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Mark Schmidt is a Professor in the Department of Computer Science at the University of British Columbia, Faculty of Science. He holds the Canada Research Chair in Large-Scale Machine Learning and is a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute. His research spans multiple centers including CAIDA (Centre for Artificial Intelligence Decision-making and Action), the Data Science Institute, and the Machine Intelligence Learning Discovery (MILD) group. Dr. Schmidt's educational background includes a Ph.D. from UBC (2005-2010), an M.Sc. from the University of Alberta (2003-2005), and a B.Sc. from the University of Alberta (2000-2003). His academic career progressed from Postdoc positions at Simon Fraser University, Ecole Normale Superieure, and UBC to Assistant Professor (2014-2019), Associate Professor (2019-2024), and current Professor (2024-present) at UBC. His research focuses on machine learning optimization, with particular emphasis on improving numerical algorithms for large-scale machine learning applications. His work bridges theoretical optimization and practical applications across computer vision, natural language processing, and reinforcement learning. He has developed numerous optimization techniques including variants of stochastic gradient methods, coordinate descent algorithms, and natural gradient approaches. Analysis of his recent publications reveals a strong focus on optimization for over-parameterized models, particularly transformers and large language models. His work addresses critical challenges in step-size selection, convergence guarantees, and efficient implementation of optimization algorithms. His research has significant implications for training deep neural networks more effectively and understanding why certain optimization methods outperform others in practice. Among his notable awards are the Dorothy Killam Fellowship (2025), Arthur B. McDonald Fellowship (2024), Sloan Research Fellowship (2017), and multiple UBC teaching awards. He has also received the Lagrange Prize in Continuous Optimization and Best Paper Award at AISTATS 2021. Dr. Schmidt actively supervises numerous graduate students, with over 40 PhD and Master's students listed as current or alumni members of his research group. His laboratory maintains strong connections with industry partners, with many alumni securing positions at leading AI companies including Google, Amazon, Meta, and Microsoft.

• Machine Learning
• Artificial Intelligence
• Mathematical Optimization
• +6 more

Chantal David is a Professor in the Department of Mathematics and Statistics at Concordia University. Her research focuses on number theory and its intersections with mathematical statistics. Formal Affiliation: Concordia University, Department of Mathematics and Statistics Email: chantal.david@concordia.ca Office: Library Building, LB 927.09 Research Interests revolve around Number Theory , particularly: L-functions and their non-vanishing properties Elliptic curves over finite fields and function fields Statistics of group structures and root numbers Connections to random matrix theory and metaplectic functions Extremal primes and Frobenius distributions Drinfeld modules and supersingular reductions Article Trends show a focus on cubic and quartic L-functions, non-vanishing phenomena, and statistical properties of elliptic curves over finite fields. Recent work explores metaplectic theta functions, extreme value distributions, and one-level density analysis. Labs & Teams : She is affiliated with the Montreal Number Theory Group (CICMA) .

• Number Theory
• Elliptic Curves
• L-functions
• +1 more

Kyros Kutulakos is a Professor in the Department of Computer Science at the University of Toronto, where he leads research in computational imaging and 3D sensing. His affiliations include the Toronto Computational Imaging Group, Computer Vision Group, Dynamic Graphics Project (DGP), and Vector Institute Group. He teaches graduate and undergraduate courses such as CSC320 (Introduction to Visual Computing) and CSC2530 (Computational Imaging & 3D Sensing). His research interests span computational imaging, non-line-of-sight imaging, single-photon detectors, 3D sensing, and neural rendering. Notable contributions include advancements in structured-light imaging, time-of-flight systems, and super-oscillatory microscopy. He has advised numerous PhD and MSc students, fostering cutting-edge research in imaging technologies. Kutulakos has received prestigious awards, including the Dean’s Research Excellence Award (2023) and multiple best paper prizes (e.g., Marr Prize at ICCV 2023). He has served as program chair for ICCV 2013, ICCP 2010, and CVPR 2003, contributing to academic leadership in computer vision. His work bridges optics, photonics, and computation, with applications in autonomous systems, medical imaging, and astronomy. Current research focuses on extreme imaging scenarios, such as imaging in pitch-black environments and around corners, leveraging novel sensor designs and computational techniques.

