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Reihaneh Rabbany is an Assistant Professor at the School of Computer Science, McGill University, and a core faculty member of Mila - Quebec's artificial intelligence institute. She holds the Canada CIFAR AI Chair and is affiliated with the Center for the Study of Democratic Citizenship. Her research focuses on complex data analysis at the intersection of network science, data mining, and machine learning. Research Interests: Network Science Data Mining Graph Representation Learning Unsupervised and Self-supervised Learning Anomaly Detection Social Good Applications Publication Trends show emphasis on temporal graph analysis, community detection, misinformation identification, and interdisciplinary collaborations with political science and criminology experts. Notable Awards Canada CIFAR AI Chair CAIAC 2021 Best Master's Thesis Award (co-supervisor) Advising includes mentoring PhD and MSc students across multiple institutions, with graduated students transitioning to roles at Microsoft Research, Mila, Yale, and Google. Labs & Collaborations: Leads the Complex Data Lab at McGill, collaborates with Mila, and contributes to community evaluation frameworks like CommunityEvaluation and TopLeaders algorithm.

• Network Science
• Data Mining
• Machine Learning
• +3 more

Golnoosh Farnadi is an Assistant Professor at McGill University's School of Computer Science and an Adjunct Professor at the University of Montréal. She serves as a Visiting Faculty Researcher at Google, a Core Academic Member at MILA (Quebec Institute for Learning Algorithms), and holds a prestigious Canada CIFAR AI Chair. Farnadi co-directs McGill's Collaborative for AI & Society (McCAIS) and founded the EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms), which focuses on advancing algorithmic fairness and responsible AI. Her educational background includes a Ph.D. in Computer Science from KU Leuven and Ghent University (2017), with postdoctoral research at the University of Montreal/MILA (2018-2020) and the University of California, Santa Cruz (2017-2018). During her doctoral studies, she was a visiting scholar at UCLA, University of Washington, Tsinghua University, and Microsoft Research. Dr. Farnadi's research centers on developing mathematical tools and algorithms for fairness-aware machine learning systems. Her work addresses bias and discrimination in AI decision-making across critical domains including healthcare, criminal justice, financial services, and social media. She has pioneered approaches to ensure fairness in deep learning models, particularly in sequential decision-making under uncertainty. Her research bridges theoretical foundations with practical applications, examining how AI systems can be designed to promote equity while maintaining performance. Analysis of her recent publications reveals a strong focus on practical implementations of fairness mechanisms across diverse AI applications. Her work spans technical domains from generative models and large language models to recommender systems and healthcare optimization. A unifying theme is the development of mathematically rigorous frameworks that balance performance with fairness considerations, with increasing attention to cultural diversity in multilingual AI systems and privacy-preserving fairness approaches. Google Scholar Award (2021) Facebook Research Award (2021) Rising Stars in AI Ethics (2021) Google Award for Inclusion Research (2023) WAI Responsible AI Leader of the Year Finalist (2023) 100 Brilliant Women in AI Ethics (2023) Canada CIFAR AI Chair Dr. Farnadi advises numerous doctoral and master's students across McGill University, University of Montréal, and MILA, with research focusing on fairness, privacy, and responsible AI. Her EQUAL Lab brings together researchers from computer science, social sciences, and policy domains to address systemic challenges in AI ethics. She has secured significant research funding from Google and other major organizations to support her work on fairness-aware AI systems, with applications spanning healthcare, social media safety, and public policy. The EQUAL Lab serves as a hub for interdisciplinary research on algorithmic fairness, bringing together computer scientists, social scientists, and policy experts. The lab's work spans theoretical foundations of fairness metrics, practical implementations in real-world systems, and policy recommendations for responsible AI deployment. Current projects include developing frameworks for fair kidney exchange programs, mitigating cultural stereotypes in multilingual language models, and creating privacy-preserving approaches for detecting online harms while protecting user data.

• Algorithmic Fairness
• Privacy-preserving Machine Learning
• Responsible AI
• +5 more

Dr. Adegboyega Ojo is a Professor at the School of Public Policy and Administration (SPPA), Carleton University , holding the prestigious Canada Research Chair (Tier 1) in Governance and Artificial Intelligence since 2014. His work bridges digital government , AI governance , and smart city development , with a focus on public sector innovation and policy analytics . Research Interests Smart Cities and Urban Digital Infrastructure Open Government Data and Transparency Blockchain Applications in Public Administration AI Ethics and Algorithmic Governance Electronic Participation and Citizen Engagement Publication Trends : His research emphasizes interdisciplinary approaches to digital governance, often involving data-driven policy analysis , smart city frameworks , and open data platform design . Collaborations span institutions in Ireland, Poland, and the U.S., with citations exceeding 5,200. Scientific Awards : Canada Research Chair in Governance and AI (Tier 1) Labs & Teams : Collaborates with the Insight Centre for Data Analytics (Ireland), Delft University of Technology , and United Nations University on projects like OpenGovIntelligence and BOLD (Big and Open Linked Data).

