Find Academic Supervisors

• Statistical Foundations of Data Science
• Large Language Models
• Distribution Shift & Transfer Learning
• +4 more

Subha Maity is an Assistant Professor in the Department of Statistics & Actuarial Science at the University of Waterloo. Their research focuses on statistical foundations of data science, including large language models, distribution shift, performative prediction, fairness in ML, and missing data mechanisms. They hold a PhD from the University of Michigan (advised by Prof. Yuekai Sun and Prof. Moulinath Banerjee) and completed undergraduate/masters studies at the Indian Statistical Institute, Kolkata. Education: PhD in Statistics, University of Michigan Master's & Undergraduate, Indian Statistical Institute, Kolkata (Mathematics/Statistics) Recent work includes CARROT router for LLM optimization (arXiv 2024), AISTATS 2025 papers on strategic prediction and performative prediction, and NeurIPS 2024 contributions on weak supervision evaluation. Teaching includes computational statistics (Winter 2025) and undergraduate statistics courses since 2018. Labs/Teams: Active in developing benchmarks like SPROUT dataset and releasing models via HuggingFace community contributions.

Meiyappan NagappanView profile

Associate Professor

• Empirical Software Engineering
• Mining Software Repositories
• Software Security
• +3 more

Academic Profile Dr. Meiyappan (Mei) Nagappan is an Associate Professor at the University of Waterloo's Cheriton School of Computer Science. He holds a Ph.D. (2011) and M.Sc. (2008) from North Carolina State University, and a B.Sc. from Anna University, India (2006). Research Focus His research spans empirical software engineering, mining software repositories, and mobile app store analysis. Current investigations focus on software security, static analysis techniques, and diversity in software engineering. Recent work critically examines AI programming assistants like GitHub Copilot, evaluating their performance, security implications, and impact on developer workflows. Key Research Areas Diversity analysis in open-source contributions and academic publishing Vulnerability detection using machine learning Bug localization and automated debugging Cross-platform app analysis Publications Research demonstrates strong focus on empirical validation of developer tools, with recent emphasis on AI-assisted programming. Work appears in top software engineering venues covering security, testing, and developer productivity.

• Computer Science
• Telemedicine
• E-Learning
• +2 more

Carlos Lucena is an Adjunct Professor at the Cheriton School of Computer Science, University of Waterloo. His work focuses on the intersection of technology and healthcare, particularly in telemedicine, e-learning systems, and human-computer interaction for medical applications. His research spans healthcare informatics, collaborative learning platforms, and accessible e-learning design. Key areas include developing mobile health applications for clinical monitoring, designing telemedicine training systems, and implementing usability frameworks for medical software. A review of recent publications shows strong emphasis on practical healthcare technology implementations, with recurring themes in telemedicine infrastructure, mobile health solutions, and collaborative learning systems. The work demonstrates consistent application of user-centered design principles across medical and educational contexts.

Shane McIntoshView profile

Associate Professor

• Empirical Software Engineering
• Software Delivery
• Release Engineering
• +2 more

Shane McIntosh is an Associate Professor and Associate Director of Graduate Studies at the Cheriton School of Computer Science, University of Waterloo. He holds a Ph.D. from Queen's University (2015) and specializes in empirical software engineering, software delivery processes, and software quality improvement through data mining techniques. His research focuses on analyzing historical and operational data from large-scale software systems to derive actionable insights. Key areas include release engineering, DevOps optimization, and software quality enhancement. McIntosh employs empirical methods to study build systems, continuous integration practices, and defect prediction. McIntosh's recent publications demonstrate a strong focus on improving software development workflows through empirical analysis of build systems, CI/CD pipelines, and code quality metrics. His work frequently addresses efficiency optimization in development operations and vulnerability detection in machine learning systems.