• Computational Imaging
• Computer Vision
• 3D Sensing
• +9 more

Dr. Hassan Qudrat-Ullah is a Professor at the School of Administrative Studies, York University, and Coordinator of the Certificate in Logistics Management. He holds a PhD in Decision Sciences from NUS Business School and completed a post-doctoral fellowship at Carnegie Mellon University. His research focuses on dynamic decision making, system dynamics modeling, energy planning, and interactive learning environments. He teaches courses on quantitative methods, logistics, and decision analysis, informed by global industry experience across 20+ countries. Research interests include sustainability, climate change, systems thinking, and educational applications of decision sciences. He serves as Editor-in-Chief of the International Journal of Complexity in Applied Science and Technology and is a member of IEEE, DSI, and the International System Dynamics Society. His work has been published in Energy , Decision Support Systems , and others. Key projects include studies on 'structured-debriefing in dynamic decision making' and renewable energy policies in Africa. His recent articles (2023–2025) address AI integration in energy governance, system dynamics for supply chain resilience, and education for sustainability. Hassan advocates for systems thinking in K-12 education and enjoys traveling (visited 129 countries) and bird-watching.

• Sustainability
• Global/Climate Change
• System Dynamics
• +3 more

Alan Ritter is an Associate Professor at the School of Interactive Computing , Georgia Institute of Technology, with additional affiliation to the Machine Learning Center . His research focuses on Natural Language Processing , particularly robust models across domains/languages with fewer labels and efficient resource use, plus data-driven dialogue agents for open-topic conversations. Research Interests : Robust NLP models, cross-lingual transfer, resource-efficient learning, dialogue systems, cultural bias measurement, and privacy-aware language models Students : Mentors Ph.D. students in Georgia Tech's ML and CS programs, including Junmo Kang, Yang Chen, and Duong Minh Le. Alumni include Fan Bai (Ph.D. 2023), Yang Chen (Ph.D. 2024), and Andrew Li (M.S. 2024). Awards : NSF CAREER Award, Amazon Research Award, ACL 2024 Best Social Impact Paper, IUI 2009 Best Student Paper. Recent Work : Studies training budget allocation between supervised and preference-based finetuning, cross-lingual information extraction, cultural bias in LLMs, and privacy risk mitigation in social media disclosures. Service : Served as Program Chair for NAACL 2025, Area Chair for multiple top-tier conferences (COLM, EMNLP, ACL, EACL, AAAI). Email : alan.ritter@cc.gatech.edu

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +4 more

Jackie Chit Kit Cheung is an Associate Professor in the School of Computer Science at McGill University, where he co-directs the Reasoning and Learning Lab. He holds the Canada CIFAR AI Chair and serves as an Associate Scientific Co-Director at the Mila Quebec AI Institute. He is also a consulting researcher at Microsoft Research Montreal. Academic Background: Ph.D. in Computer Science, University of Toronto (2010–2014) M.Sc. in Computer Science, University of Toronto (2008–2010) B.Sc. (Honours) in Computer Science, minors in Linguistics and German, University of British Columbia (2004–2008) Research Interests: Jackie Cheung's research lies at the intersection of natural language processing, machine learning, and cognitive science. He focuses on natural language generation , automatic summarization , commonsense and pragmatic reasoning , and the evaluation of NLP systems . His work aims to build language models that reflect real-world structure and reasoning, with applications in health, education, and language revitalization. He is particularly interested in how implicit meaning is processed in context, a core concern in linguistic pragmatics. Publication Trends: His recent publications (2024–2025) show a strong emphasis on evaluation methodologies (e.g., COSMIC, ECBD), hallucination and factuality in language models, coreference and reasoning , and long-context modeling . He frequently collaborates across institutions and integrates insights from linguistics and psychology into NLP system design and analysis. Scientific Awards: Best Paper Award, ACL 2018 Outstanding Paper Award, NAACL 2018 Student Research Workshop SAC Award, ACL 2024 (for COSMIC) Best Poster Award, CMDO AI-Health Symposium 2024 Advising and Grants: He advises a large and diverse group of graduate students, including PhD and Master’s candidates, often in co-supervision with other faculty. His group has received support from major AI and health research initiatives, including CIFAR and Mila. He has trained alumni who have gone on to faculty and industry research positions. He has served in leadership roles in top NLP conferences, including as Senior Area Chair, Workshop Chair, and Program Chair of Canadian AI 2018. Labs and Teams: He co-directs the Reasoning and Learning Lab at McGill and is deeply involved with Mila – Quebec AI Institute . He founded the NLP Reading Group at McGill, which brings together researchers from computer science, linguistics, and information studies to discuss theoretical and applied NLP topics.