• Digital Government
• Artificial Intelligence Governance
• Public Sector Innovation
• +6 more

Catherine Valcke is a Professor at the University of Toronto Faculty of Law , where she served as Associate Dean and Acting Associate Dean. She holds degrees from the University of Sherbrooke (LL.B. Civil Law), University of Toronto (LL.B. Common Law), University of Chicago (LL.M.), and Columbia University (S.J.D.). Her academic career includes visiting appointments at the University of Montreal , University of Tunis II , and the Center for Transnational Legal Studies . Education: J.S.D., Columbia University (1997) LL.M., University of Chicago (1989) LL.B. (Common Law), University of Toronto (1988) LL.B. (Civil Law), University of Sherbrooke (1985) Research focuses on comparative legal theory , contractual obligations , and Anglo-French legal convergence , with a current SSHRC-funded project: French and English Private Law Paradigms . Her 2018 book Comparing Law: Comparative Law as Reconstruction of Collective Commitments proposes an interpretive framework for comparative studies. Recent publications analyze legal harmonization in bilingual contexts and AI’s impact on legal scholarship , including co-authorship of the forthcoming Théorie des Obligations (5th ed., 2022) and co-editorship of the Elgar Encyclopedia of Comparative Law (3rd ed., 2023). Awards include SSHRC grants and election to the International Academy of Comparative Law (2007). Her work has been cited by the UK House of Lords in Chartbrook Ltd. v. Persimmon Homes Ltd. (2009).

• Comparative Law
• Contract Law
• Civil Law
• +9 more

Victoria Lemieux is a Professor at the University of British Columbia (UBC) Faculty of Arts, School of Information, and Cluster Lead for Blockchain@UBC, Canada’s largest research cluster focused on blockchain technology. Her research centers on risks to trustworthy records in blockchain systems and their impact on transparency, financial stability, and human rights. She has pioneered Canada’s first research-oriented graduate blockchain training program and organized multiple interdisciplinary summer institutes. Education: Ph.D. in Archival Studies from University College London (2002), Certified Information Systems Security Professional (CISSP, 2005). Affiliated with UBC’s Peter Wall Institute for Advanced Studies, Sauder School of Business, and Institute for Computers, Information and Cognitive Systems (ICICS). Research interests span blockchain technology , trustworthy records , risk management , information governance , and visual analytics , with recent work addressing healthcare data frameworks, Web3 AI integration, and socio-cultural dynamics of decentralized systems. She has published extensively on blockchain applications in archives, land transactions, and privacy-preserving technologies. Scientific Awards : 2015 Emmett Leahy Award 2015 World Bank Big Data Innovation Award 2016 Emerald Literati Award 2016 Emerald Literati Outstanding Paper Award Supervision: Currently accepts doctoral students in Computational Archival Science and blockchain-related archival research. Affiliated with the Blockchain@UBC cluster and multidisciplinary research teams exploring decentralized systems for social good.

• Blockchain Technology
• Trustworthy Records
• Records and Information Management
• +8 more

Olga Vechtomova is a Professor at the University of Waterloo, affiliated with the Information Systems research group and specializing in Search Engines and Natural Language Processing. Her work bridges computational creativity, multimodal systems, and AI-driven text generation. She leads projects like LyricJam , a real-time lyric generation system for live music, and explores applications in dynamic story generation, hate speech detection, and low-resource summarization. Her research emphasizes ethical AI, creative technologies, and leveraging large language models for diverse tasks. Research interests include natural language processing, machine learning, and multimodal interaction. Recent work focuses on artistic inspiration modeling, stylized text generation, and improving NLP efficiency through semi-supervised learning and distillation techniques. Her contributions span over 60 papers since 2000, with a strong emphasis on foundational NLP challenges and real-world applications. She collaborates on systems like Promptmix for model distillation and LyricJam sonic for music-audio lyric generation.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +3 more

