Find Academic Supervisors

Alfred HermidaView profile

Professor

• Digital Journalism
• Media Innovation
• Social Media Analysis
• +3 more

Alfred Hermida is a Professor at the University of British Columbia (UBC), holding a joint appointment in the School of Journalism, Writing, and Media. He is renowned for his contributions to digital journalism, media innovation, and the societal impact of social media and artificial intelligence. Hermida was honored as a Fellow of the International Communication Association (ICA) in 2024, recognizing his transformative scholarly work in communication studies. His career spans academic research, teaching, and leadership. Before joining UBC in 2006, he spent 16 years as a BBC journalist, including foundational roles in the BBC News website. As Director of UBC’s Graduate School of Journalism from 2015 to 2020, he secured major SSHRC grants, including two $2.5M Partnership Grants. He co-founded The Conversation Canada in 2017, a platform bridging academic research with public discourse. Research focuses on journalism’s evolution amid technological shifts, including the Global Journalism Innovation Lab, a SSHRC-funded initiative examining innovation practices and policy frameworks shaping journalism’s future. His publications, such as Data Journalism and the Regeneration of News (2019) and Tell Everyone (2014), explore digital media’s role in information sharing and verification. Hermida’s teaching spans media innovation, visual journalism, and research methods. Colleagues praise his triple threat: exceptional scholarship, pedagogy, and administrative leadership. His work consistently bridges theory and practice, emphasizing journalism’s societal relevance in an algorithm-driven world.

Richard S. SuttonView profile

Professor

• Machine Learning
• Artificial Intelligence
• Computational Intelligence
• +5 more

Richard S. Sutton is a Professor in the Department of Computing Science at the University of Alberta , a Research Scientist at Keen Technologies, and the Principal Investigator at the Reinforcement Learning and Artificial Intelligence Lab . He also serves as the Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute (Amii) and as Chief Scientific Advisor there. Additionally, he founded the Openmind Research Institute and is Chief Scientific Officer at ExperienceFlow. His research focuses on identifying general computational principles underlying intelligence and goal-directed behavior. He emphasizes the interaction between intelligent agents and their environment , exploring how goals, choices, and information sources emerge from this interaction. His work spans reinforcement learning (RL) , agent-environment dynamics, and ethical considerations in AI, such as robot rights and moral philosophy. His articles include technical works on RL, ethical frameworks, and philosophical essays like The Bitter Lesson (2019) and The Definition of Intelligence (2016). Canada CIFAR AI Chair Co-author of foundational RL textbook Developed Tile Coding Software for RL

David LieView profile

Professor

• Computer Systems Security
• Trusted Execution Environments
• Mobile Platform Security
• +7 more

David Lie is a Professor at the University of Toronto, jointly appointed in the Edward S. Rogers Department of Electrical and Computer Engineering, Department of Computer Science, and Faculty of Law. He directs the Schwartz Reisman Institute for Technology and Society, co-founded the IT3 Lab, and serves as Associate Director at the Data Sciences Institute. His research focuses on securing computer systems through operating systems, architecture, and formal verification approaches. B.A.Sc (University of Toronto, 1998) M.S. (Stanford, 2001) Ph.D. (Stanford, 2004) His research emphasizes building secure systems for mobile platforms and cloud computing, with significant contributions to trusted execution environments (XOM architecture precursor to Intel SGX/ARM TrustZone) and Android permission mapping (PScout tool). Recent work spans cryptographic side-channels, web tracking detection, and AI safety. Key honors include SOSP 2003 Best Paper, Ontario MRI Early Researcher Award (2008), Connaught Global Challenge Award (2017), and Canada Research Chairs (Tier 2 2013-2018, Tier 1 current). He has secured over $30M in research funding and served as General Chair for CCS 2018. Lie leads the IT3 Lab (Technology and Policy Integration), collaborates with industry leaders (Google, VMware, Telus), and mentors graduate students working on practical security implementations. He co-teaches ECE1724: Privacy Problems with Lisa Austin from the Faculty of Law, reflecting his technology-policy interests.

