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Ying ZouView profile

Professor

• Software Engineering
• Software Maintenance and Evolution
• Empirical Studies
• +7 more

Dr. Ying Zou is a Professor and Canada Research Chair (Tier II) in Software Evolution at Queen’s University’s Department of Electrical and Computer Engineering, with a cross-appointment to the School of Computing. She is a Visiting Faculty Fellow at IBM Centers for Advanced Studies (CAS) and a member of IEEE. Her research focuses on software engineering, maintenance, empirical studies, and service-oriented computing, with notable contributions to refactoring patterns, performance analysis, and developer tooling. Education: BEng from Beijing Polytechnic University (China), MEng from Chinese Academy of Space Technology (China), and PhD from the University of Waterloo (Canada). Research interests include software evolution dynamics, code clone analysis, and optimizing developer workflows. Notable achievements include the 2014 IBM CAS Research Faculty Fellow of the Year and two IBM Faculty Awards (2007, 2008). Her work bridges empirical software engineering with practical industrial applications. Awards: Canada Research Chair, IBM Faculty Awards, IBM CAS Fellow Grants: IBM CAS collaborations, cross-disciplinary research funding Labs/Teams: Active contributor to the Software Evolution and Analysis Lab (SEAL), focusing on empirical studies and tool development for software maintenance challenges.

• Software Engineering
• Data Analytics
• Mining Software Repositories
• +5 more

**Ahmed Hassan** is a **Professor** and **Canada Research Chair (Tier 2) in Software Analytics** at the **School of Computing**, **Queen's University**, affiliated with the **Faculty of Arts and Science**. He holds the **NSERC/RIM Industrial Research Chair in Software Engineering for Ultra Large Scale Systems**. His research focuses on software systems, data analytics, and mining software repositories (MSR), leading the **Software Analysis and Intelligence Lab (SAIL)**. **Education**: PhD (2005), MMath (2001), BMath (1999) in Computer Science from the University of Waterloo, Canada. **Research Interests**: Software evolution, performance engineering, distributed systems debugging, cybersecurity, and AI-driven software solutions. SAIL explores tools for maintaining large-scale software systems, improving developer productivity, and integrating AI into software workflows. **Awards & Recognition**: ACM and IEEE Fellowships, patents in multiple jurisdictions, and leadership roles in MSR and ICSE conferences. Industrial experience includes RIM/BlackBerry, IBM Research, and Nortel Networks. **Current Projects**: Focus on foundation model-powered software (FMware), AIOps, and ethical AI applications. Teaching courses like CISC322: Software Architecture.

• 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.

• Computer Vision
• Machine Learning
• Sports Analytics
• +3 more

David Lau is a Lab Instructor/Supervisor at the University of Waterloo, specializing in computer vision and machine learning applications across sports analytics and environmental monitoring. His work focuses on developing algorithms for hockey puck localization, player tracking, and sea ice classification using SAR imagery. He contributes to datasets like AI4Arctic and explores synthetic data augmentation techniques for improving model robustness. Research interests include deep learning architectures for multi-task learning, contextual cue utilization in vision systems, and the integration of remote sensing data with climate models. Key areas of exploration involve improving hockey video analysis through homographic projections and leveraging Bayesian methods for uncertainty quantification in environmental predictions. Recent work emphasizes domain-specific adaptations like sports field localization and ice hockey event detection. His contributions span from foundational algorithms (e.g., IceGCN for SAR imagery) to practical systems like PuckNet for puck location estimation in broadcast videos. Advising and grants: No specific grants or advisees listed in available information. Collaborates on projects involving hockey analytics teams and environmental monitoring initiatives, though specific team names are not disclosed.