Richard Evans is an Associate Professor in Digital Innovation at the Faculty of Computer Science, Dalhousie University, where he contributes to research and teaching in digital transformation, human-computer interaction, and information management. He is actively involved in the IEEE Technology and Engineering Management Society, currently co-chairing the Design and Engineering Technical Activity Committee and serving on the editorial board of IEEE Transactions on Engineering Management . His educational background includes a PhD in Knowledge Management for Collaborative Product Design from the University of Greenwich (2013), a PG Cert in Higher Education (2015), an MSc from the University of Wales (2010), and a BA (Hons.) from the University of Exeter (2008). Richard's research focuses on digital innovation and transformation, with strong emphasis on design and engineering management, information systems, and knowledge management in distributed environments. His work bridges academic research and industrial applications, particularly in aerospace and manufacturing sectors. He has published over 90 scientific papers and helped co-found the International Conference on Artificial Intelligence for Good , highlighting his commitment to socially beneficial technology. His research output spans topics such as collaborative systems, user-centered design, and knowledge modeling, indicating a consistent trajectory toward human-centric technological innovation and digital transformation strategies. University of Greenwich prize for Best PhD student in 2013 Richard has supervised and mentored students at various levels during his tenure at multiple institutions including Brunel University London, University of Westminster, and Dalhousie University. He has secured research funding through industrial partnerships such as BAE Systems and has built a global network of academic and industry collaborators. His work often involves interdisciplinary grants focused on engineering management, digital design, and AI for societal good. Richard is affiliated with key research clusters in HCI, Visualization & Graphics at Dalhousie and leads initiatives connecting design, engineering, and digital innovation. He collaborates with international teams through IEEE and co-founded a major conference promoting ethical AI, demonstrating strong leadership in building research communities.

• Digital Innovation
• Human-Computer Interaction (HCI)
• Visualization
• +6 more

Blayne Haggart is a Professor in Political Science at Brock University, Canada. His research focuses on the international political economy of knowledge, particularly intellectual property rights, data governance, and internet governance. He previously worked as a professional journalist and economist with the Parliamentary Information and Research Service before transitioning to academia. BA (Carleton University) MA (University of Toronto) MA (Carleton-NPSIA) PhD (Carleton University) Dr. Haggart's current research explores: International Political Economy frameworks Digital policy and governance Intellectual property rights in the digital age North American political economy dynamics Intersections between sports and politics His scholarly work has been published in leading journals including: Canadian Public Administration Telecommunications Policy Journal of Digital Media & Policy Journal of Information Policy Dr. Haggart serves as: Senior Fellow with the Centre for International Governance Innovation Associate Senior Fellow with the Käte Hamburger Kolleg/Centre for Global Cooperation Research

• International Political Economy
• International Relations
• Digital Policy
• +3 more

Dr. Ying Zhu is an Associate Professor in the Department of Marketing and Consumer Studies at the Lang School of Business and Economics, University of Guelph. She holds a Ph.D. in Marketing from Texas A&M University, alongside master’s degrees in Computer Science (Minzu University, China) and Management (University of Lethbridge, Canada). Previously, she served as an Associate Professor at UBC Okanagan. Her research focuses on digital marketing, consumer behavior, marketing analytics, and the impact of technology (e.g., AI, Metaverse) on consumer decisions. She has published 19 peer-reviewed articles since 2017, many in top-tier journals like American Psychologist and European Journal of Marketing . Her work explores themes such as brand extension strategies, digital advertising effectiveness, and sustainability initiatives. Dr. Zhu has received significant funding through SSHRC grants and has been recognized for her teaching excellence, including the Golden Apple Award. She actively advises graduate students and volunteers with community organizations like the Kelowna Museums Society. Her research on smartphone-induced consumer behavior has garnered media attention in outlets like Time Magazine , Global News , and Science Daily .

• Digital Marketing
• Consumer Behavior
• Marketing Analytics
• +5 more

Irina Rish is a Full Professor at the Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute, where she leads the Autonomous AI Lab. She holds a Canada Excellence Research Chair (CERC) and a CIFAR AI Chair, reflecting her leadership in foundational AI research. Her work is supported by major initiatives, including the U.S. Department of Energy’s INCITE project on Summit and Frontier supercomputers. PhD in AI, University of California, Irvine MSc in AI, University of California, Irvine MSc in Applied Mathematics, Moscow Gubkin Institute Her research focuses on machine learning, neural scaling laws, emergent behaviors in foundation models, continual learning, robustness, and neuroscience-inspired AI . She explores how AI systems can become more general, flexible, and aligned with human cognition. Her recent work investigates training dynamics in large language models, efficient pruning techniques, and the development of time-series foundation models. The analysis of her recent publications reveals a strong focus on scaling behaviors, continual adaptation, and robustness in AI systems . Her work spans theoretical understanding of training dynamics (e.g., zero-sum learning), practical optimization methods, and applications in climate modeling and mental health. She emphasizes open science, leading open-source projects and co-founding Nolano.ai to build efficient, compressed foundation models. Canada Excellence Research Chair (CERC) CIFAR AI Chair IBM Eminence & Excellence Award (2018) IBM Outstanding Innovation Award (2018) IBM Outstanding Technical Achievement Award (2017) IBM Research Accomplishment Award (2009) Irina Rish advises a large group of PhD and Master’s students across Université de Montréal, McGill, and Concordia. She leads major research grants and collaborates internationally on HPC-based AI research. She is also the co-founder and CSO of Nolano.ai, driving innovation in efficient AI systems. She leads the Autonomous AI Lab, which focuses on building large-scale foundation models, understanding neural scaling laws, and developing bio-inspired learning systems. She actively organizes reading groups on scaling, continual learning, and out-of-distribution generalization, fostering a collaborative research environment.

