Find Academic Supervisors

• Artificial Intelligence
• Medical Imaging
• Machine Learning
• +5 more

Dr. Alexander Wong is a Professor in the Department of Systems Design Engineering at the University of Waterloo, cross-appointed in other departments. His research focuses on advancing artificial intelligence (AI), machine learning, and computer vision with applications in healthcare, biomedical engineering, and robotics. Key areas include medical imaging technologies like OCT+ERG systems for neurovascular analysis, AI-driven diagnostics (e.g., prostate cancer segmentation), and radar-based monitoring for aging wellness. He leads interdisciplinary projects in health informatics, synthetic data generation, and AI ethics. His work integrates hardware-software systems for real-world challenges, such as non-invasive retinal imaging and in-home gait analysis. Notable contributions include the Cancer-Net framework for cancer detection, the NutritionVerse dataset for dietary monitoring, and the COVID-Net for pandemic response. His research also addresses public health issues like HIV/HCV co-infection and mental health impacts on healthcare utilization in marginalized populations. Dr. Wong's innovations span from foundational AI algorithms to clinical and industrial applications, emphasizing cross-disciplinary collaboration to bridge theoretical advancements with practical solutions.

• Computer Vision
• Deep Learning
• Continual Learning
• +4 more

Eugene Belilovsky is an Associate Professor at the University of Montreal's Department of Computer Science and Operational Research and an Assistant Professor at Concordia University's Department of Computer Science and Software Engineering. He is also an Associate Member of Mila – Quebec Institute for Artificial Intelligence. His research focuses on computer vision, deep learning, and their applications in areas like continual learning and few-shot learning at the intersection of vision and natural language processing. Belilovsky's expertise includes distributed systems, federated learning, and optimization. His work addresses challenges in model generalization, spurious correlations, and efficient training strategies. He has advised numerous graduate students, including Charles-Étienne Joseph, Medric B. Djeafea Sonwa, Gwendolyne Legate, and Irene Tenison. His recent publications highlight contributions to federated learning, continual pre-training, and fairness in AI systems. Notable work includes optimizing distributed learning protocols and mitigating forgetting in evolving data streams. Belilovsky's research also explores clinical applications, such as diagnosing hepatic steatosis using ultrasound imaging through deep learning techniques. He collaborates with institutions like Mila and the DIRO department, advancing interdisciplinary projects in AI-driven healthcare, robotics, and language modeling.

Wei WangView profile

Assistant Professor

• Leadership
• Business Ethics
• Organizational Behavior
• +3 more

Wei Wang is an Assistant Professor and The Associates Fellow in Leadership and Ethics at the Business Administration Department, Asper School of Business, University of Manitoba. His research focuses on leadership dynamics and ethics in the workplace, employing mixed-method approaches. He has been recognized with the 2019 Society of Business Ethics Emerging Scholar Award and the 2022 Anne Tsui Dare to Care Research Award for impactful work on employee well-being. His teaching expertise spans organizational behavior, negotiation, and business ethics. He leads the GLOW Lab, advancing research in leadership and ethical workplace practices. Key research interests include ethical leadership, workplace mistreatment, and the behavioral consequences of deviant coworker behaviors. His work has been published in leading journals such as Journal of Applied Psychology and Journal of Management . Beyond academia, he enjoys outdoor activities, running, and travel. Awards include the 2019 Emerging Scholar Award for contributions to business ethics and the 2022 Dare to Care Award for research prioritizing employee welfare. His lab, the GLOW Lab, drives innovative studies in leadership and ethical organizational practices.

Fangju WangView profile

Professor

• Machine Learning
• Artificial Intelligence
• Intelligent Systems
• +7 more

Fangju Wang is a retired Professor with expertise in artificial intelligence, machine learning, and intelligent systems. His research focuses on applying partially observable Markov decision processes (POMDP) and reinforcement learning to develop efficient algorithms for intelligent tutoring systems (ITS), emphasizing computing cost reduction and uncertainty management. He has contributed to interdisciplinary computational science education through collaborative academic program development. Key research interests include algorithm optimization, policy tree efficiency, Bellman equation solutions in POMDP frameworks, and adaptive teaching strategies in educational technology. His work bridges theoretical advancements with practical applications in speech recognition and user behavior modeling in dialogue systems. No academic awards, grants, or advising roles are explicitly documented in the provided materials. His publications span conferences like CSEDU and journals such as International Journal of Information and Education Technology, focusing on computational methods in education and AI.