Michael GodfreyView profile

Professor

• Software Engineering
• Software Evolution
• Empirical Studies
• +5 more

Michael Godfrey is a Professor at the University of Waterloo's David R. Cheriton School of Computer Science, affiliated with the Department of Electrical and Computer Engineering. His work focuses on software engineering, empirical studies of software systems, code review practices, and open-source software ecosystems. He holds a Ph.D., M.Sc., and B.Sc. from the University of Toronto (1997, 1988, 1986). Research interests include software evolution, mining software repositories, provenance tracking, code duplication analysis, and program comprehension. He actively explores how developers interact with code reviews, documentation systems, and modern CI/CD pipelines. Recent work investigates app store software ecosystems, Bash scripting vulnerabilities, and deep learning API testing. Publications span empirical studies of developer communities (e.g., Stack Overflow analysis), architectural teaching methods, and automated testing techniques like documentation-guided fuzzing. His work bridges theory and practice, addressing challenges in large-scale systems maintenance and developer productivity. Leverages tools like Elasticsearch for repository mining and explores anomaly detection in software development. Engaged in curriculum development for software architecture education and has contributed to conferences like MSR and ICSE.

• Medical Imaging
• Ultrasound Technology
• Machine Learning
• +3 more

Di Xiao is a Sessional Lecturer at the University of Waterloo. Their work focuses on advancing medical imaging technologies, particularly in ultrasound and MRI, with an emphasis on real-time processing, deep learning integration, and algorithm optimization. They are affiliated with the Sessional lecturers group. Research interests include ultrasound imaging innovations such as speed-of-sound estimation, beamforming techniques, and sparse-array optimizations. Their work also spans MRI reconstruction, signal processing, and applications of machine learning in medical diagnostics. Notable contributions include developing frameworks for live tissue assessment and minimizing image quality loss in sparse data scenarios. Publications highlight trends in real-time ultrasound systems, neural network-driven imaging enhancements, and sparsification strategies for MRI. The articles collectively emphasize interdisciplinary approaches merging AI with traditional medical imaging challenges. No scientific awards or grants are explicitly mentioned. No lab affiliations or student advising records are provided.

Ihab F. IlyasView profile

Professor

• Data Quality
• Machine Learning
• Database Systems
• +4 more

Ihab F. Ilyas is a Professor and holds the Thomson Reuters–NSERC Industrial Research Chair in Data Quality at the University of Waterloo's Department of Computer Science. His research focuses on data quality, machine learning for data enrichment, probabilistic data management, and knowledge graph systems. He leads projects like Holoclean (a probabilistic data repair system) and Data Civilizer (a data unification platform). His work bridges database systems and machine learning to address challenges in data integration, error detection, and large-scale knowledge representation. Education: PhD in Computer Science from Purdue University (2004), MSc and BSc from Alexandria University, Egypt (1999 and 1995). His publications span vector search, knowledge graph construction, and data curation techniques, with recent emphasis on adaptive indexing and scalable systems. He has contributed to open-source tools and frameworks for data cleaning and machine learning integration. Research interests include probabilistic data management, machine learning applications for data quality, and scalable systems for big data. His work often addresses real-world challenges in data integration and semantic representation, with a focus on practical, deployable solutions.

Lila KariView profile

Professor

• Biodiversity informatics
• Data science
• Machine learning
• +6 more

Lila Kari is a Professor and Cheriton Faculty Fellow at the School of Computer Science , part of the University of Waterloo in Ontario, Canada. Her research focuses on Biodiversity informatics , data science , and machine learning applications in comparative genomics and metagenomics , particularly for analyzing genomic signatures through Chaos Game Representation (CGR) and alignment-free methods. 2023 : Environment and taxonomy shape genomic signatures of extremophiles 2020 : Machine learning for rapid pathogen classification during pandemics 2019 : Ultrafast DNA sequence classification with ML-DSP 2016 : Additive genomic signatures for enhanced taxonomic differentiation 2015 : Mapping genomic signature spaces for molecular distance analysis 2009-2005 : Foundational work in DNA language theory and computational biology Her work has been instrumental in developing composite DNA signatures that combine nuclear and organellar genomic data for improved species differentiation, and assembled DNA signatures that enable analysis from fragmented sequencing data. She contributes to global initiatives like BIOSCAN for biodiversity surveillance and has created tools such as MLDSP-GUI for accessible DNA sequence analysis.