• Machine Learning
• Neural Scaling Laws
• Emergent Behaviors in AI
• +13 more

Janarthanan Rajendran is an Assistant Professor and the Sexton Chair in Reinforcement Learning at the Faculty of Computer Science, Dalhousie University, in Halifax, Nova Scotia, Canada. He is actively involved in research, teaching, and mentoring, with a focus on deep reinforcement learning and its applications in complex, dynamic environments. Education: Postdoctoral Fellow, Mila Quebec AI Institute and University of Montreal, Canada (2023) PhD in Computer Science and Engineering (AI stream), University of Michigan, Ann Arbor, USA (2021) MTech and BTech in Electrical Engineering, Indian Institute of Technology Madras, India (2016) His research focuses on enabling machines to learn through interaction, with core interests in deep reinforcement learning, model-based RL, multi-agent systems, transfer learning, and applications in materials science and economics. He also explores the integration of large language models and foundation models into reinforcement learning frameworks. His work emphasizes adaptivity, lifelong learning, and societal implications of AI. The most recent publications show a strong trend in advancing cooperative multi-agent systems, developing adaptive and memory-efficient RL methods, and applying RL to real-world challenges such as crystal design and dynamic pricing. His research bridges theoretical innovation with practical application, often in interdisciplinary contexts. Scientific Awards: Sexton Chair in Reinforcement Learning Dr. Rajendran is actively involved in mentoring graduate students and fostering an inclusive research environment. He is currently recruiting PhD and MCS students at Dalhousie University. He has no formal grants listed in the text, but his research chair and active publication record suggest strong funding support. He is also engaged in the broader AI community, having organized and participated in major conferences such as the Atlantic Canada AI Summit and NeurIPS. Labs and Research Groups: He leads a research group focused on deep reinforcement learning at Dalhousie University, working on topics including model-based RL, off-policy learning, and leveraging external knowledge sources. The group emphasizes inclusivity and supports underrepresented groups in computer science research.

• Reinforcement Learning
• Deep Learning
• Artificial Intelligence
• +12 more

Nisarg Shah is an Associate Professor in the Department of Computer Science at the University of Toronto, affiliated with the Theory Group. He also serves as a Research Lead at the Schwartz Reisman Institute for Technology and Society and a Faculty Affiliate at the Vector Institute for Artificial Intelligence. Education: Ph.D. in Computer Science, Carnegie Mellon University (2016) B. Tech. with Honors in Computer Science & Engineering, IIT Bombay (2011) Shah's research focuses on the theoretical foundations of artificial intelligence, particularly algorithmic fairness, social choice theory, game theory, and mechanism design. His work addresses fair resource allocation, strategic agent behavior, and robust AI system design through interdisciplinary approaches combining computer science, economics, and cognitive psychology. Recent publications highlight temporal fair division, constrained allocations, and market value integration in fair division. His 2024 awards include the prestigious IJCAI Computers and Thought Award and Kalai Prize for game theory contributions. Scientific Awards: 2024 - IJCAI Computers and Thought Award 2024 - Kalai Prize in Game Theory and Computer Science 2022 - MIT Technology Review Innovators Under 35 2020 - IEEE Intelligent Systems AI's 10 to Watch 2016 - Victor Lesser Distinguished Dissertation Award (IFAAMAS) 2014 - Facebook Graduate Fellowship 2013-14 - Hima and Jive Graduate Fellowship 2011 - IIT Bombay President's Gold Medal Shah supervises numerous graduate students across diverse institutions and leads research initiatives at the intersection of technology and society. His lab collaborates with institutions like Carnegie Mellon University and Harvard University, while developing practical applications such as Spliddit.org for fair decision-making in everyday life.