Fred LiuView profile

Assistant Professor

• Asset pricing
• Big data in finance
• Financial econometrics
• +2 more

Fred Liu is an Assistant Professor at the Department of Economics & Finance within the Gordon S. Lang School of Business and Economics at the University of Guelph. His research focuses on the intersection of financial econometrics, machine learning, and quantitative finance. Dr. Liu's work emphasizes applications in asset pricing, risk management, and big data analytics in financial markets. Key research themes include cryptocurrency market dynamics, high-frequency trading strategies, algorithmic methods for market predictability, and regulatory capital frameworks. He employs advanced machine learning techniques such as deep learning and quantile regression to solve complex financial problems. His publications address critical topics like intraday market behavior, executive communication analysis via AI, and Basel III regulatory impacts on risk models. While no specific awards are listed, his active publication record reflects engagement with cutting-edge financial research. Advising and grant details are not explicitly provided in the available text. Dr. Liu's work contributes to both academic discourse and practical applications in financial risk management and quantitative strategies.

Wuyang ChenView profile

Assistant Professor

• Deep Learning
• Scientific Machine Learning
• Computer Vision
• +7 more

Wuyang Chen is an Assistant Professor at the School of Computing Science, Simon Fraser University. He holds a Ph.D. in Electrical and Computer Engineering from The University of Texas at Austin (2023). His research focuses on integrating AI with physics, developing principled deep learning methods, and advancing areas like scientific machine learning (SciML), computer vision, and foundation models. Notable contributions include PDE-Controller for LLM-based scientific reasoning and neural operators for fluid reconstruction. He has received prestigious awards, including the INNS Doctoral Dissertation Award (2024) and the NSERC Discovery Grant. Dr. Chen serves as an Area Chair for NeurIPS 2025 and ICML 2025. His lab, DeLTA, emphasizes AI applications in science and hosts active collaborations. He teaches CMPT 419/983 (Special Topics in AI) and mentors students in research areas like model efficiency and self-supervised learning. Education: Ph.D., Electrical and Computer Engineering, The University of Texas at Austin (2023) Recent Awards: INNS 2024 Doctoral Dissertation Award, iSchools Doctoral Dissertation Award 2024, Google GCP Credit Award Key Activities: Organized NeurIPS 2024 workshop on Foundation Models for Science; presented at AAAI 2025, SIAM CSE25, and Berkeley BLISS seminar

Pooya Moradian ZadehView profile

Professor

• Artificial Intelligence
• Public Health
• Social Network Analysis
• +4 more

Prof. Pooya Moradian Zadeh is a Professor in the School of Computer Science at the University of Windsor, specializing in Artificial Intelligence and its applications in public health, social networks, and pandemic response. His research focuses on leveraging AI to address challenges like social isolation, disease surveillance, and healthcare accessibility. He has led interdisciplinary projects such as the $15 million INSPIRE initiative for pandemic preparedness and contributed to the WE-Spark Health Institute's COVID-19 Dashboard . Recognized for his teaching excellence, he received the 2024 Alumni Association Distinguished Teaching Award . His work involves collaborations with institutions like the Great Lakes Institute for Environmental Research and WE-Spark, addressing topics ranging from recommender systems to compassionate community approaches for vulnerable populations. He mentors students like Saghi Khani and Kameswara Peddada, guiding projects such as Simplified SCO on Wheels , which improves retail checkout processes. Research & Grants : Prof. Zadeh has secured grants totaling over $15M for pandemic response, AI-driven health solutions, and social network analysis. His teams develop algorithms for social isolation detection, personalized healthcare recommendations, and agent-based models for public health policy evaluation. He is also involved in advancing conversational AI for healthcare accessibility and optimizing team formation in digital networks. Labs & Collaborations : He leads projects at the University of Windsor’s 300 Ouellette Ave. campus and collaborates with industry and academic partners to bridge AI innovation with real-world health challenges. His work emphasizes ethical AI practices and community-focused solutions.

• Natural Language Processing
• Machine Learning
• Autobiographical Memory
• +2 more

Claudia Morales Valiente is a Researcher at the University of Alberta working in the Memory for Events Lab, where she applies computational methodologies to investigate autobiographical memory systems through linguistic analysis of personal narratives. Her academic foundation includes: Bachelor of Science in Psychology from the University of Havana, Cuba Master of Science in Psychology from the University of Western Ontario, Canada Doctor of Philosophy in Psychology from the University of Western Ontario, Canada under Dr. Ken McRae Dr. Morales Valiente's research integrates natural language processing with cognitive psychology to decode narrative structures in autobiographical accounts. She specializes in supervised machine learning algorithms, mixed-effects modeling, and hierarchical statistical frameworks to quantify memory representation patterns. Her methodological innovations bridge computational linguistics and experimental psychology, particularly in extracting semantic features from life-story narratives to understand memory organization and retrieval mechanisms. Active in the Memory for Events Lab, she develops open-source analytical tools including lexical property extractors and NLP pipelines for narrative scoring, as evidenced by her GitHub repositories which implement R and Python frameworks for text analysis. Her technical contributions focus on adapting sentiment analysis and word norm databases to psychological research contexts.

