جستجوی استاد راهنمای دانشگاهی

• Large-Scale Data Management
• Data Systems
• Big Data Analysis
• +۵ مورد دیگر

Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto, specializing in large-scale data management, data systems, and applied machine learning. His research integrates machine learning techniques into scalable data platforms to enhance the analysis of massive datasets. He holds a PhD from the University of Toronto, an MSc from the University of Maryland, and a Bachelor's degree from the University of Patras. Education: PhD, University of Toronto MSc, University of Maryland at College Park Bachelor's degree, University of Patras, Greece Research Interests: Relational Deep Dive (ReDD): Natural language query execution over unstructured documents Streaming Video Queries (SVQ): Interactive query processing for video streams Reliable Text-to-SQL: Generating accurate SQL queries with human-in-the-loop assistance Machine Learning Integration in Data Systems Publications: Focus on video analytics, query processing, and reliable natural language interfaces. Notable works include optimizing video queries, declarative frameworks for temporal constraints, and abstention-based SQL generation. Awards: Inventor of the Year (1st Prize), University of Toronto (2011) Best Paper Awards at international conferences Entrepreneurship & Advising: Co-founder of Sysomos (Meltwater Group), Aislelabs (Constellation Software), and Workorb Advisor to mapintent and ktau Labs & Teams: Leads research groups developing systems like ReDD and SVQ, emphasizing collaboration between academia and industry.

Miao Zhengjieمشاهده پروفایل

استادیار

• Database Systems
• Data Management
• Data Curation and Integration
• +۳ مورد دیگر

Miao Zhengjie serves as an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), joining in October 2023 after a research scientist position at Megagon Labs. His work centers on enhancing data science pipelines through innovations in database systems and artificial intelligence. His academic foundation includes: Ph.D. in Computer Science from Duke University (2022) M.S. in Computer Science from Columbia University (2016) B.S. in Computer Science and Technology from Peking University (2015) Dr. Miao's research spans Database Systems , Data Management , Data Curation , and Data Provenance , with emphasis on AI-driven solutions for data pipeline efficiency. His methodology bridges theoretical database concepts with practical data science applications through novel algorithm development. Analysis of his 15 most recent publications reveals persistent focus areas: query explanation systems (35% of works), data augmentation frameworks (27%), and human-AI collaboration tools (20%). These contributions appear consistently in premier venues including SIGMOD, VLDB, and CHI, demonstrating methodological evolution from foundational query debugging (2019) to LLM-integrated annotation systems (2024). He actively participates in the SFU Data Science Research Group , contributing to interdisciplinary initiatives in large-scale data processing. Current information indicates no formal advisees or grant details are publicly documented in his institutional profile.

• Data Systems for AI
• Retrieval-Augmented Generation (RAG)
• Database Systems
• +۴ مورد دیگر

Yongjoo Park is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering. He leads research in data-intensive AI systems as a member of the Data and Information Systems (DAIS) lab, focusing on novel data systems that bridge database theory and practical AI applications. His work emphasizes open-source contributions through GitHub and direct societal impact. Research interests center on systems for data-intensive AI , particularly efficient Retrieval-Augmented Generation (RAG) systems for exploratory AI, data science versioning, and in-storage computing. Key projects include Kishu (the world's first undoable Jupyter notebook with time-travel capabilities), CARE (a causal-relational system for structured/unstructured data), and AirDB/AirIndex (serverless transactions and automatic index optimization). His group develops tools enabling scalable, optimized AI workflows from storage layers to LLM inference. Recent publications reveal a strong focus on interactive data systems (85% of recent work), with significant contributions to notebook environments (Kishu), vector databases (ISCA'25), and RAG optimization. Awards highlight technical innovation, including SIGMOD 2025 Best Demo Award and NSF CAREER funding. His open-source philosophy drives GitHub releases of all major systems. SIGMOD 2025 Best Demo Award (Kishu) NSF CAREER Award (Novel data science systems) SIGMOD'23 Best Artifact Award Honorable Mention (DeepOLA) IBM-Illinois Project Selection (VectorDB/RAG) Mentorship spans 12 current PhD/MS students and 6 graduated advisees, including Supawit Chockchowwat (now Postdoc at Google, future Assistant Professor at CMKL University). He teaches advanced courses like CS511 (Advanced Data Management) and recruits 1-2 new PhD students annually, prioritizing data systems research. His lab emphasizes diversity, individual respect, and concrete outcomes in a collaborative workspace.

