جستجوی استاد راهنمای دانشگاهی

Khalid Elgazzarمشاهده پروفایل

دانشیار

• Internet of Things (IoT)
• Artificial Intelligence
• Big Data
• +۷ مورد دیگر

Dr. Khalid Elgazzar is an Associate Professor and Canada Research Chair in the Internet of Things (IoT) at Ontario Tech University's Faculty of Engineering and Applied Science. He holds a PhD in Computer Science from Queen's University and has expertise in IoT, AI, Big Data, distributed systems, and mobile computing. His research bridges physical infrastructure with technological innovations, with applications in healthcare, transportation, and smart cities. Education: PhD in Computer Science, Queen's University, 2013 MSc in Computer Engineering, Arab Academy for Science and Technology, 2007 BSc in Computer and Communication Engineering, Alexandria University, 1995 Research Interests: Dr. Elgazzar focuses on IoT architectures, real-time data analytics, and edge computing, with emphasis on cybersecurity, smart healthcare systems, and transportation safety. His work integrates AI techniques like deep learning and reinforcement learning to address challenges in distributed computing and sensor networks. Awards: Best Paper Award at IEEE/ACM International Conference on Utility and Cloud Computing (2013) Outstanding Achievement in Sponsored Research Award (2017) Queen's School of Computing Distinguished Research Award (2014) Grants & Labs: Recipient of a Canada Research Chair Tier II (2018-present). He leads the IoT Research Lab, advancing projects like real-time ECG monitoring systems and intelligent traffic prediction models. Collaborations include institutions like Carnegie Mellon University and IBM Canada. Teaching: Design and Analysis for IoT Software Systems (SOFE 4610U) Real-Time Data Analytics for IoT (ENGR 5785G) Introduction to Programming for Engineers (ENGR 1200U)

• Quantum Information Science
• Quantum Computing
• Quantum Information Theory
• +۲ مورد دیگر

Hlér Kristjánsson is an Adjunct Professor at the Faculty of Arts and Science within the Department of Computer Science and Operations Research at Université de Montréal. His research focuses on quantum information science, quantum computing, and theoretical physics, with affiliations at Mila - Quebec Artificial Intelligence Institute. He teaches IFT6760C – Seminar in Machine Learning and oversees research projects funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). Key projects include exploring causal structures and functional programming in quantum computation (2025–2031), supported by NSERC Discovery Grants and Tremplin programs. His work bridges quantum mechanics with computational models, addressing topics like quantum switch simulation, indefinite causal order, and resource-theoretic frameworks. Collaborations involve experimental quantum communication enhancements and second-quantized Shannon theory. Teaching responsibilities span master’s (e.g., Machine Learning), doctoral programs in computer science, and specialized DESS programs. Funding milestones include a 7-year NSERC grant (PVX20965/RGP) and a PVXXXXXX/DGECR initiative. No scientific awards are explicitly listed, though his contributions to quantum gravity as a communication resource and routed quantum circuits highlight innovative directions. Research is anchored at Mila, emphasizing interdisciplinary applications in AI and quantum systems.

Maryam Daryalalمشاهده پروفایل

استادیار

• Sequential decision making under uncertainty
• Stochastic programming
• Robust optimization
• +۵ مورد دیگر

Dr. Maryam Daryalal is an Assistant Professor in the Department of Decision Sciences at HEC Montréal, affiliated with the University of Montréal. She holds secondary memberships in CIRRELT (Centre Interuniversitaire de Recherche sur les Réseaux d'Entreprise) and GERAD (Groupe pour la Recherche en Analyse des Décisions). Her research focuses on sequential decision-making under uncertainty, stochastic/robust optimization, and their applications in telecommunications, energy systems, and supply chain management. Education: Ph.D. in Industrial Engineering, University of Toronto (2022) M.Sc. in Computer Science, Concordia University (2016) M.Sc. in Industrial Engineering, Amirkabir University of Technology (2013) B.Sc. in Industrial Engineering & Systems Analysis, Amirkabir University of Technology (2011) Research Interests: Her work develops novel methodologies for multistage stochastic programming and robust optimization, addressing challenges in telecommunications network migration, energy systems planning, and large-scale decision-making. She bridges theoretical advances with practical applications through innovative decomposition techniques like hybrid logic-based Benders decomposition. Awards: 2023 INFORMS Telecom Best Paper Award Judith Liebman Award (2021) NSERC Discovery Grant (2024-2029) Advising & Grants: Supervises PhD/MSc students in optimization and telecommunication projects. Leads IVADO grants (e.g., $40k R3AI project) and co-leads a $100k IVADO project on net-zero power grids. Active in Mitacs industry collaborations. Professional Engagement: Serves on INFORMS committees, organizes international conferences (e.g., CORS, INFORMS Computing Society), and reviews for top journals like Operations Research and Mathematical Programming.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Qian WANG is a Lecturer at the Faculty of Law of Université de Montréal, contributing to both teaching and interdisciplinary research. Her work bridges legal education with advanced computational methods, though her recent publications primarily focus on artificial intelligence and computer science. Research interests span a broad spectrum including artificial intelligence, machine learning, natural language processing, and computer vision. Her work often intersects with systems-level challenges such as operating system control and cognitive modeling, demonstrating a unique interdisciplinary approach. Publications reflect a trajectory from foundational contributions in graph theory and saliency detection (2020-2021) to cutting-edge advancements in large language models and reasoning frameworks (2023-2025). Key themes include improving AI capabilities through dynamic reasoning mechanisms and exploring the ethical dimensions of autonomous systems. No academic awards or grants are explicitly noted in the provided materials, though her prolific publication record indicates active research engagement. She currently holds no listed advisees or doctoral supervision roles. Labs or collaborative teams associated with her research are not detailed in the available information.