Ryan NicholsView profile

Professor

• Biocultural Evolution
• Interdisciplinary Methodology
• 18th-Century European Philosophy
• +3 more

Ryan Nichols is a Professor of Philosophy specializing in the biocultural evolution of China, interdisciplinary methods, and 18th-century European philosophy. His research integrates perspectives from philosophy, psychology, data modeling, and classical Chinese studies. Key areas of interest include cultural transmission mechanisms, the psychology of religion, and experimental approaches to philosophical questions. Collaborations involve psychologists, data scientists, and experts in classical Chinese texts. His work explores how Confucian heritage influenced parent-child dynamics and shaped East Asian moral frameworks, leveraging computational tools for textual analysis. Recent projects include modeling cultural evolution through supernatural beliefs and re-evaluating historical philosophical debates using quantitative methods. Ryan's publications span evolutionary psychological science, religion-brain-behavior studies, and interdisciplinary metaphilosophy. He advocates for integrating empirical methods into philosophical inquiry, challenging traditional armchair approaches. His research bridges cognitive science, intellectual history, and literary analysis to address 'big questions' in new interdisciplinary ways.

• Authentication
• Brain-Computer/Machine Interface
• Cybersecurity
• +1 more

Dr. Garima Bajwa is an Assistant Professor in the Department of Computer Science at Lakehead University, where she joined in January 2021. Her research focuses on authentication systems, brain-computer/machine interfaces, cybersecurity, and machine learning applications. She holds a Ph.D. in Computer Science and Engineering from the University of North Texas (2016), an MEng in Electrical and Computer Engineering from the University of Waterloo (2011), and a B.Tech in Electronics and Communication Engineering from Mody Institute of Technology & Science, India (2009). Education: Ph.D., Computer Science & Engineering, University of North Texas (2016) MEng, Electrical & Computer Engineering, University of Waterloo (2011) B.Tech, Electronics & Communication Engineering, Mody Institute (2009) Her research interests span cybersecurity frameworks for neurotechnology, explainable AI in healthcare diagnostics, and multimodal fusion of EEG and image data. Recent work includes EEG signal anonymization, federated learning for medical diagnosis, and reproducibility in BCI research. Her publications explore cutting-edge topics like BCI standardization, driver distraction detection, and quantum computing integration. Grants and lab affiliations are not explicitly mentioned in the provided text.

• Artificial Intelligence
• Network Science
• Computational Biology
• +3 more

Yong Gao is a Professor of Computer Science, Data Science, and Mathematics at the University of British Columbia (UBC) Okanagan, affiliated with the Irving K. Barber Faculty of Science. He holds a PhD from the University of Alberta and leads research in algorithmic and computational problems in artificial intelligence, network science, and computational biology. His work emphasizes graph theory, probabilistic methods, and applications in social media and biological systems. Educational Background : PhD in Computer Science, University of Alberta Research Interests : Algorithmic foundations of AI and network science Graph-based methods for computational biology and social media analysis Probabilistic modeling of complex systems Awards & Grants : Recipient of multiple NSERC Discovery Grants (2006–2019) UBC Okanagan Startup Grant (2005–2008) Senior Member, Association for the Advancement of AI (AAAI) Professional Roles : Member, Centre for Optimization, Convex Analysis and Nonsmooth Analysis Graduate student supervisor Teaching : Courses in algorithm design, artificial intelligence, discrete mathematics, and network science.

Kelsey AllenView profile

Assistant Professor

• Cognitive Science
• Machine Learning
• Robotics
• +4 more

Kelsey Allen is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC) and a Senior Research Scientist at DeepMind. Her research bridges cognitive science, machine learning, and robotics, focusing on understanding and replicating human-like problem-solving, tool use, and physical reasoning. She holds a PhD from MIT (2016) under Josh Tenenbaum and a B.Sc. in Physics from UBC (2010). Research Interests : Allen investigates computational mechanisms underlying human complex behaviors, particularly tool use and design. Her work emphasizes endowing machines with flexible problem-solving abilities. Key themes include lifelong learning, embodied cognition, and integrating symbolic and neural approaches. Awards & Recognition Best Paper Award at Robotics: Science and Systems (RSS) 2018 Oral Presentation at Cognitive Science Society 2019 Spotlight at NeurIPS 2018 and ICLR 2019 Key Projects : Includes developing graph network simulators for rigid body dynamics, tools for physical design optimization, and studies on human tool-use learning. Her work often combines empirical experiments with machine learning models to bridge human and artificial intelligence.

