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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.

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.

Sirisha RambhatlaView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Representation Learning
• +4 more

Sirisha Rambhatla is an Assistant Professor at the University of Waterloo, holding appointments in the Management Sciences Department (Faculty of Engineering), Systems Design Engineering, and the David R. Cheriton School of Computer Science (Faculty of Mathematics). She leads the CriticalML lab, focusing on reliable AI for healthcare, aviation, and climate change, leveraging deep learning and spatiotemporal analysis. Her work bridges theory and practice, with publications at top venues like NeurIPS, ICLR, and clinical journals. Affiliations include the Waterloo AI Institute and the Waterloo Institute for Sustainable Aeronautics. Education: Ph.D. and M.S. in Electrical Engineering, University of Minnesota (2019, 2012) B.Tech in Electronics and Telecommunication Engineering, College of Engineering Roorkee (2010) Research Interests: Representation Learning, Spatiotemporal Data Analysis, AI for Healthcare, Computer Vision, and provable algorithms. Her work emphasizes interpretable AI and applications in surgery, intelligent automation, and climate modeling. Awards: 2021 Merit Award for Excellence in Postdoctoral Research E. Bruce Lee Memorial Fellowship University Bronze Medalist (2010) Teaching: Courses include Advanced Machine Learning (MSCI 546/MSE 546), Big Data Analytics (MSCI 623), and Special Topics in Management Engineering (MSCI 598/MSE 598). Labs/Teams: Leads the CriticalML lab, focusing on trustworthy AI systems for critical applications.

Victor ZhongView profile

Assistant Professor

• Machine Learning
• Natural Language Processing
• Reinforcement Learning
• +3 more

Victor Zhong is an Assistant Professor in the Department of Computer Science at the University of Waterloo. He holds a PhD from the University of Washington (2023), an MS from Stanford University (2016), and a BASc in Computer Engineering from the University of Toronto (2014). His research focuses on machine learning, natural language processing, reinforcement learning, and artificial intelligence, with particular emphasis on dialogue systems, text-to-SQL translation, and multimodal agents. Zhong’s work addresses challenges in automated workflow generation, reward function design, and policy improvement using language feedback. His research interests include developing robust question answering systems (e.g., RoMQA benchmark), simplifying bioinformatics analysis through conversational interfaces, and evaluating language models in real-world enterprise contexts (e.g., Spider 2.0). He has contributed to foundational NLP tasks like dialogue state tracking, neural network-based query translation, and grounding language in dynamic environments. Recent publications explore open-ended tasks in real computer environments (OSWorld benchmark) and the limitations of parametric/non-parametric memories in language models. Zhong’s work bridges theoretical advancements with practical applications, aiming to automate data science workflows and enhance AI agents’ real-world adaptability. Though no specific grants or awards are listed, his prolific publication record reflects sustained innovation in machine learning and NLP domains.

Pooya RonaghView profile

Academic

• Quantum Computation
• Quantum Algorithms
• Quantum Error Correction
• +8 more

Pooya Ronagh is a Research Assistant Professor at the University of Waterloo, affiliated with the Department of Physics & Astronomy and the Institute for Quantum Computing (IQC). He also serves as a Scientific Lead at the Perimeter Institute Quantum Intelligence Lab (PIQuIL) and directs the Hardware Innovation Lab at 1QBit. His work bridges quantum computation, machine learning, and optimal control, focusing on quantum algorithms, error correction, and hybrid quantum-classical systems. Education: PhD in Mathematics (University of British Columbia, 2016), MSc in Mathematics (UBC, 2011), dual BSc in Mathematics and Computer Science (Sharif University of Technology, 2009). Awards include the Benjamin Franklin Fellowship (2009). Research Interests: Quantum algorithms for machine learning, reinforcement learning, fault-tolerant quantum architectures, cryogenic systems, and quantum control. He explores applications of quantum simulation to improve learning efficiency and robustness in AI systems. Recent work includes optimizing quantum error correction decoders, developing scalable superconducting architectures, and advancing neural network-based quantum state tomography. His contributions span theoretical frameworks (e.g., lattice surgery scheduling) and experimental methods (e.g., SFQ pulse control). Teaching: Courses like PHYS 490 (Machine Learning in Physics) emphasize practical coding and interdisciplinary projects. Grants and collaborations involve industry and academic partners in quantum hardware and software development. Labs: Hardware Innovation Lab (1QBit), IQC Quantum Control Group Future Work: Scaling quantum supercomputers, cryogenic neural decoders, quantum-enhanced generative AI

