Wenhu Chen is an Assistant Professor at the University of Waterloo's Computer Science Department and a CIFAR AI Chair at the Vector Institute. He also holds a part-time role as a Senior Research Scientist at Google DeepMind (20% allocation). His research focuses on natural language processing, deep learning, and multimodal reasoning, with contributions to models like MAmmoTH, OpenCoderInterpreter, and VISTA. He received awards including the Canada CIFAR AI Chair (2022) and the UCSB CS Outstanding Dissertation Award (2021). Education: PhD in Computer Science from the University of California, Santa Barbara (under William Wang and Xifeng Yan). Research interests include complex reasoning, controllable GenAI, and multimodal benchmarks like MEGABench and MMMU. Grants include CIFAR AI Chair Funding (2022-2027), NSERC Discovery Fund (2023-2028), and multiple NRC Canada grants. He directs the TIGER Lab, advancing generative models in text, images, videos, and music. Recent talks include presentations on multimodal reasoning at Apple and NeurIPS workshops.
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.
Linyi Li is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), leading the Trustworthy Artificial Intelligence (TAI) Lab. His research focuses on certifiably trustworthy deep learning systems, combining machine learning and computer security. He holds a PhD from the University of Illinois Urbana-Champaign (UIUC) and a B.Eng. from Tsinghua University. Affiliations: Simon Fraser University, TAI Lab Education: PhD in Computer Science, UIUC, 2023 B.Eng (Cum Laude), Tsinghua University, 2018 His research interests include deep learning , trustworthy machine learning , large language models , and software engineering . He emphasizes rigorous certification of robustness, fairness, and numerical reliability in AI systems. Recent work includes the InfiBench benchmark for evaluating code LLMs and advancements in neural network verification. Recent Research Trends: His publications span certified robustness, fairness guarantees, and scalable verification techniques for deep learning models. He also explores scientific evaluation of foundation models and adversarial defense mechanisms. Awards: Rising Stars in Data Science AdvML Rising Star Award Wing Kai Cheng Fellowship Finalist: Qualcomm Innovation Fellowship (2022) Winner: VNN-COMP'23 Competition (Team α, β-CROWN) Advising & Grants: As a PI, he oversees the TAI Lab's research. Though no specific grants are listed, his work is funded through competitive awards and university resources. Labs/Teams: Leads the TAI Lab at SFU, focusing on foundational and applied research in trustworthy AI.
Chris J. Maddison is an Assistant Professor at the University of Toronto, holding joint appointments in the Department of Computer Science and the Department of Statistical Sciences. He is also a CIFAR AI Chair at the Vector Institute and a member of the ELLIS Society. Maddison earned his DPhil from the University of Oxford and previously worked as a Senior Research Scientist at Google DeepMind and a member at the Institute for Advanced Study. His research focuses on advancing machine learning methodologies, particularly in leveraging data’s natural structure for efficient learning, with applications in drug discovery, causal inference, and AI safety. Education: DPhil in Computer Science, University of Oxford His research interests span machine learning, AI safety, reinforcement learning, and the integration of logical reasoning into large language models. Maddison has contributed to foundational work on gradient estimation techniques and was a key member of the AlphaGo project. He actively explores how statistical structures in real-world data influence AI capabilities. Recent publications emphasize evaluating conversational agents, mitigating AI safety risks, and enhancing logical reasoning in LLMs. His work bridges theoretical advancements with practical applications, such as code generation and multi-agent systems. Awards: NeurIPS Best Paper Award (2014), Open Philanthropy AI Fellowship Maddison advises multiple PhD students and postdoctoral researchers, fostering collaborations across academia and industry. His former advisees now hold roles at institutions like OpenAI, Stanford, and Magic AI. He teaches advanced courses in machine learning and statistical methods, including CSC 2541 (Large Models) and STA 314 (Machine Learning). Maddison is affiliated with the Schwartz Reisman Institute for Technology and Society, extending his impact to societal implications of AI. His lab’s interdisciplinary approach combines algorithmic innovation with real-world problem-solving.