Peter WestView profile

Assistant Professor

• Natural Language Processing
• Artificial Intelligence
• Large Language Models
• +3 more

Peter West is an incoming Assistant Professor at the University of British Columbia (UBC) Computer Science Department, specializing in Natural Language Processing (NLP) and AI. His research focuses on understanding the capabilities and limitations of large language models (LLMs) and generative AI systems, emphasizing their divergence from human intuition and alignment challenges. He holds a PhD from the University of Washington (2024), supervised by Yejin Choi, and a BSc (Honours Computer Science) from UBC (2017). His work has been recognized with awards including Best Method Paper at NAACL 2022 and Outstanding Paper awards at ACL 2023 and EMNLP 2023. He conducted internships at the Allen Institute for AI and Microsoft Research’s NLP group. Research interests include analyzing LLM behavior through a natural sciences lens, exploring model capabilities versus human expectations, and developing decoding algorithms to infuse models with algorithmic logic. His recent publications address generative AI paradoxes, constrained text generation, and symbolic knowledge distillation. He serves on panels for NeurIPS workshops and is beginning a postdoc at Stanford with Chris Potts. His research group at UBC seeks students interested in generative AI’s analytical frontiers.

Osmar ZaianeView profile

Professor

• Data Mining
• Machine Learning
• Social Network Analysis
• +14 more

Osmar Zaiane is a Professor in the Department of Computing Science at the University of Alberta's Faculty of Science. With over 20 years of service at the university, he has established himself as a leading researcher in data mining and machine learning with applications across multiple domains. His educational background includes a Ph.D. in Computer Science from Simon Fraser University (1999), a Master's in Computer Science from Laval University (1992), a Master's in Electronics from Institut National des Sciences et Techniques Nucléaires and Paris XI University (1989), and a Bachelor's in Computing Science from Institut Supérieur de Gestion, Université de Tunis (1988). Zaiane's research focuses on discovering patterns in large complex datasets with practical applications. His primary research interests include data mining, machine learning, social network analysis (particularly community mining and link prediction), and precision health applications. He has made significant contributions to associative classifiers, class imbalance learning, explainable AI, and educational data mining. His work spans multiple application domains including healthcare (particularly for Alzheimer's disease prediction, diabetic retinopathy grading, and lung cancer detection), social media analysis, and natural language processing. His publication record shows a strong focus on medical AI applications, with numerous recent papers on medical image segmentation, brain network analysis, and diagnostic systems. His work increasingly integrates large language models and transformer architectures with traditional machine learning approaches. Best Paper Award at IEEE/ACM Int. Conf. on Advances in Social Network Analysis and Mining (2023) Best Paper Award at International Symposium on Foundations and Applications of Big Data Analytics (2022) Best Paper Award at International AAAI Conference on Web and Social Media (2019) Best Paper Award at 29th International Conference on Database and Expert Systems Applications (DEXA) (2018) Zaiane has supervised over 80 graduate students during his career at the University of Alberta. His research has been supported by numerous grants, particularly in the areas of precision health and educational data mining. He maintains an active research lab focusing on applied machine learning with strong industry and healthcare partnerships. His current work explores the intersection of traditional machine learning techniques with emerging large language models and vision-language models for healthcare applications.

• Human-Robot Interaction
• Haptics
• Virtual Reality
• +6 more

Matthew Pan is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen's University and a core member of the Ingenuity Labs Research Institute. He leads the Machine Intelligence and Human-Robot Interaction Laboratory (MItHRIL), focusing on human-robot interaction, haptics, and virtual/augmented reality technologies for practical applications in assistive robotics and entertainment. His educational background includes: PhD and MASc in Mechanical Engineering, University of British Columbia BASc in Mechatronics Engineering, University of Waterloo Dr. Pan's research centers on human-robot interaction , haptics , and virtual/augmented reality , with applications in assistive technologies and human augmentation. His work explores affective computing , psychophysiological responses , and haptic feedback systems to enhance natural human-robot collaboration. Key projects include robotic handover systems, mixed reality interfaces, and social robotics for Disney Parks. Analysis of his 15 most recent publications reveals a strong focus on robotic handovers , human search/approach , and haptic proxies in virtual reality . His work integrates machine learning for adaptive robot behaviors and studies social cues (gaze, touch, gestures) to improve interaction fluency. Trends indicate growing emphasis on real-world deployment of social robotics in assistive and entertainment contexts. Dr. Pan actively advises graduate students in the MItHRIL laboratory and has led industry projects at Disney Research, developing animatronic technologies and interactive guest experiences for Walt Disney Imagineering and Disney Parks. The MItHRIL laboratory serves as an interdisciplinary hub for robotics research, combining expertise in mechanical engineering, computer science, and human factors to advance intelligent systems for human augmentation and collaborative robotics.