Michael EvansView profile

Professor

• Statistical Computing
• Theoretical Statistics
• Statistical Inference
• +3 more

Professor Michael Evans is a faculty member at the University of Toronto , affiliated with the Department of Statistical Sciences (St. George campus) and the Department of Computer and Mathematical Sciences at the Scarborough Campus . His research focuses on statistical inference , Bayesian methods , and measuring statistical evidence through his work on the relative belief ratio . He has contributed to ROC analysis , linear models , and stochastic processes , with recent work addressing biases in statistical reasoning and connections to frequentist criteria. Research Themes : Defining and measuring statistical evidence Bayesian inference and prior-data conflict resolution Monte Carlo methods and stochastic process theory Recent Publications explore statistical evidence in scientific practice, medical diagnostics, and Bayesian frameworks. His 2024 Encyclopedia article critiques frequentist criteria, while the 2022 Entropy paper introduces relative belief in ROC analysis. Awards : ASA Fellow (American Statistical Association) Teaching : He teaches advanced courses like STAC62 (Probability and Stochastic Processes) and STAC63 (Probability and Stochastic Processes II), emphasizing theoretical rigor and applications in fields like mathematical finance and machine learning .

Trevor GoodyearView profile

Assistant Professor

• Substance Use
• Mental Health
• LGBTQ+ Health
• +3 more

Trevor Goodyear is an Assistant Professor at the University of British Columbia (UBC) Faculty of Applied Science, School of Nursing. His research explores substance use, mental health, and health equity among marginalized populations, particularly 2S/LGBTQ+ youth and people who inject drugs. Education: MSN, MPH, PhD Research Interests: Harm reduction, LGBTQ+ health, mental health, public health, health equity, social determinants of health His recent work examines harm reduction strategies, structural vulnerabilities, and health narratives in urban drug scenes. Publications analyze cannabis use, overdose prevention, conversion practices, and the intersection of gender identity with substance use. His research employs qualitative methods like photovoice and narrative analysis to center marginalized voices. Key trends in his articles include the role of stigma in healthcare, policy implications of substance use research, and the impact of the COVID-19 pandemic on mental health disparities. He advocates for evidence-informed policies and community-driven interventions to address systemic inequities.

Bradley BuchsbaumView profile

Associate Professor

• Cognitive Neuroscience
• Working Memory
• Functional Neuroimaging
• +5 more

Bradley Buchsbaum is an Associate Professor and Senior Scientist at the Rotman Research Institute, Baycrest , Toronto, Canada. His research focuses on cognitive neuroscience, particularly on working memory, episodic memory, and functional neuroimaging using fMRI. PhD in Cognitive Science (2003) from University of California, Irvine BSc in Bio-Psychology (1997) from University of California, Santa Barbara His lab investigates how memories are stored, represented, and reactivated in the brain, combining functional neuroimaging, eye-tracking, and computational modeling. Key areas include multivariate statistics, machine learning applications in neuroimaging, and quantifying memory fidelity through behavioral and neural data. Recent publications emphasize neuroimaging methodology (e.g., MRI data consistency checks), memory pattern completion mechanisms, and feature-specific neural reactivation during episodic recall. Research trends highlight interdisciplinary approaches merging cognitive neuroscience with advanced statistical techniques. Lab team includes post-doctoral fellows (Stephen Rhodes), graduate students (Carolyn Guay, Corey Loo, Michael Bone, Nick Hoang, Ryan Barker), and research assistants. The lab actively develops open-source tools like rMVPA and neuroim2 for fMRI analysis.

Sudhir R. PaulView profile

Professor

• Biostatistics
• Statistical Inference
• Generalized Linear Models
• +3 more

Dr. Sudhir R. Paul is a Professor in the Department of Mathematics and Statistics at the University of Windsor, Faculty of Science. He holds a Ph.D. from Wales and has received prestigious awards including Fellowships from the American Statistical Association (2006) and the Royal Statistical Society (1982). His research focuses on Biostatistics and Statistical Inference, with expertise in areas such as Generalized Linear Models, Clustered/Longitudinal Data Analysis, and Categorical Data Analysis. He has supervised numerous graduate students and maintains an active research program addressing topics like risk difference estimation, bias correction in statistical models, and applications in environmental and medical contexts. Education: Ph.D. (Wales). Research interests span advanced statistical methodologies, including zero-inflated models, measurement error correction, and dose-response modeling. His work bridges theoretical development and practical applications in epidemiology, clinical trials, and environmental studies. His publications reflect contributions to clustered data analysis, interval estimation, and generalized estimating equations. Awards highlight his impact in advancing statistical science through teaching, research, and service. Advising: Over 30 M.Sc. and Ph.D. students have been supervised, with current students engaged in doctoral and master’s research. Postdoctoral fellows include experts in statistical theory and applications.