Tamer ÖzsuView profile

Professor

• Distributed Data Management
• Graph Databases
• RDF Systems
• +2 more

Tamer Özsu is a University Professor at the David R. Cheriton School of Computer Science, University of Waterloo, where he previously served as Director (2007-2010) and currently holds the role of Associate Dean of Research. He is also a Distinguished Visiting Professor at Tsinghua University. Professor Özsu's research spans distributed data management, graph/RDF systems, and database fundamentals. His current work focuses on: Distributed graph processing algorithms SPARQL query optimization for RDF systems Streaming graph analytics Indexing techniques for modern hardware Distributed database architectures He has authored the seminal textbook Principles of Distributed Database Systems and co-edited the Encyclopedia of Database Systems . As founding Editor-in-Chief of ACM Books, he has shaped computing literature. His recent publications demonstrate continued innovation in graph partitioning, streaming graph algorithms, and scalable RDF processing. Özsu maintains active research leadership in database systems with over 30 years of influential contributions.

• Sustainable Operations Management
• Business Data Analytics
• Game Theory
• +7 more

Stan Dimitrov is a Professor in the Department of Management Sciences at the University of Waterloo, Canada, and Director of the Business Data Analytics Lab. He holds a PhD in Industrial and Operations Engineering from the University of Michigan (2010). His research focuses on the intersection of operations research and information systems, with expertise in sustainable operations management, business analytics, mechanism design, prediction markets, game theory, nonlinear optimization, and network design. He has secured funding from NSERC, SSHRC, Mitacs, and the University of Waterloo. Education: PhD in Industrial and Operations Engineering, University of Michigan (2010) MEng in Industrial and Operations Engineering, University of Michigan (2006) BSc in Computer Science, University of Michigan (2004) Research interests include addressing industry challenges in pricing, process improvement, and customer relationship management. His work spans theoretical contributions (e.g., game theory, optimization) and applied domains such as wildfire management, circular economy policies, and mental health impacts of digital financial tools. Recent studies analyze pandemic-driven consumer behavior shifts and the role of AI in dynamic pricing. He has received prestigious awards including the 2021 Canadian Operational Research Society Eldon Gunn Service Award and the 2018 Faculty of Engineering Distinguished Performance Award. His teaching portfolio includes advanced courses on scheduling, game theory, and data analytics, with a focus on graduate and professional development programs. Advising and grants: Dimitrov actively supervises graduate students and holds Sole-Supervisory Privilege Status at Waterloo. His funded projects investigate topics like wildfire budgeting, circular economy subsidies, and eco-innovation licensing. Labs/Teams: Leads the Business Data Analytics Lab, which develops data-driven solutions for organizational challenges. Collaborates on interdisciplinary initiatives in sustainable operations and emergency network resilience.

Rob DuimeringView profile

Associate Professor

• Management Engineering
• Organizational Behavior
• Technology Management
• +3 more

Rob Duimering is an Associate Professor at the Department of Management Science and Engineering, University of Waterloo. His affiliations include the Organization and Human Behaviour group, Management of Technology, and Management of Innovation. His research focuses on organizational behavior, engineering design methodologies, systems engineering, and decision-making processes. He explores topics such as design trade-offs, sustainable design education, and the interplay between technology and organizational structures. His research interests extend to cognitive processes in problem-solving, social identity dynamics, and the application of mathematical models (e.g., set theory) to engineering challenges. His recent work highlights innovations in neural network models for optimization and robust training in power systems. Duimering also contributes to experimental approaches in understanding plan-action relationships and conceptual combination theories. His publications span over two decades, with a focus on advancing theoretical frameworks while addressing practical challenges in management systems, innovation, and technology adoption. Notable trends include interdisciplinary collaborations between engineering, organizational behavior, and cognitive science. No scientific awards are explicitly mentioned in the provided materials. His advising and grant activities are not detailed here, though his research often involves student contributions, as seen in co-authored works like the 2023 AC-OPF paper. He is affiliated with teams exploring design education, sustainable systems, and information quality frameworks.

Oliver SchneiderView profile

Associate Professor

• Haptic Technology
• User Experience (UX) Design
• Digital Fabrication
• +5 more

Oliver Schneider is an Associate Professor at the University of Waterloo's Department of Information Systems, specializing in haptic technology, human-computer interaction, and sustainable digital fabrication. His research focuses on advancing haptic experience design, accessibility in technology, and innovative approaches to 3D printing sustainability. Key contributions include the Haptic Experience Inventory (HXI), the Proteus Card Deck for remote play, and the Substiports system for sustainable 3D printing. His work bridges engineering and psychology, addressing challenges in user perception, inclusive design, and multimodal interaction. Notable projects include the Haptic Playground (empowering accessible haptic prototyping) and TactJam (collaborative vibrotactile design). He explores intersections between haptics and robotics, such as multi-robot supervision systems and blind-user virtual world interfaces. Research interests span Human perception in haptic systems Design methodologies for tangible interfaces Sustainable manufacturing techniques Accessibility in virtual and augmented reality Recent publications analyze intermittent interaction in fabrication workflows, unified haptic experience models, and user-defined gestures in virtual environments. Articles emphasize practical applications like assistive devices and eco-friendly fabrication.

