Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Data Science
• +6 more

Christian Jauvin serves as Professor of Practice in the Department of Computer Science at TÉLUQ University, with extensive experience across academic and corporate domains including public health (McGill), video games (Ubisoft), geographic information systems (K2 Géospatial), and climate modeling (Ouranos). He holds a Master's degree in Computer Science from the University of Montreal, where he contributed to foundational statistical language modeling research with Yoshua Bengio, co-authoring the seminal paper "A Neural Probabilistic Language Model". His research spans Artificial Intelligence, Machine Learning, Data Science, and Software Architecture, with specialized expertise in Geospatial Computing and Distributed Systems. He develops cloud-based distributed applications, open-source tools, and custom software solutions while critically examining technology's societal implications. Dr. Jauvin teaches programming, machine learning, large language models, databases, and critical thinking applied to technology. He actively explores distance learning methodologies and designs engaging online educational experiences, having tracked MOOC evolution since the early 2010s and currently developing an AI introductory course at TÉLUQ.

Periklis AndritsosView profile

Associate Professor

• Big Data analytics
• Cluster analysis
• Data mining
• +3 more

Periklis Andritsos is an Associate Professor at the Faculty of Information (iSchool) of the University of Toronto. He holds a PhD in Computer Science from the University of Toronto and a BSc in Electrical and Computer Engineering from the National Technical University of Athens. Prior to his return to Toronto, he held academic positions at the University of Lausanne (Switzerland), University of Trento (Italy), Free University of Bozen-Bolzano (Italy), and was a Visiting Professor at the Technical University of Berlin (Germany). His research focuses on large-scale data analysis, structure discovery, and applying information-theoretic and probabilistic techniques to identify redundancies and errors in evolving datasets. He developed the LIMBO algorithm for categorical data clustering, widely used in academia and industry. Professor Andritsos has received several awards, including the Techcrunch 50 Award (2009) as co-founder of Thoora.com and Best Paper Awards at ICPM (2021), ADBIS (2019), and DSS (2017). He teaches courses such as INF1343H (Data Modeling and Database Design) and INF2190H (Introduction to Data Analytics). His current advisee is Mirai Gendi. He is affiliated with the Data Sciences Institute at the University of Toronto and has led grants from MITACS, SSHRC, and the Data Sciences Institute. His work spans customer journey mapping, process mining, and distributed data systems. Andritsos has also contributed to industry collaborations, including co-founding Odaia Intelligence (2018–present). His media engagements include interviews on data privacy, AI ethics, and technology trends in outlets like CBC, The Toronto Star, and Voice Magazine. His research bridges theoretical foundations with practical applications in data management and analytics.

Farah ChancharyView profile

Lecturer

• Computer Science
• Cloud Computing
• Data Structures
• +7 more

Farah Chanchary is a Lecturer and Lab/Course Coordinator at the School of Computer Science, Carleton University. She specializes in areas such as data structures, cloud computing, and educational technologies. Her work spans algorithm design, privacy in advertising, and challenges in e-learning, particularly in Saudi Arabia. She coordinates laboratory and course activities, supporting academic infrastructure. Research Interests: - Data Structures and Algorithms - Cloud Computing and Migration - Privacy-Preserving Models in Advertising - E-Learning Readiness and Challenges - Machine Learning Applications (Neural Networks, Genetic Algorithms) - Geometric Query Processing Publications Trends: Her recent work focuses on efficient data structures for window queries, privacy concerns in online advertising, and cloud computing challenges. Earlier research includes studies on e-learning adoption in Saudi Arabia and neural network applications in rainfall forecasting. No awards or grants are listed. She has no documented advisees. She manages laboratory facilities and course coordination in the School of Computer Science.

Jian TangView profile

Professor

• Database Systems
• Distributed Computing
• Data Mining
• +3 more

Dr. Jian Tang is a Professor in the Department of Computer Science at Memorial University of Newfoundland. He holds a M.Sc. from the University of Iowa and a Ph.D. from Penn State University. His primary affiliation is within the Faculty of Science. His research focuses on database systems, distributed computing, data mining, and IoT security. Key projects include fault tolerance in distributed systems, workflow management, intrusion detection in IoT networks, and machine learning applications in healthcare and genomics. He has published extensively on topics like autonomous IoT security systems, feature selection methods for microarray data, and probabilistic resource scheduling in grid environments. Dr. Tang’s work bridges theoretical foundations with practical implementations, particularly in cybersecurity, bioinformatics workflows, and scalable distributed systems. His recent publications emphasize real-time autonomous IoT security solutions and machine learning-driven predictive models for healthcare outcomes.

Ihab Francis IlyasView profile

Professor

• Data Science
• Data Management
• Machine Learning for Data Quality
• +4 more

Ihab Francis Ilyas is a Professor at the University of Waterloo , affiliated with the Cheriton School of Computer Science . He currently holds the Thomson Reuters Research Chair in Data Quality and is on leave while serving as a Distinguished Engineer, Proactive Intelligence at Apple Inc. . He has co-founded two successful startups— Inductiv (acquired by Apple) and Tamr —and is a Fellow of the Royal Society of Canada , IEEE Fellow , and ACM Fellow . Research Interests : AI for Data Quality and Curation Knowledge Graphs Large-Scale Data Integration Information Extraction Managing Uncertain Data Data Cleaning Error Detection and Repair Probabilistic and Uncertain Data Management Scientific Awards and Recognitions : C.C. Gotlieb Computer Award, 2024 IEEE Fellow, 2021 ACM Fellow, 2020 NSERC-Thomson Reuters Industrial Research Chair, 2018 Google Faculty Award, 2014 Ontario Early Researcher Award, 2008 IBM CAS Faculty Fellow, 2006–2010 Taha Hussein Medal (Egyptian Ministry of Education), 1990 Leadership and Service : Board of Trustees, VLDB Endowment (2016–2021) Vice Chair, ACM SIGMOD (2016–2021) Co-founder, Inductiv (acquired by Apple) and Tamr Co-author of the leading text Data Cleaning (ACM Books) Lead developer of the HoloClean open-source data repair system Contributor to Saga , a next-generation knowledge construction platform at Apple Publications and Trends : His recent research focuses on AI-driven data cleaning, knowledge graph construction, and scalable data integration systems. Collaborative works span probabilistic inference, differentially private data synthesis, error detection, and HTAP workloads. His publications appear in top venues like SIGMOD , VLDB , and ICDE .