Stephen Thomasمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Big Data
• +۴ مورد دیگر

Stephen W. Thomas is an Assistant Professor & Distinguished Teaching Fellow of Management Analytics at the Smith School of Business, Queen’s University. He holds a PhD in Computer Science from Queen’s University and has expertise in Natural Language Processing (NLP), machine learning, and data analytics. His research focuses on practical and fair AI applications, including bias detection and ethical considerations in NLP models. He has held roles as Executive Director of Smith's Analytics & AI Ecosystem and directs the Master of Management Analytics and Master of Management in Artificial Intelligence programs. His work bridges academia and industry, with consulting experience in big data and AI. Education: PhD (Computer Science, Queen’s University 2012), M.S. (Computer Science, University of Arizona 2009), B.S. (Computer Science, New Mexico State University 2006). Research Interests: NLP applications in messy real-world data, fair AI, AutoML for NLP, temporal databases, and mining software repositories. He has published in IEEE Transactions journals and led initiatives like τBench for temporal benchmarking. Awards: Scotiabank Scholar, multiple Professor of the Year awards, Queen’s Graduate Award Scholarship, and Raytheon Advanced Scholarship Program recipient. He has advised over 15 students in analytics and AI projects. Teaching: Courses include Machine Learning & AI, Text Analytics, Big Data, and Mathematical Analysis across Smith’s programs. He is a frequent speaker on AI ethics and management applications.

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 .

Michael Godfreyمشاهده پروفایل

استاد

• Software Engineering
• Software Evolution
• Empirical Studies
• +۵ مورد دیگر

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.

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.

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.

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.

Victor Zhongمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Reinforcement Learning
• +۳ مورد دیگر

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 Ronaghمشاهده پروفایل

دانشگاهی

• Quantum Computation
• Quantum Algorithms
• Quantum Error Correction
• +۸ مورد دیگر

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

Igor Shinkarمشاهده پروفایل

دانشیار

• Computational Complexity
• Property Testing
• Discrete Mathematics
• +۳ مورد دیگر

Igor Shinkar is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on theoretical computer science, discrete mathematics, and probability theory, with particular interests in computational complexity, property testing, and local-to-global phenomena in combinatorial objects. He holds a PhD in Mathematics and Computer Science from the Weizmann Institute of Science (2014), an MSc from the same institution (2009), and a BSc in Mathematics and Computer Science from Tel Aviv University (2005). Shinkar has taught numerous courses at SFU, including CMPT 125 (Introduction to Computing Science and Programming II), CMPT 225 (Data Structures and Programming), CMPT 405/705 (Design and Analysis of Algorithms), and CMPT 706 (Design and Analysis of Algorithms for Big Data). He has also co-taught courses at UC Berkeley and NYU, focusing on topics like coding theory and approximation algorithms. His research explores the interplay between theoretical computer science and mathematics, with contributions to areas such as randomized algorithms, approximation algorithms, and probabilistically checkable proofs. He actively seeks MSc and PhD students interested in theoretical computer science.

• Artificial Intelligence
• Multi-Agent Systems
• Preference Elicitation
• +۶ مورد دیگر

Scott Buffett is an Adjunct Professor at the Faculty of Computer Science, University of New Brunswick, and an Associate Research Officer at the National Research Council Canada (NRC), working in the Learning and Collaborative Technologies Group under the Information and Communications Technology Portfolio. He holds a PhD in Computer Science from the University of New Brunswick (UNB). His primary research focus is on artificial intelligence, particularly multi-agent systems, preference elicitation, workflow/process mining, and social commerce. He has developed systems like OmniBid, which demonstrates mechanism design for automated negotiations that balance individual utility and societal welfare. His work extends to privacy in e-commerce, data mining, and machine learning. Education: PhD (UNB), MSc in Automated Theorem Proving. Current teaching includes MBA courses in Production and Operations Management and Social Network Analysis. Past courses include Decision-Theoretic Agents and FORTRAN Programming. He supervises graduate students in preference modeling, negotiation systems, and workflow analytics. His research has been published extensively in conferences like CAI, ICEC, and journals like Electronic Commerce Research and Applications. He is active in collaborative technologies and data-driven process optimization. Research Interests: Multi-Agent Systems: Negotiation mechanisms, utility theory, and societal welfare optimization. Preference Elicitation: Techniques for extracting user preferences with minimal intrusion, including Bayesian methods and clustering. Workflow Mining: Dynamic process modeling for real-time guidance in industries like manufacturing and energy. Social Commerce: Analyzing social network effects on commercial interactions using network analysis techniques. Privacy and Data Analytics: Frameworks for privacy compliance in collaborative environments and energy management systems. Publications: Over 30 peer-reviewed articles, including work on automated negotiation, process mining theory, and preference network adaptation. Recent focus on dynamic process composition and socially aware commerce systems.

James Ian Munroمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Theoretical Computer Science
• +۱ مورد دیگر

James Ian Munro is a Professor of Computer Science at the University of Waterloo. His research focuses on algorithms, data structures, and theoretical computer science. He has been recognized with prestigious awards including the ACM Fellow (2008) and Fellowship in the Royal Society of Canada. Education: Earned degrees from the University of British Columbia, University of Toronto, and University of New Brunswick. His doctoral advisor was Allan Borodin. Research Interests: Specializes in succinct data structures, graph algorithms, search trees, and efficient algorithm design. His work emphasizes space-efficient and optimal data representation. Awards: ACM Fellow (2008) for contributions to algorithms and data structures; Royal Society of Canada Fellow. Advising: Supervised numerous doctoral students including Erik Demaine and Venkatesh Raman. His academic lineage spans foundational contributions to theoretical computer science.