Azadeh TabibanView profile

Assistant Professor

• Cybersecurity
• Cloud Security
• Network Security
• +4 more

Azadeh Tabiban is an Assistant Professor in the Department of Computer Science at the University of Manitoba, leading the FOCUS research lab. She specializes in cybersecurity with a focus on cloud/edge security, network security, and applying machine learning to security challenges. Her work emphasizes practical solutions for real-world systems, including provenance analysis, forensics, and securing smart grids and 5G networks. Education: PhD from Concordia University (supervised by Prof. Lingyu Wang and Prof. Makan Pourzandi), followed by a postdoctoral fellowship at the University of Waterloo collaborating with Ericsson Montreal. Previously involved in the NSERC/Ericsson IRC in SDN/NFV Security Project. Research Interests: Building scalable security technologies for transparency and trustworthiness in computing systems. Key areas include provenance systems, cloud/NFV security, smart grid cybersecurity, and AI-driven security solutions. Recent projects include URGP-funded work on AI-based intrusion detection and NCC-supported 5G security collaborations with Ericsson and other universities. Awards and Grants: NSERC Discovery Grant (2024), University Research Grants Program (URGP) (2025), National Cybersecurity Consortium (NCC) Grant (2023), and Best Paper Candidate at CNS'20. Active in securing industrial partnerships and government-funded initiatives. Advising and Training: Supervises PhD and MSc students in system security and machine learning applications. Offers funded positions prioritizing underrepresented groups. Mentors undergraduate students interested in programming and practical cybersecurity solutions. Labs/Teams: Leads the FOCUS lab focused on foundational and operational cybersecurity research. Collaborates with industry partners like Ericsson and academic institutions including Waterloo and Concordia.

Jean-François GodboutView profile

Professor

• Data Science
• AI Safety
• Generative AI
• +6 more

Jean-François Godbout is a Professor in the Department of Political Science at the Université de Montréal and an Associate Academic Member of Mila - the Quebec AI Institute. He directs the undergraduate program in Big Data Analytics in Social Sciences and Humanities at UdeM and conducts interdisciplinary research through the Complex Data Lab. Affiliated with IVADO (AI Consortium) Member of CÉRIUM (International Research Centre) and CECD (Democratic Citizenship Centre) His research focuses on: Data Science applications in political institutions AI Safety and generative AI's impact on political attitudes Misinformation Mitigation through large language models Comparative Political Development in Canadian and Lower Canada contexts Legislative Institutions and voting records analysis Political Polarization in online societies Recent publications analyze social media disinformation, AI persuasion on harmful topics, and education-focused text simplification. His articles frequently combine graph mining , machine learning , and political science methodologies. Scientific collaborations include: Mila researchers (Andreea Musulan, Maximilian Puelma Touzel) IVADO data science initiatives McGill University interdisciplinary projects He supervises students in: Political science (Julien Robin, Matthew Taylor) Artificial Intelligence (Kellin Pelrine, Camille Thibault) Computational social science applications

Mark R. GreenstreetView profile

Professor

• Formal Verification
• VLSI Design
• Analog Circuit Verification
• +3 more

Mark R. Greenstreet is a Professor in the Department of Computer Science at the University of British Columbia (UBC). He holds a BSc from Caltech (1981), MA (1988), and PhD (1993) in Computer Science from Princeton University. His primary research focuses on formal verification of analog and mixed-signal (AMS) circuits, VLSI design, and hybrid systems. Notable contributions include the STARI signaling technique, tools like Coho for reachability analysis, and PReach for parallel model checking. He has advised numerous graduate students and collaborators, including Brad Bingham, Chao Yan, and Yan Peng. His work has been recognized with a Best Paper Award at the ASYNC Symposium. Supported by NSERC, Intel, and Oracle, his research bridges theoretical foundations and practical challenges in circuit design and verification. He teaches courses on formal methods, computer architecture, and automata theory at UBC.

• Algorithmic Fairness
• Responsible AI
• Deep Learning
• +5 more

Golnoosh Farnadi is an Assistant Professor at McGill University's School of Computer Science , an Adjunct Professor at Université de Montréal , and a Visiting Faculty Researcher at Google Research . She holds the Canada CIFAR AI Chair and is a core academic member of Mila – Quebec AI Institute . Her research focuses on algorithmic fairness , responsible AI , and optimization . She founded the EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms) to address bias and discrimination in AI systems. Key publications explore fairness in kidney exchange programs, generative model geometry, multilingual LLM de-biasing, and prototype-based recommender systems. Her work bridges causal inference, adversarial robustness, and ethical AI. Google Award for Inclusion Research (2023) Women in AI Awards North America Finalist (2023) Facebook Privacy Enhancing Technologies Award (2021) IVADO Postdoctoral Fellowship (2018–2021) She has supervised over 15 PhD and Master's students, including Prakhar Ganesh (McGill) and William St-Arnaud (Université de Montréal). Her teaching includes Responsible AI and Machine Learning courses at McGill and HEC Montréal.

