جست‌وجوی استاد و پژوهشگر

David Wehlauمشاهده پروفایل

استاد

• Algebraic groups
• Invariant theory
• Commutative algebra
• +۴ مورد دیگر

David Wehlau is a Professor of Mathematics at the Royal Military College of Canada (RMC), with a joint affiliation at Queen's University. He holds a Ph.D. (Brandeis, 1989) and Master’s (Brandeis, 1984) in Mathematics, and an Honors BSc in Mathematics and Computer Science from Western University. His research focuses on algebraic groups, invariant theory, commutative algebra, and discrete mathematics, with notable contributions to modular invariant theory and cryptographic applications. He has advised numerous graduate students, including Asia Matthews (PhD 2015), Yinglin Wu (PhD 2009), and Emilie Dufresne (PhD 2008). His awards include the Cowan Prize (2012) for research and the RMC Teaching Excellence Award (2002). He has organized major conferences like the 2016 Algebraic Combinatorics and Group Actions conference and co-authored influential books such as Modular Invariant Theory . Wehlau collaborates extensively with researchers at Queen's University, including Eddy Campbell and Ivan Dimitrov, on algebraic geometry and representation theory. His work also bridges cryptography and finite geometry, with applications in error-correcting codes and secure communication systems.

Chenghao Liuمشاهده پروفایل

پژوهشگر ارشد

• Molecular Design
• Chemistry
• Computer Science
• +۳ مورد دیگر

Chenghao Liu is a postdoctoral Research Fellow at the California Institute of Technology (Caltech), where he is supervised by Prof. Frances Arnold starting in 2025. He previously completed his Ph.D. at McGill University (2019-2024) under Prof. Dmytro Perepichka and collaborated with Prof. Yoshua Bengio at Mila. He co-founded the protein design startup Dreamfold (2021-2025) and was a Vanier Canada Graduate Scholar (2021-2024). His research bridges chemistry and computer science, focusing on AI-driven molecular and materials design. His recent publications highlight work on generative AI for 3D molecule synthesis (SynCoGen), GFlowNets for synthesizable molecular generation (RGFN), and covalent organic frameworks with tailored electronic properties. He has contributed to machine learning applications in chemistry, including protein backbone generation and CO₂ photoreduction catalysts. His work spans molecular design , AI algorithms , and materials discovery . Scientific Awards Vanier Canada Graduate Scholar (2021-2024)

Pin-Han Hoمشاهده پروفایل

استاد

• Wireless Communications
• Quantum Communications
• Unmanned Aerial Vehicles (UAV)
• +۴ مورد دیگر

Pin-Han Ho is a Professor at the University of Waterloo's Department of Electrical and Computer Engineering (ECE), part of the Faculty of Engineering. His research focuses on advanced wireless communication systems, including Near-Field Communications (NFC), Reconfigurable Intelligent Surfaces (RIS), and Quantum Key Distribution (QKD). He explores applications in UAV-enabled networks, IoT security, and energy-efficient trajectory planning for autonomous systems. His work integrates machine learning techniques such as Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) to address challenges in signal processing, resource allocation, and network optimization. Key contributions include beamforming design for dynamic environments, secure resource management in optical networks, and hybrid communication systems combining near-field and far-field capabilities. Dr. Ho's publications span over 50 peer-reviewed articles, emphasizing interdisciplinary approaches to modern communication challenges. Notable areas include UAV trajectory optimization, quantum CNN-based detection systems, and connectivity-preserving routing algorithms. His research has been featured in special issues on NFCs, quantum networks, and IoT security. Despite extensive contributions, no academic awards or grants are explicitly listed in the provided information. His advising role and lab affiliations remain unspecified in the text.

Joanne M. Atleeمشاهده پروفایل

استاد

• Software Engineering
• Software Product Lines
• Feature Interaction Detection
• +۳ مورد دیگر

Joanne M. Atlee is a Professor at the Cheriton School of Computer Science , University of Waterloo, Canada. Her research focuses on software engineering , particularly improving software quality through feature modeling and feature interaction management in complex systems like software product lines , systems-of-systems , and the Internet of Things . She develops theories of composition, interaction detection algorithms, coordination architectures, and proof systems for interoperability. Key research areas include Feature Interaction Management Software Product Line Engineering Model-Based Engineering Formal Verification Her recent publications explore AI-generated code detection (2024), variability-aware analysis (2024), and dynamic assertion generation (2023). Collaborations span institutions like McGill University , INRIA , and Siemens Canada , with applications in automotive software and cyber-physical systems .

Shalev Ben-Davidمشاهده پروفایل

دانشیار

• Computational complexity
• Quantum computing
• Theoretical computer science
• +۳ مورد دیگر

Shalev Ben-David is an Associate Professor at the University of Waterloo's Department of Computer Science. His research focuses on computational complexity and quantum computing, exploring foundational questions in algorithm design, quantum advantages, and theoretical limits of computation. He holds a PhD from MIT (2017) and a BMath from the University of Waterloo (2011). Education: PhD in Computer Science, MIT, 2017 BMath in Computer Science, University of Waterloo, 2011 His work bridges classical and quantum computational complexity, with contributions to query complexity, oracle separations, and quantum algorithm design. Recent research includes analyzing quantum speedups in symmetric problems and exploring cryptographic applications of quantum tokens. Despite no explicitly listed awards, his prolific publication record reflects sustained academic impact. Advising and grants details are not provided in available texts. His office is located in DC 3128, and further details are accessible via his personal webpage .