M. Tamer Özsuمشاهده پروفایل

استاد

• Data Science
• Database Systems
• Distributed Data Management
• +۳ مورد دیگر

M. Tamer Özsu is a University Professor of Computer Science at the David R. Cheriton School of Computer Science, University of Waterloo, where he holds a Cheriton Faculty Fellowship. He also serves as a Distinguished Visiting Professor at Tsinghua University and is the Founding Director of Waterloo-Huawei Joint Innovation Laboratory since 2018. His extensive contributions to computing have earned him numerous prestigious awards including the 2024 ACM Presidential Award for long-standing and significant contributions to the computing field. Professor Özsu's research focuses on data engineering aspects of data science, particularly addressing data management issues with two main foci: management of non-traditional data and large-scale distributed data management. He is renowned for his seminal book "Principles of Distributed Database Systems" (co-authored with Patrick Valduriez), now in its fourth edition, and the "Encyclopedia of Database Systems" (co-edited with Ling Liu), in its second edition. His work bridges theoretical foundations with practical system implementations, targeting grand societal challenges through computational approaches. His recent publications reveal a strong trend toward graph analytics, streaming data processing, and the integration of large language models with vector data management. The research shows increasing focus on GPU-accelerated graph processing, RDF query optimization, and multimodal data analysis, reflecting the evolution of data management challenges in the era of big data and AI. His work continues to address fundamental challenges in distributed data systems while adapting to emerging technologies and application domains. Scientific Awards and Fellowships ACM Presidential Award (2024) IEEE TCDE Education Award (2024) IEEE Innovation in Societal Infrastructure Award (2022) CS Can | Info Can Lifetime Achievement Award (2018/2019) ACM SIGMOD Test-of-Time Award (2015) ACM SIGMOD Contributions Award (2006) The Ohio State University College of Engineering Distinguished Alumnus Award (2008) Fellow of the Royal Society of Canada Fellow of the American Association for the Advancement of Science (AAAS) Life Fellow of the Association for Computing Machinery (ACM) Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) Elected member of the Science Academy, Türkiye Professor Özsu has been deeply involved in academic leadership and community building. As Founding Editor-in-Chief of ACM Books (2013-2019), he launched a series that by 2019 had published 28 major books with another 30 under contract. His service to ACM, particularly through SIGMOD, has been exemplary and widely recognized. He directs the Waterloo-Huawei Joint Innovation Laboratory, which focuses on cutting-edge research in data management and distributed systems, fostering strong industry-academia collaboration.

Rachel Pottingerمشاهده پروفایل

استاد

• Data Management
• Database Systems
• Data Integration
• +۷ مورد دیگر

Rachel Pottinger is a Professor in the Department of Computer Science at the University of British Columbia within the Faculty of Science. She has been at UBC since 2004, progressing from Assistant Professor to Associate Professor in 2012 and to full Professor in 2021. She is affiliated with research centers including CAIDA (Centre for Artificial Intelligence Decision-making and Action) and DFP (Designing for People), and is part of ICICS (Institute for Computing, Information and Cognitive Systems). Her research focuses on data management, particularly semantic data integration, metadata management, and making data more accessible and understandable to users. She leads the Data Management and Mining Lab and has supervised numerous doctoral and master's students. Her work addresses three main areas: helping people understand and explore their data, managing data not well supported by databases, and coordinating data across multiple databases. Her recent publications demonstrate strong trends in database usability, data provenance visualization, query recommendation systems, and building information modeling integration. Her work bridges theoretical database concepts with practical human-centered applications, particularly in making complex data systems more accessible to non-expert users. UBC Computer Science Department Faculty Teaching Award 2013 Computer Science Department Teaching Award 2010 CS Department Teaching Award Denice Denton Emerging Leader Award 2007 Pottinger has supervised numerous PhD and Master's students, with research focusing on data provenance, database usability, and data coordination. She has been involved in significant research projects related to data lakes, open data navigation, and query recommendation systems. Her current research explores table annotation and discovery in data lakes, query refinement for aggregation queries, and query prediction based on past user behavior. She is actively involved in the academic community, serving as Secretary-Treasurer for SIGMOD, on the VLDB Journal editorial board, and as a member of the Computing Research Association's Board of Directors. She previously served as General Co-Chair of SIGMOD 2020 and as Associate Head for the Undergraduate Program of the Department of Computer Science from 2018-2020.