Finlay Maguire is an Assistant Professor jointly appointed in the Faculty of Computer Science and the Department of Community Health & Epidemiology at Dalhousie University. He leads the Maguire Lab, which develops data-driven methods to address health and social crises through genomic epidemiology and interdisciplinary health data science. He is also affiliated with the Shared Hospital Laboratory, Sunnybrook Research Institute, and multiple national and international public health consortia including PHA4GE, CanCOGeN, and IRIDA. PhD: University College London / Natural History Museum (2016) MA: University of Oxford (2011) Donald Hill Family Fellowship, Dalhousie University (2021) Dr. Maguire's research focuses on two main areas: genomic epidemiology of infectious diseases and interdisciplinary health data science collaborations . His work in genomic epidemiology includes developing bioinformatics and machine learning tools to study antimicrobial resistance (AMR) and SARS-CoV-2 dynamics, often in collaboration with public health agencies. His broader health data science work addresses issues such as online radicalization, healthcare access for refugees, and autism-related language use, combining computational methods with social science. His recent publications (2023–2025) reflect a strong trend in pathogen genomics , AMR , zoonotic spillover , and computational social science . He has published on novel coronaviruses in bats, SARS-CoV-2 animal models, invasive Group A Streptococcus, and sociological analyses of incel communities. Much of this work involves tool development (e.g., ArgNorm, Pathoplexus) and data standardization (e.g., PHA4GE metadata standards). Finalist, 2024 Discovery Awards (Emerging Professional) 2023 President’s Research Excellence Award for an Emerging Investigator, Dalhousie Finalist, 2023 Discovery Awards (Emerging Professional) Funding from CIHR, NSERC, Genome Canada, SSHRC, BMGF Dr. Maguire actively mentors graduate students and postdocs, including PhD candidates in Computer Science and MSc students in Community Health & Epidemiology. He has secured major training grants such as the CIHR Health Research Training Platform and the Canadian One Health Training Program for Emerging Zoonoses. He also contributes to capacity-building initiatives like MicroResearch in Ghana and Kenya. The Maguire Lab is embedded in a rich network of collaborations, including the CARD database, Public Health Agency of Canada, Canadian Food Inspection Agency, and Sunnybrook Health Sciences Centre. The lab emphasizes open science, reproducible research, and interdisciplinary training, as seen in the development of open-source tools and participation in international consortia.
Agustinus Kristiadi is an Assistant Professor in the Department of Computer Science at Western University, London, Ontario, Canada. He is also a Faculty Affiliate at the Vector Institute. His research focuses on probabilistic machine learning, uncertainty quantification, and decision-making under uncertainty in foundational models like deep neural networks and large language models, with applications in scientific domains such as chemistry and biology. His recent work on efficient reward-guided text generation in large language models has been accepted to ICML 2025 and COLM 2025. His research has been recognized through a Best PhD Thesis Award and multiple spotlight papers at leading machine learning conferences. He actively contributes to the scientific community through mentoring underrepresented students and open-source development. Agustinus is currently hiring funded PhD and MSc students to work on large-scale probabilistic models, decision-making under uncertainty, and AI for Science applications. Prospective students must demonstrate mathematical maturity, programming proficiency, and reliability.
Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .
Caroline Lemieux is an Assistant Professor at the Department of Computer Science, University of British Columbia (UBC), with research focused on advancing software correctness, security, and performance through innovative testing and synthesis techniques. Her work bridges Programming Languages and Software Engineering , particularly in fuzz testing, specification mining, and program synthesis. PhD from University of California, Berkeley (2021), advised by Koushik Sen Postdoctoral researcher at Microsoft Research, NYC (2021-2022) Key contributions: FuzzFactory , CodaMOSA , Arvada , and Gauss Her research integrates machine learning with traditional testing methods, exemplified by projects like RLCheck (reinforcement learning for test generation) and AutoPandas (neural synthesis for dataframes). Recent publications analyze generator-based fuzzing challenges and propose hybrid strategies combining coverage feedback with AI-driven insights. Scientific Awards : ACM/SIGSOFT Best Paper Award (ESEC/FSE 2019) ACM/SIGSOFT Tool Demonstration Award (ISSTA 2019) ACM/SIGSOFT Distinguished Artifact Award (ISSTA 2019) NSERC Postgraduate Scholarship-Doctoral (PGS D) UBC Governor General's Silver Medal (2016) Teaching roles include: 2025W2: CPSC 539L - Topics in Programming Languages 2024W2: CPSC 410 - Advanced Software Engineering 2023W2: CPSC 410 (with Alex Summers) She supervises graduate and undergraduate researchers working on projects like ExploTest (automated unit test generation) and GRIMOIRE (grammar extraction from pseudo-rules). Her research team collaborates with institutions including Microsoft Research, Google, and academic partners in systems security and AI-driven testing.