Mark SmuckerView profile

Professor

• Information Retrieval
• Search Engines
• Natural Language Processing
• +3 more

Mark Smucker is a Professor at the University of Waterloo, specializing in Information Retrieval and Search Engine Technology. His primary research focuses on evaluating search systems, combating health misinformation, and understanding user behavior in search contexts. He has been actively involved in TREC tracks since 2019, contributing to tracks like Health Misinformation, Decision, and Lateral Reading. His work emphasizes developing robust evaluation measures and preference judgment tools. Research interests include: Advanced evaluation metrics for search systems User interaction and behavior analysis Health misinformation detection and mitigation Design of preference-based ranking algorithms Optimization of high-recall retrieval systems Recent work highlights innovative approaches to: Expanding benchmark datasets for evaluation Visualizing gaze patterns in search interactions Reducing misinformation through trustworthy web source learning Simulating user behavior for predictive system evaluation His contributions to TREC tracks demonstrate leadership in collaborative research. Though no academic awards are listed, his extensive publication record reflects significant scholarly impact.

Hong ZhangView profile

Assistant Professor

• Large-scale data analytics
• Distributed ML training and serving systems
• Application and network scheduling
• +5 more

Hong Zhang is an Assistant Professor at the University of Waterloo, specializing in large-scale data analytics, distributed machine learning systems, and cloud computing. His research focuses on advancing robust and efficient methods for modern AI systems, including topics like adversarial robustness, zero-knowledge proofs for deep learning, and scalable inference techniques for large language models. He holds a Ph.D. in Computer Science from the Hong Kong University of Science and Technology and dual bachelor's and master's degrees in Electronics and Communications Engineering from Huazhong University of Science and Technology. Education: Ph.D., Computer Science and Engineering, Hong Kong University of Science and Technology (2019) B.Sc. and M.Sc., Electronics and Communications Engineering, Huazhong University of Science and Technology, China (2013) Research Interests: Hong's work bridges theoretical foundations and practical applications in distributed ML systems, data center networking, and secure AI. Key areas include optimizing large-scale training/serve pipelines, mitigating adversarial vulnerabilities, and developing privacy-preserving techniques like zero-knowledge proofs for deep learning. His contributions span both algorithmic innovations and real-world system implementations. Publications Trends: Recent work emphasizes scalable inference acceleration (e.g., Eagle series), trustworthy AI (e.g., generative model guidelines), and cryptographic safeguards for ML (e.g., zkdl/zkllm frameworks). He frequently explores intersections between security, efficiency, and scalability in modern AI systems. Awards/Grants: No specific awards or grants mentioned in available texts. Advising & Labs: While student names aren't listed, his research group likely focuses on distributed systems and secure ML. Active collaborations likely involve cloud computing and adversarial machine learning domains.

• Cold Spray Technology
• Additive Manufacturing
• Materials Durability
• +5 more

Hamid Jahed is a Professor and University Research Chair in Cold Spray Technology at the University of Waterloo's Mechanical and Mechatronics Engineering Department. He directs the Fatigue and Stress Analysis Laboratory (FATSLab), focusing on cold spray technology, materials durability, and structural life enhancement. His research spans additive manufacturing, residual stress analysis, and EV battery durability, with strong industry partnerships. He has received numerous awards, including the International Magnesium Association 2021 Award of Excellence and multiple University of Waterloo distinctions. Education: PhD (1997, University of Waterloo), MEng (1982, University of Houston), BEng (1981, University of Houston). Research interests include cold spray applications (coatings, 3D printing), fracture mechanics, digital twins, and magnesium alloy processing. Recent work explores hydrophilic Teflon coatings, fatigue behavior of additively manufactured alloys, and antimicrobial surface treatments. He supervises 11 graduate students and has trained 100+ researchers. Awards highlight his teaching and research excellence, including the Sandford Fleming Teaching Award and multiple Distinguished Performance Awards. Current projects involve EV battery durability and low-carbon manufacturing innovations. FATSLab investigates fatigue properties of materials under multiaxial loads, residual stress measurement, and cost-effective magnesium alloy fabrication. He collaborates with automotive OEMs and Tier 1 suppliers on large-scale industry projects.