• Natural Language Processing
• Natural Language Generation
• Automatic Summarization
• +12 more

Alexei A. Efros is a Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, where he holds the Howard Friesen Professorship and is affiliated with the Berkeley Artificial Intelligence Research (BAIR) Lab. He previously served on the faculty at the Robotics Institute of Carnegie Mellon University (CMU) and completed a postdoctoral fellowship at the University of Oxford. His research spans data-driven computer vision, self-supervised learning, computational photography, and applications to computer graphics and robotics. His research interests include: Data-Driven Computer Vision Self-Supervised and Unsupervised Learning Generative Models and Image Synthesis Visual Representation Learning Applications in Robotics and Human-Computer Interaction Intersections with Human Vision and the Humanities The recent publications highlight a strong trend toward self-supervised learning, visual reasoning, and generative modeling, particularly diffusion models and 3D scene understanding. His work increasingly bridges computer vision with language, robotics, and cognitive science, emphasizing interpretability and real-world applicability. There is a clear focus on leveraging unlabeled data and developing methods for robust, generalizable AI systems. His scientific awards and recognitions include: Berkeley Fellowship Google Fellowship Soros Fellowship NSF Fellowship SIGGRAPH Outstanding Doctoral Dissertation Award Facebook Fellowship Adobe Fellowship CMU School of Computer Science Distinguished Dissertation Award ACM Doctoral Dissertation Honorable Mention Alexei Efros has advised numerous PhD students and postdocs, many of whom have gone on to faculty positions at top institutions including CMU, Stanford, MIT, Columbia, NYU, and Georgia Tech. His lab has received research funding from major tech companies and federal agencies, though specific grants are not detailed in the text. He teaches core computer vision and machine learning courses at both undergraduate and graduate levels at UC Berkeley. His research group is highly active, with ongoing projects in 3D perception, generative modeling, and vision-language systems. He leads a vibrant research lab at UC Berkeley, part of the BAIR consortium, collaborating with leading researchers such as Jitendra Malik, Trevor Darrell, Pieter Abbeel, and Angjoo Kanazawa. His lab fosters strong interdisciplinary connections with institutions worldwide, including Oxford, INRIA, and École Normale Supérieure.

• Computer Vision
• Self-Supervised Learning
• Computational Photography
• +7 more

Sriram Subramaniam is a Professor in the Department of Biochemistry and Molecular Biology at the University of British Columbia (UBC) and holds the Gobind Khorana Canada Excellence Research Chair in Precision Cancer Drug Design. His research leverages cryo-electron microscopy (cryo-EM) to advance structural biology and drug design, focusing on protein dynamics and therapeutic target identification. Education: PhD in Physical Chemistry (1987) from Stanford University; MSc in Chemistry (1981) from Indian Institute of Technology, Kanpur. Subramaniam's interdisciplinary work combines cryo-EM with computational tools and molecular biology to study protein structures at atomic resolution. His lab has pioneered cryo-EM applications in precision medicine, including mapping small molecule drugs on patient-specific cancer mutants. Recent publications (2024-2022) highlight his contributions to understanding SARS-CoV-2 immune evasion, structural mechanisms of ATPases, and AI integration in structural biology. His research spans viral entry mechanisms, CRISPR systems, and neurodegenerative disease pathways. Scientific Awards: Gobind Khorana Canada Excellence Research Chair NIH Director’s Award for Scientific Excellence Fellow of the Biophysical Society Breakthrough Prize nomination Based at the Djavad Mowafaghian Center for Brain Health, Subramaniam leads the Program in Cryo-EM Guided Drug Design, contributing to over 177 peer-reviewed publications with a career h-index of 58 and citations exceeding 12,340.

• cryo-electron microscopy
• structural biology
• drug-design
• +4 more

Kate Holland is an Associate Professor and Acting Associate Chair (Graduate) at the University of Toronto, specializing in 19th-century Russian literature and its cultural contexts. Her work focuses on the history of the novel, Russian literary theory, and the interplay between Russian and European literary traditions. She teaches courses such as 'The Golden Age of Russian Literature' and 'Dostoevsky in Literary Criticism and Theory.' Dr. Holland earned her PhD from Yale University (2004) and a BA/MA from the University of Cambridge (1998). Her research explores themes like genre evolution in Dostoevsky’s works, the publication culture of 19th-century Russian periodicals, and the theoretical frameworks of Historical Poetics. Her recent publications include A Dostoevskii Companion: Texts and Contexts (2018) and The Novel in the Age of Disintegration (2013), analyzing Dostoevsky’s engagement with genre and modernity. Her scholarship frequently examines narrative form, cultural history, and the intersection of literature with philosophical and religious thought.