• Artificial Intelligence
• Algorithmic Fairness
• Social Choice Theory
• +3 more

Zhijing Jin is an Assistant Professor at the University of Toronto and a postdoc at the Max Planck Institute for Intelligent Systems, working with Bernhard Schölkopf. She is also a faculty member at the Vector Institute and an ELLIS advisor. Her research focuses on Causal Reasoning with LLMs , Moral Reasoning in LLMs , and AI Safety , with contributions to AI for Science and NLP for Social Good. She leads the Jinesis AI Lab , which explores causal LLMs, multi-agent systems, and ethical AI applications. Education: PhD in Computer Science from Max Planck Institute (Germany) and ETH Zurich (Switzerland) Bachelor’s degree from University of Hong Kong, with visiting semesters at MIT and National Taiwan University Research Interests: Her work bridges causal inference and NLP, addressing robustness, interpretability, and ethical alignment of LLMs. Key projects include Corr2Cause (causal reasoning), GovSim / MoralSim (multi-agent LLMs), and frameworks like NLP4SG for social impact. She advocates for causal mechanisms to tackle AI safety and societal challenges. Recent Articles: Recent work explores political bias in LLMs, ethical dilemmas in multi-agent systems, and causal foundations for trustworthy AI. These studies emphasize real-world applications, such as healthcare NLP and policy analysis. Awards & Recognition: 3 Rising Star Awards 2 Best Paper Awards at NeurIPS 2024 Workshops Fellowships from Open Philanthropy and Future of Life Institute Grants & Mentorship: Funded by NSERC, Schmidt Sciences, and the Cooperative AI Foundation. She mentors ~20 students globally, including PhD candidates in multi-agent LLMs, causal LLMs, and AI safety. The Jinesis Lab offers remote mentorship across institutions like UofT, ETH Zurich, and University of Michigan. Labs & Collaborations: Active collaborations include MPI-IS, Vector Institute, and ETH Zurich’s AI Center. Her lab emphasizes open science, with tools like MoralLens and RouterAttack released publicly.

• Large Language Models (LLMs)
• Natural Language Processing (NLP)
• Causal Inference
• +7 more

Jia Xue is an Associate Professor at the Factor-Inwentash Faculty of Social Work, University of Toronto, with a joint appointment in the Faculty of Information. She joined U of T in 2018 as an Assistant Professor and was promoted in 2025. Her research focuses on computational approaches to social justice issues, including intimate partner violence, rape myth culture, and AI ethics. She holds a Ph.D. from the University of Pennsylvania, a law degree from Tsinghua University, and postdoctoral training at Harvard University. Her research lab, the Artificial Intelligence for Justice (AIJ) Lab, develops AI tools to study social ills like image-based sexual abuse and school bullying. Key projects include AI-powered chatbots for sexual violence victims and analyzing biases in algorithmic systems. Funded by grants from Connaught, SSHRC, and CIHR, her work bridges social work, computer science, and policy analysis. Jia has authored over 50 peer-reviewed articles in journals like Journal of Interpersonal Violence and Child Abuse & Neglect . Awards include the Ontario Early Researcher Award (2025) and Deborah K. Padgett Early Career Achievement Award (2025). She leads initiatives at the Schwartz Reisman Institute for Technology and Society, focusing on tech’s societal impacts. Teaching emphasizes ethical big data use in social justice contexts. Recent work includes pandemic-related social media analysis and developing interventions for marginalized populations. Her interdisciplinary approach combines machine learning, policy analysis, and qualitative research to address systemic inequities.

• Intimate Partner Violence
• Sexual Assault
• AI Ethics
• +6 more

Amine Trabelsi serves as an Assistant Professor in the Department of Computer Science at the University of Sherbrooke, specializing in socially impactful AI research. His work bridges technical NLP innovations with real-world applications in public health, climate action, and social behavior analysis, with direct relevance to Canadian policy contexts. His educational background includes: M. Sc. in Computer Science from Université de Montréal Ph. D. in Computing Science from the University of Alberta Research focuses on unsupervised analysis of unstructured text, detection of dubious content in social media, antisocial behavior identification, and emotion recognition in conversational systems. Current projects address brain tumor data capture for cancer registries, corporate alignment with clean energy transitions, and climate empowerment through community network mapping in Canada. His methodology emphasizes explainable AI systems for social good applications, particularly in multilingual Canadian contexts. Dr. Trabelsi maintains active industry engagement through Twitter and LinkedIn, with primary contact via email Amine.Trabelsi@USherbrooke.ca (office D4-1022-1, phone 819-821-8000 ext. 62031). No formal advising or grant structures are detailed in current public profiles.

• Natural Language Processing
• Data Mining
• Artificial Intelligence for Social Good
• +3 more