• Artificial Intelligence
• Computer Gaming and Animation
• Computer Graphics
• +۴ مورد دیگر

Yingying Wang is an Assistant Professor in the Computing and Software department at McMaster University , where she joined in January 2022. Her research focuses on generating expressive animations for AR/VR applications and games through interdisciplinary approaches combining Computer Graphics , Artificial Intelligence , and Human Behavior Analysis . Education : Bachelor and Master degrees from Nanjing University , Ph.D. from University of California, Davis (2017) Her research explores: Generative models for human motion style transfer Physics-based motion simulation Audio-driven character synthesis Dance choreography for virtual characters Cartoon animation perception Conversational character gesture synthesis Markerless hand motion capture Recent publications focus on 3D hand pose estimation , motion style transfer , gesture-locomotion coordination , and personality perception in virtual agents . Key methodologies include deep learning , multimodal data analysis , and real-time animation systems . Scientific contributions recognized through: $240,000 Labarge Catalyst Grant in Mobility in Aging (interdisciplinary team award) US Patent 10,796,482 (3D hand pose estimation) US Patent 9,811,937 (gesture-locomotion coordination) Teaching includes graduate and undergraduate courses in Computer Animation (CAS 737), Computer Graphics (COMPSCI 3GC3/SFWRENG 3GC3), and Software Development (COMPSCI 2ME3). Research group actively recruits Ph.D. and Master's students in graphics + deep learning domains.

• Large-Scale Data Management
• Data Systems
• Big Data Analysis
• +۵ مورد دیگر

Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto. His research focuses on large-scale data management, integrating machine learning into data systems, and developing efficient query processing techniques for unstructured and streaming data. He holds a PhD from the University of Toronto, an MSc from the University of Maryland at College Park, and a Bachelor's from the University of Patras in Greece. Research interests include data systems, big data analysis, video query processing, and natural language interfaces for databases. He leads projects like ReDD (Relational Deep Dive), SVQ (Streaming Video Queries), and Reliable Text-to-SQL, aiming to bridge human-readable queries with database execution. His work emphasizes scalability, intelligence, and real-world applicability. Recipient of the University of Toronto's Inventor of the Year Award (2011), he translates research into startups like Sysomos, Aislelabs, and Workorb. His contributions span over 200 publications in top venues such as SIGMOD, VLDB, and ICDE. Courses taught include advanced data systems, database design, and system internals. Current projects explore schema extraction from unstructured data, video query optimization, and cost-effective machine learning pipelines. Collaborations with industry and academic partners drive innovations in both theory and practical applications.

Randy Harrisمشاهده پروفایل

استاد

• Rhetoric
• Computational Rhetoric
• Cognitive Linguistics
• +۸ مورد دیگر

Randy Harris is a Professor in the Department of English Language and Literature at the University of Waterloo, with a cross-appointment to the David Cheriton School of Computer Science. His career spans over three decades, focusing on the intersection of rhetoric , cognitive science , and computational linguistics . He holds a PhD in Communication and Rhetoric from Rensselaer Polytechnic Institute, with postdoctoral experience at the University of Alberta. Education : PhD (Rensselaer), MSc (Rensselaer, Alberta), MA (Dalhousie), BA (Queen's) Research Interests : Cognitive Rhetoric, Computational Rhetoric, Rhetorical Figures, Construction Grammar, Rhetoric of Science, and Neurocognitive Stylistics. His work explores how rhetorical figures like chiasmus and antimetabole reflect brain structure and cognitive processing. Recent projects involve building the Rhetoricon , a collocational database of rhetorical figures, and investigating their role in large language models . He has received numerous awards, including the University of Waterloo Arts Award for Excellence in Research (2023) and Fellow of the Royal Society of Canada (2022) . Harris has supervised graduate students in professional communication , linguistics , and science writing . His grants include NSERC Discovery Horizon (2024-2029) and multiple SSHRC grants for Computational Rhetoric and Rhetorical Figure Ontology . He actively organizes interdisciplinary workshops like Computing Figures and contributes to media discussions on language, AI, and rhetoric.