• 19th Century Russian Literature
• Literary Theory
• History of the Novel
• +3 more

Tanya Trussler is Associate Professor in the Department of Economics, Justice, and Policy Studies at Mount Royal University. Her research examines structural determinants of violent crime, policing practices, crime prevention strategies, and public perceptions of criminal justice systems. Research explores police-community relations across different contexts, including recent projects on rural versus urban policing perceptions in Alberta and religious influences on policing in Pacific Island countries. Methodologically specializes in survey research and comparative analysis. Recipient of multiple research grants including from Immigration, Refugees and Citizenship Canada, and British Columbia's Crime Reduction Research program. Actively presents findings at major conferences including Congress of the Humanities and Social Sciences.

• Criminology
• Policing Studies
• Violent Crime
• +2 more

Claire Célérier is an Associate Professor of Finance at the Rotman School of Management, University of Toronto, where she holds the Canada Research Chair in Household Finance. Her academic career spans prestigious institutions including the University of Toronto (where she advanced from Assistant to Associate Professor between 2016-2022) and the University of Zurich (2014-2016). She maintains an affiliation with the People's History Lab and actively contributes to academic discourse through conference organization including 'Race, Equity and Public Policy' (2023) and 'Banks, States and the People' (2024). Dr. Célérier's research explores how finance can benefit households through multiple lenses including behavioral economics, asset pricing, and financial history. Her work investigates innovation in finance, diversity and inclusion impacts, and historical perspectives on financial systems. She has developed significant expertise in household finance, banking, and the intersection of finance with social issues, particularly racial disparities in financial markets as demonstrated in her award-winning research on the Freedman's Savings Bank. Her scholarly output shows a clear trajectory from foundational work on security design and investor behavior toward increasingly socially relevant research on financial inclusion, racial equity, and household financial decision-making. Recent publications demonstrate her growing focus on historical contexts of financial systems and their contemporary implications, particularly regarding marginalized communities. Canada Research Chair in Household Finance (2024) Bank of Canada Governor's Award (2022) University of Toronto Top 5% Merit Award (2021) Petro Canada Young Innovators Award Program (2021) Multiple Rotman School of Management Teaching Awards (2019-2021) Ieke van den Burg Prize for Research on Systemic Risk (2015) Dr. Célérier has successfully secured substantial research funding including Insight Development Grants (CAD 54,500), Connaught Research Grant (CAD 10,000), and the Marianne and Marcus Wallenberg Foundation Grant (EUR 400,000). She mentors several PhD students including Andrada Bilan (2020), Alexey Vasilenko (2023), and Purnoor Tak (Expected 2026), and has supervised numerous master's theses at both University of Toronto and University of Zurich. Her service contributions include Rotman School of Management MBA program committee, executive committee, seminar coordination, and faculty recruiting committee.

• Household Finance
• Banking
• Behavioral Economics
• +4 more

Aishwarya Agrawal is an Assistant Professor at Université de Montréal in the Department of Computer Science and Operations Research (DIRO), affiliated with Mila – Quebec Institute of Artificial Intelligence and a Canada CIFAR AI Chair. She also serves as a research scientist at Google DeepMind, spending one day weekly there. Education: B.E. in Electrical Engineering (IIT Gandhinagar, 2014), Ph.D. in Computer Science (Georgia Tech, 2019). Her research focuses on multimodal learning , deep learning , natural language processing , and computer vision , particularly in developing AI systems that 'see' and 'communicate' effectively. Grants & Awards: Canada CIFAR AI Chair, 2020 Sigma Xi Best PhD Thesis Award, NVIDIA Fellowship (2018–2019), and multiple fellowships from Google and Facebook. She leads projects like Advancing Multimodal Vision-Language Learning (CRSNG-funded) and StarDoc: Document Structure Extraction (MITACS). Research Contributions: Pioneered benchmarks like CulturalVQA and UI-Vision , and frameworks such as PROGRESS for efficient VLM training. Her work emphasizes cross-modal alignment, robust evaluation, and cultural understanding in AI systems. Labs/Teams: Active in Mila’s core academic group and collaborates with Google DeepMind on multimodal and vision-language research. Supervises a dynamic team of PhD and master’s students in Montreal.