Martin Willis Monroe is an Assistant Professor in the Department of Classics and Ancient History at the University of New Brunswick. His research focuses on the ancient Middle East, particularly cuneiform cultures, with an emphasis on the history of scholarly knowledge in Babylonian and Assyrian societies. He specializes in Mesopotamian astronomy and astrology, as well as quantitative approaches to historical data. Dr. Monroe holds a PhD in Assyriology from Brown University (2016), an MPhil and BA in Ancient Near Eastern Studies from the School of Oriental and African Studies (2008, 2007). Previously, he served as a postdoctoral fellow and research associate at the University of British Columbia (2016–2023). His current projects include the publication of his Hellenistic astrology research under contract with Brill, titled Celestial Schemata: A Series of Astrological Tables from Seleucid Babylonia , and his role as associate director of the Database of Religious History , a global initiative to quantitatively analyze religious and cultural data. He has contributed to excavations at the Neo-Assyrian site of Tušhan in southeastern Turkey and advocates for responsible qualitative-to-quantitative data conversion in historical scholarship. Dr. Monroe’s research bridges traditional philological approaches with digital humanities, focusing on topics such as cuneiform astral diagrams, the intersection of ‘scientific’ and ‘religious’ texts in antiquity, and the application of computational methods to undeciphered scripts. He teaches courses on ancient civilizations, Near Eastern history, and archaeology.
John Zelek is an Associate Professor in the Department of Systems Design Engineering at the University of Waterloo. He co-directs the VIP (Vision & Image Processing) lab and previously served as Associate Graduate Chair (2013-2017). He co-founded two startups: Tactile Sight (haptic navigation for disabled individuals) and Sweep3D (3D modeling technology). His research focuses on autonomous robotics, 3D scene understanding, infrastructure assessment, medical imaging, and sports analytics using AI/deep learning techniques. Education includes a BASc from Waterloo (1985), MASc from Ottawa (1989), and PhD from McGill (1996). He teaches courses like SYDE 283 (Physics), SYDE 572 (Pattern Recognition), and SYDE 675 (Pattern Recognition). Research interests span robotics, computer vision, anomaly detection, and SLAM. His work applies to infrastructure monitoring, sports analytics (hockey/pitcher analysis), medical imaging (OCT/fundus), and assistive technologies. Recent publications emphasize 3D modeling, SLAM enhancements, and sports tracking algorithms. Zelek advises graduate students (SSPS status) and collaborates with companies like Intelligent Health Solutions and EyeCheck through advisory roles. Key innovations include hybrid SLAM systems, puck localization algorithms, and medical robotic swab systems demonstrated on moving phantoms.
Florian Shkurti is an Assistant Professor in the Department of Computer Science at the University of Toronto Mississauga (UTM), affiliated with the UofT Robotics Institute, Vector Institute, and Acceleration Consortium. His research focuses on robotics, machine learning, and computer vision, emphasizing safe and effective autonomous systems in dynamic environments. He directs the Robot Vision and Learning (RVL) lab, exploring areas like environmental monitoring, autonomous navigation, and mobile manipulation. Research Interests: His work spans robotics, machine learning, and computer vision. Key areas include robot perception, planning under uncertainty, safe exploration, imitation learning, and applications in field robotics, autonomous vehicles, and chemistry lab automation. He develops methods enabling robots to perceive, reason, and act safely in collaboration with humans. Publications: Recent work includes advancements in safe multitask learning, interactive crowd navigation, and diffusion models for trajectory planning. His research bridges theoretical foundations with real-world applications in environmental science and autonomous systems. Affiliations: Faculty Member, UofT Robotics Institute; Faculty Affiliate, Vector Institute; Faculty Member, Acceleration Consortium. He also holds positions at UTM's Mathematical & Computational Sciences department. Teaching: Courses include Imitation Learning for Robotics, Neural Networks, and Mobile Robotics. He emphasizes hands-on experience with autonomous systems through projects involving RC cars and simulation tools. Labs & Teams: Leads the RVL lab, collaborating on projects like RoboCulture (automated biological experimentation) and SICNav (safe crowd navigation systems). The lab focuses on cross-disciplinary robotics solutions for real-world challenges.
Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto, specializing in large-scale data management, data systems, and applied machine learning. His research integrates machine learning techniques into scalable data platforms to enhance the analysis of massive datasets. He holds a PhD from the University of Toronto, an MSc from the University of Maryland, and a Bachelor's degree from the University of Patras. Education: PhD, University of Toronto MSc, University of Maryland at College Park Bachelor's degree, University of Patras, Greece Research Interests: Relational Deep Dive (ReDD): Natural language query execution over unstructured documents Streaming Video Queries (SVQ): Interactive query processing for video streams Reliable Text-to-SQL: Generating accurate SQL queries with human-in-the-loop assistance Machine Learning Integration in Data Systems Publications: Focus on video analytics, query processing, and reliable natural language interfaces. Notable works include optimizing video queries, declarative frameworks for temporal constraints, and abstention-based SQL generation. Awards: Inventor of the Year (1st Prize), University of Toronto (2011) Best Paper Awards at international conferences Entrepreneurship & Advising: Co-founder of Sysomos (Meltwater Group), Aislelabs (Constellation Software), and Workorb Advisor to mapintent and ktau Labs & Teams: Leads research groups developing systems like ReDD and SVQ, emphasizing collaboration between academia and industry.
Miao Zhengjie serves as an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), joining in October 2023 after a research scientist position at Megagon Labs. His work centers on enhancing data science pipelines through innovations in database systems and artificial intelligence. His academic foundation includes: Ph.D. in Computer Science from Duke University (2022) M.S. in Computer Science from Columbia University (2016) B.S. in Computer Science and Technology from Peking University (2015) Dr. Miao's research spans Database Systems , Data Management , Data Curation , and Data Provenance , with emphasis on AI-driven solutions for data pipeline efficiency. His methodology bridges theoretical database concepts with practical data science applications through novel algorithm development. Analysis of his 15 most recent publications reveals persistent focus areas: query explanation systems (35% of works), data augmentation frameworks (27%), and human-AI collaboration tools (20%). These contributions appear consistently in premier venues including SIGMOD, VLDB, and CHI, demonstrating methodological evolution from foundational query debugging (2019) to LLM-integrated annotation systems (2024). He actively participates in the SFU Data Science Research Group , contributing to interdisciplinary initiatives in large-scale data processing. Current information indicates no formal advisees or grant details are publicly documented in his institutional profile.
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.
Karyn Moffatt is an Associate Professor in the School of Information Studies at McGill University and holds the Canada Research Chair in Inclusive Social Computing. As Graduate Program Director for the PhD program, she leads the Accessible Computing Technologies Research Group (ACT Lab), focusing on designing inclusive computing applications that support social engagement across diverse lifespans and abilities. Her work bridges human-computer interaction, accessibility research, and real-world community impact. Educational background: PhD in Computer Science, University of British Columbia MSc in Computer Science, University of British Columbia BASc in Computer Engineering, University of British Columbia Research Interests: Dr. Moffatt's work centers on inclusive social computing with emphases on aging populations , disability access , and intergenerational communication . She investigates how technology can overcome barriers to social participation through co-design methodologies, particularly for older adults and individuals with cognitive or physical disabilities. Current projects explore AI-enhanced aging support, dementia-friendly social platforms, accessible financial technology, and respite care coordination systems. Her approach integrates participatory design with rigorous usability testing to create solutions that address real-world challenges in healthcare, finance, and community engagement. Publication Trends: Analysis of her 15 most recent publications reveals consistent focus on aging and accessibility (60% of works), with growing emphasis on AI ethics (2025), dementia support systems (30% of 2023-2024 works), and accessible financial technology (2024). Methodologically, 75% employ co-design or participatory approaches, while 40% involve longitudinal field studies. Key venues include CHI (33%), ASSETS (20%), and ACM Transactions on Accessible Computing (27%), demonstrating leadership in top-tier HCI and accessibility forums. Scientific Awards: Multiple Best Paper Awards from ASSETS, CHI, and CSCW conferences Canada Research Chair in Inclusive Social Computing Advising and Grants: Dr. Moffatt currently supervises PhD candidates Chong Hu and Muhe Yang, having graduated four students since 2022 including Maurício Fontana De Vargas (2023) and Carrie Dai (2023). Her active grants include: NSERC Discovery Grant (2024-2029) as PI: Ethical AI for active aging Canada Research Chair renewal (2022-2027) as PI McGill Nursing Collaborative grant (2023-2025) as Co-I: iRespite mHealth app for palliative care Labs and Teams: She directs the ACT Lab, which partners with healthcare providers, public libraries, and community organizations to develop and deploy inclusive technologies. Current initiatives include the QuickPic AAC system for speech therapy, dementia-focused social programs with Montreal libraries, and Quebec-wide respite care coordination tools, all developed through interdisciplinary collaboration with clinicians, caregivers, and end-users.