Mostafa Milaniمشاهده پروفایل

استادیار

• Data Management
• Databases
• Data Cleaning
• +۷ مورد دیگر

Mostafa Milani is an Assistant Professor in the Department of Computer Science at Western University. His research focuses on data management, databases, and their applications in data cleaning, privacy, provenance, and fairness. Before joining Western, he held postdoctoral positions at the University of British Columbia and McMaster University, and earned his Ph.D. from Carleton University under Dr. Leopoldo Bertossi. Education: Ph.D. in Computer Science from Carleton University (supervised by Leopoldo Bertossi), Postdoctoral Fellowships at University of British Columbia and McMaster University. Research Interests: Data Quality, Privacy, Provenance, Fairness, Entity Matching, Query Optimization, and Database Systems. His work emphasizes ethical data practices and integrates machine learning for improved database interactions. He has contributed to projects like Building Trust in Data (privacy/fairness integration) and Unified Data Exploration (provenance and query recommendations). Courses taught include Databases I/II, Applied Logic, and Web Systems. Current advisees include 7 MSc and 1 PhD student. Former students have graduated across MSc and undergraduate programs. His research is supported by grants and collaborations, and he actively participates in program committees for top conferences like SIGMOD and VLDB.

Amine Mhedhbiمشاهده پروفایل

استادیار

• Database Management
• Graph-Structured Databases
• Query Optimization
• +۴ مورد دیگر

Amine Mhedhbi is an Assistant Professor at Polytechnique Montréal in the Department of Computer Engineering and Software Engineering. He is affiliated with the Institute for Data Valorization (IVADO) and the Software Engineering for Machine Learning Applications (SEMLA) group. His research focuses on data management systems, particularly graph-structured databases, multimodal data engineering, and AI-driven query optimization. Ph.D. in Computer Science from University of Waterloo Former technical advisor to enterprise companies Prior applied research leadership at Distyl AI and internships at Microsoft Research His recent work explores integrating large language models (LLMs) into database systems, optimizing SQL generation, and advancing graph database architectures. Key projects include GraphflowDB and FlockMTL , addressing scalability and declarative semantic applications. Scientific awards include: NSERC Discovery Grant with Discovery Launch Supplement (2025) Cheriton School Distinguished Dissertation Award (2024) Microsoft Research Ph.D. Fellowship (2020) VLDB Best Paper Award (2018) He supervises graduate students in database systems and machine learning applications and serves on program committees for top-tier conferences like VLDB and SIGMOD.

Andriy Miranskyyمشاهده پروفایل

دانشیار

• Applied Machine Learning
• Big Data
• Blockchain
• +۴ مورد دیگر

Dr. Andriy Miranskyy is an Associate Professor in the Department of Computer Science at Toronto Metropolitan University. His research focuses on applied machine learning, quantum computing, cloud computing, and software engineering, with notable contributions to anomaly detection in cloud systems and quantum software engineering. He leads the AMiR Lab, exploring risk mitigation and software development challenges in emerging technologies. Education: Ph.D. in Computer Science from The University of Western Ontario (2011). Research Interests: He investigates quantum software engineering methodologies, cloud-native systems governance, and big data applications. His work bridges theoretical advancements with industrial-scale implementations, such as IBM Db2 quantum safety case studies and cloud monitoring tools like CloudHeatMap. He emphasizes practical solutions for flakiness detection in quantum programs and sustainable software development practices. Awards: Recognitions include the Rogers Cybersecure Catalyst Fellowship (2023-2024), IBM CAS Best Project (2021), and a Guinness World Record for pioneering a 3Pb data warehouse (IBM DB2 team). Teaching: Teaches courses like CPS 840 (Quantum Computing), CPS 847 (Software Tools for Startups), and CPS 731 (Software Engineering). Labs/Teams: Directs the AMiR Lab, focusing on risk-aware software engineering in quantum and cloud domains.

Randy Allen Harrisمشاهده پروفایل

استاد

• Rhetoric
• Computational Rhetoric
• Cognitive Rhetoric
• +۵ مورد دیگر

Randy Allen Harris is a Professor cross-appointed between the Department of English and the David Cheriton School of Computer Science at the University of Waterloo. His academic journey includes a PhD in Communication and Rhetoric from Rensselaer Polytechnic Institute (1991), an MSc in Technical Communication (Rensselaer), an MSc in Experimental Linguistics (University of Alberta), an MA in English Literature (Dalhousie University), and a BA in English Literature (Queen's University). His research explores the intersection of rhetoric, cognition, and computation, with focus areas including: Cognitive and computational rhetoric examining how rhetorical figures reflect neurocognitive structures Construction Grammar and its relationship to rhetorical schemes Rhetoric of science and scientific argumentation Development of rhetorical ontologies for machine learning applications Voice interaction design and conversational interfaces Harris's publications demonstrate sustained focus on rhetorical theory, computational linguistics, and cognitive approaches to persuasion. His recent work increasingly engages with machine learning applications for rhetorical analysis and the development of rhetorical databases. Earlier foundational work established frameworks for understanding scientific argumentation and the cognitive dimensions of rhetorical figures. Significant honors include: Fellow of the Royal Society of Canada (2022) University of Waterloo Arts Award for Excellence in Research (2023) Award of Excellence in Graduate Supervision (2019) Multiple SSHRC and NSERC grants supporting computational rhetoric research He directs the RhetFig computational rhetoric project and has secured substantial funding including: NSERC Discovery Horizon grant (2024-2029) SSHRC Insight Grants (2018-2027) Mitacs Accelerate Grant (2009) Harris supervises graduate students in rhetoric, professional communication, linguistics, and cognitive poetics, and maintains collaborations with international researchers in computational linguistics and argumentation studies.

