جست‌وجوی استاد و پژوهشگر

• Information Retrieval
• Web Search
• Machine Learning
• +۴ مورد دیگر

Prof. Jian-Yun Nie is a Full Professor at the Université de Montréal's Faculté des arts et des sciences , within the Département d'informatique et de recherche opérationnelle . He holds a Canada Research Chair in Natural Language Information Processing and Applications. His research focuses on advancing information retrieval and web search technologies, leveraging machine learning, natural language processing, and multilingual systems. He has led numerous projects funded by grants from organizations like the CRSNG and MITACS. Key research interests include improving search algorithms through contextual understanding, diversifying query expansion using user logs and knowledge bases, and applying retrieval techniques to healthcare, e-commerce, and social media analysis. His work emphasizes practical applications, such as health goal-oriented recommendations and cross-lingual information retrieval. Grants & Projects: Building next-generation information access systems (2025–2031) Canada Research Chair (2023–2030) Computing servers for NLP applications (2022–2026) Labs & Teams: Member of RALI (Recherche appliquée en linguistique informatique) and the Laboratory for Artificial Intelligence in Cybersecurity (LIA). Collaborates on initiatives like Axel (social companion for seniors) and Myelin (AI for autism). Awards: Canada Research Chair in Natural Language Information Processing and Applications.

• 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.

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

عضو هیئت علمی

• 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

• 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.

Mohammad Masudur Rahmanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Software Engineering
• Software Maintenance and Evolution
• +۳ مورد دیگر

Mohammad Masudur Rahman is an Associate Professor in the Faculty of Computer Science at Dalhousie University. His research focuses on intelligent automation of software maintenance and evolution , combining Artificial Intelligence (AI) and Software Engineering (SE) to address challenges in bug detection, diagnosis, and reproducibility. He leads the RAISE Lab , which aligns with Dalhousie’s strategic goals in Advanced AI & Digital Innovation , particularly Sustainable Software Innovation and Sustainable AI . Dr. Rahman earned his PhD in Computer Science/Software Engineering from the University of Saskatchewan (2019), advised by Prof. Dr. Chanchal Roy, and completed a postdoc at Polytechnique Montreal under Prof. Dr. Foutse Khomh. He has published 50+ papers in top venues like ICSE , ESEC/FSE , ASE , and TOSEM , with research funded by NSERC Discovery Grant, Mitacs Accelerate International, and Dalhousie Startup Fund. His work investigates the challenges of software bugs, crashes, vulnerabilities, and technical debt , particularly in AI-driven systems like Large Language Models and Deep Learning frameworks. He develops tools to automate bug diagnosis, leveraging code structures and neural machine translation. Recent articles analyze deep learning bug reproducibility , fault diagnosis in attention models , and code smell impacts . Scientific Awards : Governor General's Gold Medal, U of S Doctoral Thesis Award, Dalhousie Belong Research Fellowship, President Gold Medal (Bangladesh). Grants : $475K+ (PI) and $4.3M+ (Co-PI) from NSERC, Mitacs, Climate Action Fund, and Dalhousie.

• Mobility Data Analytics
• Big Data
• Machine Learning
• +۸ مورد دیگر

Dr. Omid Isfahani Alamdari serves as Assistant Professor in the Master of Data Analytics program at the University of Niagara Falls Canada, bringing expertise in mobility data analytics and big data systems developed through international research experience. Education PhD in Computer Science, University of Pisa, Italy MSc in Computer Engineering - Software (Distributed Systems), Iran University of Science and Technology BSc in Computer Engineering - Software, Urmia University, Iran Research Focus His research centers on developing efficient trajectory analysis methods and advanced indexing techniques for massive mobility datasets. Key applications include sustainable transportation solutions (electric vehicle adoption, carpooling optimization) and explainable event prediction systems combining historical patterns with real-time data streams. He actively explores generative AI applications for mobility challenges and time series analysis. Publication Trends Publications from 2018-2023 reveal consistent contributions to transportation analytics, with dominant themes in trajectory processing (40%), sustainable mobility (30%), and prediction systems (30%). His work appears in top transportation venues (IEEE Transactions on ITS) and data science conferences (IEEE BigData, SIGSPATIAL), featuring strong international collaboration patterns. Academic Activities Teaching: Agile Software Development, Python for Data Analytics, SQL Databases, and Data Analytics Case Studies Research: Currently leads EU-inspired projects on sustainable mobility and event prediction Specialization: Trajectory analysis, spatio-temporal indexing, EV simulation, graph embedding