Beni YoshidaView profile

Research Professor

• Quantum Information Theory
• Quantum Gravity
• Condensed Matter Theory
• +3 more

Beni Yoshida is a Research Professor at Perimeter Institute for Theoretical Physics, focusing on quantum information theory, quantum gravity, and condensed matter physics. His work explores applications of quantum coding theory to quantum many-body systems and black hole physics. He held a 5-Year Senior Postdoctoral Researcher position at Perimeter (2015–2017) and was a Burke Fellow at Caltech’s Institute for Theoretical Physics (2012–2015). Research Interests: Quantum error correction and its holographic interpretations Information retrieval from black holes via entanglement scrambling Connections between quantum gravity and condensed matter systems Awards Nishinomiya Yukawa Memorial Prize in Theoretical Physics (2021). Advising & Grants: No formal advisees listed, but his research involves collaborations with institutions worldwide. Grants and funding details not specified in text. Labs/Teams: Active member of Perimeter Institute’s quantum gravity and quantum information theory research groups, with seminar engagements at leading institutions like Caltech, University of Chicago, and international conferences.

• 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).

• Neuroscience
• Cognitive Psychology
• Autism Spectrum Disorders
• +7 more

Boutheina Jemel is an Associate Professor at the School of Speech-Language Pathology and Audiology within the University of Montreal . Her research focuses on neurocognitive processes in visual and auditory information processing, particularly in autism spectrum disorders and developmental language disorders .

• 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.

Jeffrey PaiView profile

Professor

• Risk management
• Derivatives statistical modeling
• Term structure of interest rates
• +3 more

Jeffrey Pai is a Professor at the Warren Centre for Actuarial Studies and Research within the Asper School of Business at the University of Manitoba. With over 30 years of academic experience spanning multiple countries, he specializes in actuarial science, risk modeling, and financial derivatives. His research examines livestock/crop insurance, weather derivatives, and Bayesian statistical methods for risk assessment. Research encompasses quantitative risk management frameworks, stochastic modeling of insurance products, and econometric analysis of financial instruments. Recent work focuses on parametric insurance for natural disasters, mortality catastrophe modeling, and agricultural risk transfer mechanisms using advanced statistical approaches. Publications demonstrate consistent focus on practical applications of actuarial science, particularly in agricultural insurance and weather-related financial instruments. Recent trends show increased emphasis on Bayesian methods, catastrophe modeling, and developing market applications. Awards: Students' Teacher Recognition Awards (2021, 2002) CAS Best Paper Award (2016) Literati Network Outstanding Paper Award (2011) Featured in MACLEAN's Guide to Canadian Universities (2004) Extensive teaching includes courses in life contingencies, risk theory, and actuarial modeling. Secured research funding from international agencies including SSHRC (Canada), Society of Actuaries (USA), and UK Development Partnerships.

Darren WershlerView profile

Associate Professor

• Media History
• Video Game Studies
• Communication Studies
• +3 more

Darren Wershler is an Associate Professor of English at Concordia University and holds a Tier 2 Research Chair in Media & Contemporary Literature. His work bridges avant-garde art/literature with communication studies/media history, emphasizing interdisciplinary connections between academia and broader cultural contexts. Education: PhD (York University, 2005), MA (University of Alberta), BA (University of Manitoba, First Class Honours). Research focuses on media archaeology, video game studies, and digital rights. He co-founded the Residual Media Depot, housing North America's largest institutional collection of vintage video game consoles and modified hardware. Key research areas include material culture of analog/digital technologies, media policy, and experimental poetics. Awards include SSHRC grants, the Alcuin Citation, and ReLit nomination. He collaborates across disciplines through initiatives like the Artmob project and the Milieux Institute. Notable works include The Iron Whim (2007), a groundbreaking study of typewriting's cultural impact; Guy Maddin's My Winnipeg (2010), analyzing Canadian avant-garde cinema; and collaborative projects like Dynamic Fair Dealing (2020) addressing digital cultural rights. Teaching: Media Archaeology, Video Game Theory, Graduate Research Methods Labs/Initiatives: Media History Research Centre, Residual Media Depot Publications: 12 authored/co-authored books spanning academic and creative works