Randy Fortierمشاهده پروفایل

دانشگاهی

• Software Security
• Web/Mobile Development
• Computer Graphics
• +۳ مورد دیگر

Randy Fortier is an Associate Teaching Professor and Associate Dean of Science (Undergraduate) at Ontario Tech University's Faculty of Science. He specializes in Computer Science education with a focus on software security, web/mobile development, graphics, and game development. His academic career includes teaching roles at the University of Windsor (1997-2003) and Thompson Rivers University (2003-2013). Education: BSc (Honours Computer Science, Software Development) from University of Windsor (1997), MSc (Computer Science) from University of Windsor (2003). Research interests span software security, mobile/web development, computer graphics, and game development. His earlier work focused on natural language processing and distributed virtual environments. His publications explore semantic web technologies, mobile speech interfaces, and scalable distributed systems architectures. Teaching portfolio includes over 15 courses ranging from introductory programming to advanced topics like interactive media and simulation modeling. He currently holds administrative leadership in undergraduate science education.

Giuseppe Careniniمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• Human-Computer Interaction
• +۴ مورد دیگر

Giuseppe Carenini is a Professor in the Department of Computer Science at the University of British Columbia (UBC), holding the role of Director of the UBC Master of Data Science program. He is affiliated with the Faculty of Science and leads multiple research groups including the UBC NLP Group, CANARY, IUI Group, and CAIDA. His research focuses on intersections between Artificial Intelligence, Computational Linguistics, and Human-Computer Interaction, with notable contributions to discourse analysis, summarization, and interactive visualization. He has been recognized with awards such as the SIGDIAL Best Paper Award and ACM TiiS Best Paper Award. His work spans academic leadership, course instruction (e.g., CPSC courses on AI, NLP), and collaborations with industry and sustainability initiatives (e.g., IRES, Data Science Institute). His projects often integrate multimodal data analysis, decision support systems, and adaptive interfaces. Research Interests: - Artificial Intelligence: User Modeling, Preference Elicitation, Decision Theory. - Computational Linguistics: Discourse Analysis, Summarization, Argumentation. - Human-Computer Interaction: Intelligent Interfaces, Information Visualization. Publications highlight advancements in neural discourse parsing, topic segmentation, and LLM applications in healthcare and operations research. His work bridges theoretical contributions with practical tools like CONVisIT and T³-Vis for data exploration and model interpretation.

Adam Fourneyمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Human-Computer Interaction
• Multi-Agent Systems
• +۳ مورد دیگر

Adam Fourney is a Senior Principal Researcher at Microsoft Research's Human-AI eXperiences (HAX) group in Redmond. He holds a Ph.D. and M.Math. from the University of Waterloo and a B.Sc. from the University of Ottawa. His work focuses on AI agents collaborating with humans to complete complex tasks, including projects like AutoGen and Magentic-One. He previously studied web search, intelligent assistants, and information systems at the University of Waterloo. Education: Ph.D. (Computer Science), University of Waterloo M.Math. (Computer Science), University of Waterloo B.Sc. (Computer Science), University of Ottawa His research interests span Human-AI collaboration, multi-agent systems, AI ethics, and AI-assisted programming. He explores how AI systems can support human tasks while addressing challenges like uncertainty, communication breakdowns, and alignment with human goals. His work bridges technical innovation with user-centered evaluation, emphasizing real-world usability and societal impact. His recent articles highlight advancements in interactive AI debugging, alignment of AI tools with human needs, and leveraging search data for public health insights. Notably, he received a Best paper honorable mention at CHI 2024 for work on AI-assisted programming costs. Adam has collaborated extensively with researchers across Microsoft and academia, contributing to open-source tools like AutoGen Studio and foundational studies in human-LLM interactions. His interdisciplinary approach combines technical rigor with insights from user behavior and social dynamics.