• Software Engineering
• Human-Computer Interaction
• Information Visualization
• +3 more

Margaret-Anne Storey is a Professor of Computer Science at the University of Victoria and holds the Canada Research Chair Tier I in Human and Social Aspects of Software Engineering. She is affiliated with the Faculty of Engineering and Computer Science and leads the Computer Human Interaction and Software Engineering Lab. Her research focuses on software engineering, human-computer interaction, information visualization, and collaborative work practices. Storey earned her PhD from Simon Fraser University (SFU). Her work bridges socio-technical systems, developer experience (DevEx), and the ethical integration of AI in software engineering. She has pioneered studies on remote work productivity during the pandemic, developer satisfaction, and the human-centered design of tools. Key research interests include understanding developer productivity through frameworks like SPACE (2021), analyzing code review strategies, and exploring the impact of generative AI on software engineering research. Her work often employs mixed-methods approaches and emphasizes empirical validation. Storey has been recognized with the Canada Research Chair Tier I (2020–present). Her contributions span keynote addresses at major conferences (e.g., ICSE), framework development (e.g., DASP for security practices), and interdisciplinary collaborations with organizations like Microsoft. Her research also addresses societal challenges, such as drug-checking technology and participatory culture in education. She advocates for human-centric AI in software engineering and critical questioning of AI’s societal impacts.

• Law and Forensic
• Psychology and Law
• Eyewitness Identification
• +3 more

Ryan Fitzgerald is an Associate Professor (on leave) in the Department of Psychology at Simon Fraser University. His research focuses on law and forensic psychology, particularly eyewitness identification, face recognition, memory, and meta-analysis. He directs the EyeWitnessLab (www.eyewitnesslab.com/) and has contributed to advancing understanding of how social media, lineup procedures, and stress affect eyewitness accuracy. His work bridges psychological science with legal practice through police training initiatives and evidence-based policy reforms. Research Interests: Psychology and Law Eyewitness Identification Mechanisms Face Recognition Technologies Memory Distortion Factors Cross-Cultural Forensic Practices Meta-Analytic Methodology Recent work highlights innovations like the Eyewitness Lineup Identity (ELI) database and augmented reality lineup applications. Fitzgerald emphasizes practical implications of his research, aiming to reduce wrongful convictions through evidence-based practices.

Sudhakar GantiView profile

Associate Professor

• Cloud Computing Resource Management
• Software Defined Networking (SDN)
• Traffic Management and Quality-of-Service
• +3 more

Sudhakar Ganti is an Associate Professor in the Department of Computer Science at the University of Victoria, part of the Faculty of Engineering and Computer Science. He holds a PhD from the University of Ottawa. His research focuses on cloud computing resource management, software-defined networking (SDN), traffic management, quality-of-service optimization, and performance evaluation through queueing theory. His work bridges theoretical frameworks with practical applications in network efficiency and distributed systems. Dr. Ganti’s expertise includes optimizing resource allocation in fog-cloud systems, enhancing telehealth IoT energy efficiency, and developing dynamic defense frameworks for SDN security. His contributions span network traffic prediction, large file transport protocols, and formal verification of networking systems. He has published extensively in top-tier conferences and journals, addressing challenges in distributed computing, cyber security, and edge computing. His research trends emphasize leveraging reinforcement learning for fog-cloud resource allocation, multi-objective optimization in IoT, and SDN-driven network security. Earlier work includes foundational studies on optical router bypass, cloud workload characterization, and conversational agents for smart environments. Despite his prolific output, no academic awards or grants are explicitly mentioned in his profile.

Yizhou ZhangView profile

Assistant Professor

• Programming Languages
• Compiler Design
• Formal Methods
• +3 more

Yizhou Zhang is an Assistant Professor in the Department of Computer Science at the University of Waterloo. He holds a PhD and MS from Cornell University (2019 and 2016) and a BS from Shanghai Jiao Tong University (2012). His research focuses on programming languages, including design, implementation, and theory, with emphasis on formal methods, compiler optimization, and probabilistic programming. Education: PhD, Cornell University, 2019 MS, Cornell University, 2016 BS, Shanghai Jiao Tong University, 2012 Research interests span programming language theory, compiler construction, and formal verification. His work explores topics like certified compilers, effect handlers, and probabilistic program analysis. Recent publications emphasize formal models for memoization, nested family polymorphism, and bidirectional control flow. His publications reflect contributions to probabilistic programming semantics, compiler optimization techniques, and type systems. No scientific awards are explicitly listed. Advising and grant details are not provided in the text. Zhang’s research often intersects with formal methods and practical compiler implementation challenges.