Karen GordonView profile

Professor

• Biomechanics
• Soft Tissue Mechanics
• Medical Imaging
• +4 more

Karen Gordon, PhD, P.Eng., is a Professor and Associate Dean (Academic) at the Gordon S. Lang School of Business and Economics, University of Guelph. She holds expertise in Biomechanics and Soft Tissue Mechanics, with research focusing on medical imaging, wearable devices, and engineering education. Her academic roles include advising on curriculum development and overseeing academic programs at the Lang School. Her research spans biomechanics of wrist and joint kinematics, with applications in orthopedics and ergonomics. She has developed novel 3D-printed wearable sensors for monitoring joint motion, emphasizing low-cost and accessible medical technologies. Her work also explores STEM education, particularly problem-solving pedagogy and the role of memorization in engineering courses. Dr. Gordon teaches courses in Biosystems Engineering, Mechanics, Biomaterials, and Engineering Design. She is affiliated with the University's Biological and Biomedical Engineering programs. Her recent publications address machine learning in biomechanics, carpal tunnel morphology, and interdisciplinary educational strategies.

William N. SumnerView profile

Associate Professor

• Automated Debugging
• Concurrency & Parallelism
• Program Analysis & Transformation
• +3 more

William N. Sumner is an Associate Professor and Associate Director, Academic and Administration in the School of Computing Science at Simon Fraser University. He holds a Ph.D. in Computer Science from Purdue University (2013) and a B.Sc. in Computer Science from Hope College (2005). His research focuses on automated debugging tools, concurrency, parallelism, program analysis, and software quality. He teaches courses such as Software Development Methods, Software Testing, and Automated Software Analysis. Sumner actively recruits graduate and undergraduate students for research in improving software development processes through automation. His work emphasizes reducing repetitive tasks in software development using techniques like test case reduction and program analysis. Education: Ph.D., Computer Science, Purdue University, 2013 B.Sc., Computer Science, Hope College, 2005 Research Interests: Sumner’s research addresses challenges in software development automation, including automated debugging, test suite assessment, and program hardening. He develops tools like Pardis to optimize test case reduction and improve software reliability. His work intersects with concurrency, parallelism, and program transformation to enhance developer productivity and software quality. Recent Contributions: Recent publications include advancements in type batched program reduction (2023) and leveraging models for test case reduction in software repositories (2021). His Pardis framework (2019) improves test case reduction efficiency by addressing priority inversion in structured inputs. Teaching & Engagement: Sumner teaches courses such as CMPT 373 (Software Development Methods), CMPT 473 (Software Testing), and CMPT 479 (Automated Software Analysis). He emphasizes hands-on projects, such as the Geotagged Meeting Planner in CMPT 276, focusing on teamwork, agile methodologies, and software engineering best practices.

Asokan Mulayath VariyathView profile

Professor

• Machine Learning
• Statistical Process Control
• Empirical Likelihood
• +8 more

Dr. Asokan Mulayath Variyath is a Professor of Statistics at the Department of Mathematics and Statistics, Memorial University of Newfoundland. He holds roles as Deputy Head (Graduate) and Director of the Statistical Consultancy Centre. His academic journey includes positions at Texas A&M University, University of Waterloo, and the Indian Statistical Institute. Education: Ph.D. in Statistics (2006), University of Waterloo S.D.P. Fellow (1991) and Postgraduate Diploma (1988) in SQC & OR, Indian Statistical Institute M.Sc. in Agricultural Statistics (1987), Kerala Agricultural University Research Focus Dr. Variyath's research spans Machine Learning, Statistical Process Control, Empirical Likelihood, Spatial Analysis, and Fisheries Data Modeling. He has developed statistical applets to enhance learning, such as interactive tools for Normal Distribution and Sampling Techniques. Awards: 2024: Dean of Science Distinguished Service Award 2023: Dean’s Award for Service Excellence 2010: Professional Statistician (P.Stat) from SSC Teaching & Supervision: Currently supervising 1 Ph.D., 5 M.Sc., and 3 professional students. Over 20 advisees graduated under his mentorship. Courses taught include Design of Experiments, Survival Analysis, and Computational Statistics. Key Contributions: Principal Investigator for the 'Statistical Applets' project funded by MUN, advancing interactive learning in statistics.