• Computer Vision
• Machine Learning
• Sports Analytics
• +۳ مورد دیگر

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.

Ihab F. Ilyasمشاهده پروفایل

استاد

• Data Quality
• Machine Learning
• Database Systems
• +۴ مورد دیگر

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.

Yuying Liمشاهده پروفایل

استاد

• Computational Optimization
• Data Science
• Computational Finance
• +۳ مورد دیگر

Yuying Li is a Professor at the University of Waterloo's Cheriton School of Computer Science. Research focuses on computational optimization algorithms for finance, including neural network approaches for leverage-constrained portfolios, benchmark outperformance strategies, and inflation-regime asset allocation. Holds a BSc from Sichuan University (1982), MMath (1985) and PhD (1988) from Waterloo. Her work bridges continuous optimization, machine learning, and financial engineering. Recent publications develop neural alternatives to dynamic programming for portfolio management, with applications to ETF leverage, decumulation strategies, and high-inflation environments. Methodological innovations address computational scalability and regime-specific modeling.

Naomi Nishimuraمشاهده پروفایل

دانشیار

• Combinatorial Reconfiguration
• Parameterized Complexity
• Graph Algorithms

Naomi Nishimura is an Associate Professor at the Cheriton School of Computer Science, University of Waterloo. She holds a Ph.D. from University of Toronto (1991) and researches combinatorial reconfiguration, parameterized complexity, and graph algorithms. Her work develops efficient algorithms for reconfiguration problems, including recent advances in parallel implementations for atom reconfiguration using GPUs. Nishimura's research establishes theoretical foundations for transforming combinatorial structures through step-by-step modifications while maintaining solution properties. Recent publications survey parameterized complexity landscapes for reconfiguration problems and introduce novel algorithms like the assignment-rerouting-ordering technique. Her group analyzes computational boundaries for problems involving independent sets, dominating sets, and graph minors.

Tamer Özsuمشاهده پروفایل

استاد

• Distributed Data Management
• Graph Databases
• RDF Systems
• +۲ مورد دیگر

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.

Recep Bekciمشاهده پروفایل

استادیار

• Data-Driven Optimization
• Operations Research
• Supply Chain Management
• +۴ مورد دیگر

Recep Bekci is an Assistant Professor of Operations Research in the Department of Management Science and Engineering at the University of Waterloo. His research focuses on developing data-driven optimization models for decision-making under uncertainty, with applications in supply chain management, revenue management, and inventory control. He holds a Ph.D. in Operations Management from McGill University (2023) and a BSc in Industrial Engineering from Bilkent University. Education Background: Ph.D., Operations Management, McGill University, 2023 B.Sc., Industrial Engineering, Bilkent University Research Interests: Specializes in integrating machine learning with operations research methodologies. Recent work includes: Image-based sales forecasting for fast-fashion using CNNs Genomic mutation analysis via graph neural networks Risk-averse revenue management frameworks Dynamic inventory control with censored demand data Teaching Experience: MSCI 609: Quantitative Data Analysis (University of Waterloo, 2024) MSCI 131: Work Design and Facilities Planning (University of Waterloo, 2024) MGCR 472: Operations Management (McGill University, 2022) Industry Collaboration: Partnered with companies like Koton (Europe's largest fast-fashion retailer), Aviva-Sa Insurance, and Turkish Aerospace Industries to implement optimization solutions.

Sirisha Rambhatlaمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Representation Learning
• +۴ مورد دیگر

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 Smuckerمشاهده پروفایل

استاد

• Information Retrieval
• Search Engines
• Natural Language Processing
• +۳ مورد دیگر

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 Zhangمشاهده پروفایل

استادیار

• Large-scale data analytics
• Distributed ML training and serving systems
• Application and network scheduling
• +۵ مورد دیگر

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.

• Computer-Aided Manufacturing
• Geometric Algebra
• Computer Graphics
• +۳ مورد دیگر

Stephen Mann is a Professor at the University of Waterloo's Department of Computer Science. His research focuses on geometric algebra, CNC machining, and spline surfaces, with applications in computer graphics and manufacturing automation. He has taught courses including CS 251 (Computer Organization), CS 488 (Computer Graphics), and specialized topics like splines in graphics. His work bridges theoretical geometric modeling with practical manufacturing challenges. Educational background includes a Doctorate from Washington University and degrees from UC Berkeley. His publications emphasize tool path optimization, neural networks in machining feature recognition, and geometric algebra frameworks. Notable contributions include books on geometric algebra and spline theory. Current research trends involve integrating machine learning with CNC processes and developing efficient algorithms for multi-axis machining. He is actively recruiting graduate students interested in computational geometry and manufacturing systems.