• Multimodal learning
• Deep learning
• Natural language processing
• +3 more

Ian Goldberg is a Professor at the Cheriton School of Computer Science , University of Waterloo, Canada. He holds the Canada Research Chair in Privacy Enhancing Technologies and leads the Cryptography, Security, and Privacy (CrySP) group . He is affiliated with the Cybersecurity and Privacy Institute and the Institute for Quantum Computing . Research Focus: Security, privacy, and privacy-enhancing technologies (PETs) for the Internet, including Off-the-Record (OTR) Messaging , censorship resistance , private information retrieval , and zero-knowledge proofs . Recent Publications: His work addresses metadata protection , deniable authentication , and secure multi-party computation , with applications to anonymous communication and censorship resistance. Scientific Awards: Canada Research Chair in Privacy Enhancing Technologies Students: He advises current and former PhD/Master’s students in projects like Lox , PRSONA , Walkie-Talkie , and SkypeMorph . Notable advisees include Sajin Sasy (Privacy-Preserving Communications), Justin Tracey (PETs Development), and Tao Wang (Website Fingerprinting Defenses). Labs & Teams: Goldberg is a core member of the CrySP group and contributes to the Tor Project and Off-the-Record Messaging open-source initiatives. His research bridges theoretical and applied cryptography, with deployments in real-world systems like Tor and PIR protocols.

• Security
• Privacy
• Privacy-Enhancing Technologies (PETs)
• +5 more

Professor Harry E. Ruda holds the Stanley Meek Chair in Advanced Nanotechnology at the University of Toronto's Faculty of Applied Science and Engineering. He directs the Centre for Advanced Nanotechnology and leads the Electronic-Photonic Materials Group. His research spans semiconductor nanostructures, focusing on nanoelectronics and nanophotonics, with notable contributions to nanowire growth, quantum transport, and plasmonic effects. Education: B.Sc. in Physics, Imperial College London Ph.D. in Physics, MIT His research interests include: Semiconductor nanowire growth via VLS process Quantum transport and spintronics Nonlinear optical effects in quantum dots/nanowires Surface state control through passivation Photonic crystal structures for optical devices Carrier dynamics in low-dimensional systems Recent publications highlight advancements in InAs nanowire sensors, Coulomb impurity effects, photonic crystal design, and hybrid superconducting/semiconductor devices. His work bridges fundamental electron dynamics and applied quantum technologies. Scientific Awards: Fellow, Canadian Academy of Engineering Fellow, Royal Society of Canada Fellow, Institute of Physics (UK) Fellow, Institute of Nanotechnology Fellow, Institution of Engineering and Technology Fellow, Institute of Materials, Minerals and Mining Fellow, Canadian Academy of Engineering Teaching: MSE335: Materials Physics MSE490: Professional Ethics and Practice MSE1023: Quantum Transport

• Semiconductor Nanostructures
• Nanoelectronics
• Nanophotonics
• +5 more

Stephen S. Kudla is a Professor in the Department of Mathematics at the University of Toronto, located in the Bahen Centre for Information Technology. He holds the prestigious distinction of being a Fellow of the Royal Society of Canada (FRSC), reflecting his significant contributions to mathematical research. Professor Kudla's research focuses on the deep connections between number theory and geometry, with particular expertise in automorphic forms, arithmetic geometry, and theta functions. His work bridges abstract mathematical theory with concrete geometric structures, exploring how modular forms can encode arithmetic information about algebraic varieties. Analysis of Kudla's publication record reveals a sustained research program centered on the relationship between derivatives of Eisenstein series and arithmetic geometry. His work consistently explores how modular and automorphic forms can be used to study arithmetic cycles on Shimura varieties. A notable pattern in his research is the interplay between analytic objects (like Eisenstein series) and geometric structures (such as arithmetic cycles), with applications to understanding heights, intersection theory, and special values of L-functions. Fellow of the Royal Society of Canada (FRSC) While specific details about Kudla's advising activities aren't provided in the available materials, his extensive publication record with prominent collaborators like Michael Rapoport and Tonghai Yang suggests he has likely mentored numerous graduate students and postdoctoral researchers throughout his career. His participation in major mathematical conferences including the International Congress of Mathematicians (ICM) indicates recognition by the broader mathematical community. Kudla's research has clearly been supported by significant funding, as evidenced by his ability to maintain a sustained publication record in top mathematical journals.

• Automorphic Forms
• Arithmetic Geometry
• Theta Functions
• +4 more