscientist, his research spans functional neurosurgery, brain repair, stem cell therapy, and remote-presence robotics for healthcare delivery. Mendez has pioneered robotic applications including the first long-distance telementoring neurosurgery (2002) and remote neuromodulation programming (2013). He leads initiatives deploying telerobotic ultrasound services to rural/Indigenous communities. His honors include Officer of the Order of Canada and Queen Elizabeth II Platinum Jubilee Medal. Awards: Canadian Red Cross Humanitarian of the Year (2010) Health Canada Award for Improving Health of Canadians (2011) Government of Canada Public Service Award of Excellence (2016) Officer of the Order of Canada (2022)

Matthew JohnsonView profile

Associate Professor

• Cosmology
• Theoretical Physics
• String Theory
• +5 more

Matthew Johnson is an Associate Professor in the Department of Physics and Astronomy at York University and an Associate Faculty member at the Perimeter Institute for Theoretical Physics. His research focuses on theoretical cosmology, including cosmic inflation, eternal inflation, dark energy, and confronting fundamental theories with observations of the Cosmic Microwave Background (CMB). He explores topics such as topological defects, string theory, and gravitation, with a particular interest in designing data analysis algorithms to interpret cosmological datasets. Johnson's affiliations include York University and the Perimeter Institute. His work spans computational and theoretical approaches to understanding the universe’s origins and evolution. He collaborates widely, contributing to projects like the Atacama Cosmology Telescope and the CMB-HD Collaboration. His research interests emphasize cosmological models, phase transitions in the early universe, and the implications of string theory’s extra dimensions for cosmology. He investigates the Multiverse concept and the dynamics of bubble universes in eternal inflation scenarios. Johnson actively supervises graduate students in the York University Physics and Astronomy program and collaborates with institutions globally. His funding sources include NSERC, the Foundational Questions Institute, and the New Frontiers in Astrophysics and Cosmology grant program.