• Compiler Testing
• Program Analysis
• Static Binary Taint Analysis
• +5 more

Chen Sun is an Adjunct Assistant Professor at the Department of Data and Systems Engineering, The University of Hong Kong. His research focuses on compiler testing, program analysis, static binary taint analysis, and software engineering methodologies. Notable areas include leveraging large language models (LLMs) for compiler testing baselines, probabilistic delta debugging, and enhancing program reduction techniques across multiple languages. Recent work addresses ransomware defense mechanisms, fuzzing countermeasures, and database management system (DBMS) bug discovery through configuration transformations. Research interests span compiler optimization, fault localization, cybersecurity, and algorithmic efficiency. His publications from 2023-2025 highlight trends in combining AI with traditional software engineering challenges, such as improving type inference for Java code and developing syntax-guided program reduction frameworks. Articles frequently address practical security concerns like protecting embedded devices from protocol fuzzing and mitigating memory leaks through sanitizer-based localization. No scientific awards are explicitly mentioned. Advising and grants sections remain underdeveloped in available data. Current affiliations emphasize academic-industrial collaboration through adjunct roles focused on cutting-edge software reliability and security research.

• Vision Science
• Neurophysiology
• Perceptual-Cognitive Training
• +4 more

Prof. Jocelyn Faubert is a Full Professor at the Université de Montréal's School of Optometry, holding the NSERC/Essilor Industrial Research Chair in Visual Function. His research focuses on perceptual and sensory aspects of vision, especially in dynamic environments involving motion, texture, and 3D tracking. He pioneered the NeuroTracker technology, a virtual reality system used for perceptual-cognitive training in sports and clinical settings. Education: Ph.D. (Experimental Psychology - Visual Psychophysics), Université Concordia (1991) M.Sc. (Experimental Psychology - Visual Psychophysics), Université Concordia (1986) B.Sc. (Honors in Experimental Psychology), Université Concordia (1984) Research Interests: Neurophysiological mechanisms of visual perception, VR applications for athlete training, cognitive enhancement in aging populations, and clinical tools for assessing vision-related deficits. His work spans sports science, neurorehabilitation, and optometry. Grants & Awards: NSERC/Essilor Industrial Research Chair (2003–present) Emerson Woodruff Medal (2006), Joe Brunneni Award (2009) Over 120 peer-reviewed publications and multiple patents in biomedical devices and VR systems. Notable Projects: NeuroTracker development for concussion assessment and performance training Clinical studies on driving safety in elderly populations Virtual reality systems for postural control research Labs & Teams: Director of the Psychophysics and Vision Perception Laboratory, affiliated with CRIR (Rehabilitation Research Center) and CIRCA (Brain and Learning Research Center).

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.

Yuvraj GajpalView profile

Associate Professor

• Transportation and Logistics
• Vehicle Routing
• Combinatorial Optimization
• +3 more

Yuvraj Gajpal is an Associate Professor in the Supply Chain Management Department at the Asper School of Business, University of Manitoba. He holds a Ph.D. in Management Science from McMaster University and a Master's from IIT Madras. His research applies heuristics and meta-heuristics to transportation, logistics, and supply chain optimization, with publications in journals like Computers and Operations Research and European Journal of Operations Research . Prior to Manitoba, he worked at King Fahd University of Petroleum and Minerals (KFUPM) and completed a postdoc at CIRRELT. Research Focus: Vehicle routing, combinatorial optimization, sustainable logistics, and retail supply chain management using algorithms like Ant Colony and Tabu Search. Publications: Recent work (2021-2024) addresses healthcare scheduling, e-commerce strategies, IoT waste systems, and pandemic testing logistics, reflecting a strong emphasis on real-world optimization challenges.