Bissan GhaddarView profile

Adjunct Associate Professor

• Operations Research
• Optimization
• Energy Systems
• +3 more

Bissan Ghaddar is an Adjunct Associate Professor affiliated with Western University's Ivey Business School. Her research focuses on optimization techniques applied to energy systems, transportation networks, and artificial intelligence. She explores challenges in robust optimization, polynomial programming, and edge computing resource management. Her work bridges theoretical advancements with practical applications in smart cities and sustainability. Research interests include operations research, network design, and machine learning-driven decision-making. Key areas of contribution include autonomous vehicle routing, community energy storage optimization, and latency-aware edge computing frameworks. Her interdisciplinary approach integrates mathematical rigor with real-world systems engineering. Recent publications emphasize robust optimization models for electric vehicle routing and power systems, as well as AI-driven algorithm selection in branch-and-bound methods. She has pioneered frameworks for crowd-sourced delivery systems and smart infrastructure standardization in emerging economies. While no specific grants or awards are listed in the provided materials, her active research portfolio indicates significant contributions to both academic and applied domains. Collaborative projects involve cross-disciplinary teams addressing challenges at the intersection of mathematics, engineering, and business strategy.

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.

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.

Stephen WattView profile

Professor

• Symbolic Computation
• Computer Algebra Systems
• Programming Languages
• +5 more

Stephen Watt is a Professor at the University of Waterloo, affiliated with the Department of Computer Science within the Faculty of Mathematics. He holds a PhD in Computer Science (1986), MMath in Applied Mathematics (1981), and BSc in Honors Mathematics and Physics (1979). His research focuses on enabling computers to handle mathematics intelligently, with key areas including programming languages for mathematical software, symbolic computation algorithms, computer algebra system interoperability, mathematical knowledge management, and STEM education technology. His work spans theoretical and applied domains, such as developing algorithms for block matrices and handwriting recognition systems like the Legendre-Sobolev approach. He has contributed to initiatives like the International Mathematical Knowledge Trust (IMKT) and collaborated on projects like InkML for mathematical collaboration. Recent publications explore generative AI in STEM assessments, software portability, and GPU-optimized arithmetic operations. Dr. Watt’s research also addresses historical perspectives on symbolic computation and its evolution, alongside modern challenges in mathematical collaboration tools and digital ink compression. His contributions span conferences such as ISSAC, CICM, and SYNASC, reflecting interdisciplinary engagement with both theoretical and applied computer science.

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.

Matthias SchonlauView profile

Professor

• Survey Methodology
• Natural Language Processing
• Data Visualization
• +3 more

Matthias Schonlau is a Professor in the Department of Statistics at the University of Waterloo. He previously worked as a statistician at the RAND Corporation (1999-2011), where he led the RAND Statistical Consulting Service. He holds a PhD from the University of Waterloo (1997) and a Master's from Queen's University (1993). His research focuses on survey methodology, natural language processing for open-ended questions, data visualization, and statistical software development. Key contributions include the Hammock Plot for mixed data visualization and automated classification algorithms for open-ended survey responses. His work spans algorithmic innovation (e.g., occupation coding, multi-label classification) and statistical software tools (e.g., HAMMOCK and RFOREST modules for Stata). Recent projects address semi-automated classification, one-shot learning, and text dataset distillation. He has held sabbaticals at the University of Auckland (2015-2016) and DIW Berlin (2009-2010), collaborating with the Max Planck Institute. Major awards include the Humboldt Research Prize (2022) and ASA Fellowship. His publications emphasize bridging statistical methods with practical applications, including books like Applied Statistical Learning (2023) and peer-reviewed articles in computational statistics and machine learning.

Roger MelkoView profile

Professor

• Quantum Computing
• Condensed Matter Physics
• Machine Learning in Physics
• +3 more

Roger Melko is a Professor at the University of Waterloo, affiliated with research groups focused on Quantum Information & Computing and Quantum Matter. His work bridges quantum physics, machine learning, and computational methods to advance understanding of complex quantum systems. He is a prominent figure in applying neural networks to quantum state reconstruction and quantum simulation. Research interests span quantum computing architectures, condensed matter physics (e.g., quantum spin liquids, Hubbard models), and leveraging machine learning for quantum phase transitions, entanglement analysis, and device optimization. His contributions include neural network wavefunctions, quantum-assisted generative models for particle detection, and economic analysis of quantum technologies. Recent articles highlight advancements in quantum simulation using recurrent neural networks, hybrid quantum-classical algorithms for high-energy physics, and robust quantum dot autotuning. His work emphasizes interdisciplinary approaches, integrating AI with quantum hardware development.