Cory ButzView profile

Professor

• Bayesian Networks
• Probabilistic Reasoning
• Artificial Intelligence
• +1 more

Cory Butz is a Professor in Computer Science at University of Regina, specializing in Bayesian networks and probabilistic reasoning. His research develops efficient inference algorithms using arc-reversal and variable elimination techniques. Education includes PhD in Computer Science from University of Regina. Teaches courses in database systems, AI, and programming. Research contributions span probabilistic graphical models, rough set theory, and evolutionary computation. Community outreach includes science fair judging and elementary school AI education programs. Awards: University of Regina Teaching Excellence Award (2014). Previously served as President of Canadian Artificial Intelligence Association.

Gösta GrahneView profile

Professor

• Database Theory
• Data Mining
• Graph Databases
• +4 more

Gösta Grahne is a Professor in the Department of Computer Science at Concordia University, Montreal, Canada. He holds a Ph.D. from the University of Helsinki and completed a postdoctoral fellowship at the University of Toronto. His research focuses on database theory, data mining, and systems for managing incomplete information and uncertainty. He is affiliated with the Concordia Database Systems Research Group. Education: Ph.D., University of Helsinki (1989) Postdoctoral Fellow, University of Toronto (1990–1992) Research Interests Dr. Grahne's work spans database theory , data integration , and uncertainty management . He has contributed to foundational areas such as regular path queries, XML processing, and probabilistic databases. His recent projects explore provenance tracking and formal methods for data exchange. His publications span over three decades, with notable contributions to conferences like PODS and ICDT. He maintains an active research group and collaborates internationally on theoretical and applied database challenges.

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +3 more

Dale Schuurmans is a Professor in the Faculty of Science at the University of Alberta, specializing in Computing Science. His research focuses on developing systems that learn predictive models from massive data sources, particularly in complex domains like perception, language interpretation, and bioinformatics. Education: B.Sc. Mathematics, University of Alberta (1985) B.Sc. Computing Science, University of Alberta (1986) M.Sc. Computing Science, University of Alberta (1988) Ph.D. Computer Science, University of Toronto (1996) His research interests span artificial intelligence, machine learning, reinforcement learning, probability modeling, optimization, and search algorithms. Schuurmans investigates fundamental challenges in knowledge representation for learning and navigating complex model spaces to prevent over/under-fitting. Schuurmans' recent publications demonstrate a strong focus on advancing fundamental machine learning techniques, particularly in reinforcement learning, optimization methods, and generative models. His work shows consistent emphasis on theoretical foundations of deep learning, model generalization, and efficient training algorithms. While no specific awards are mentioned, Schuurmans maintains an active research program investigating statistical natural language modeling, reinforcement learning, and learning search control. He has developed novel methods for probabilistic inference, optimization, and constraint satisfaction.

Giuliano AntoniolView profile

Associate Professor

• Software Engineering
• Software Evolution and Maintenance
• Reverse Engineering
• +5 more

Giuliano Antoniol is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He is a member of the Institute for Data Valorization (IVADO) and has established himself as a prominent researcher in software engineering, with recognition as one of the world's most productive software engineering researchers and among the top 2% most cited researchers in his field. Professor Antoniol's research spans several key areas in software engineering, with a particular focus on software evolution and maintenance, reverse engineering, static code analysis, and empirical software studies. His work frequently addresses practical challenges in software development, including software quality assurance, technical debt identification, and the application of machine learning techniques to software engineering problems. His research group has produced significant contributions to understanding code smells, identifier naming practices, and the impact of programming language features on software quality. His recent publications reveal a strong trend toward the intersection of traditional software engineering with artificial intelligence and machine learning. Many of his 2023-2025 publications focus on the challenges of testing and verifying machine learning systems, analyzing bugs in AI-generated code, and applying search-based techniques to complex software engineering problems. His work demonstrates a consistent empirical approach, with numerous studies analyzing real-world software systems and developer practices. Ranked among the world's most productive software engineering researchers (2021) Among the top 2% most cited researchers in his field (2021) Award for Most Influential Article of the Decade in Software Engineering (2021) Professor Antoniol has supervised an impressive number of graduate students, with 14 completed PhD theses and 18 Master's theses to his credit. His students have explored diverse topics including software testing, technical debt, machine learning applications in software engineering, and code quality analysis. His research has been supported by numerous grants that have enabled extensive empirical studies and the development of innovative software engineering tools and techniques.

Gabriel SpadonView profile

Assistant Professor

• Spatiotemporal Analytics
• Time-Series Forecasting
• Complex Network Mining
• +13 more

Gabriel Spadon is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the MAPS Lab (Modeling and Analytics on Predictive Systems), focusing on spatiotemporal analytics, network science, and machine learning. His work is part of the Big Data Analytics, AI & Machine Learning research cluster. Education: PhD in Computer Science and Computational Mathematics, University of São Paulo (USP), 2021 MSc in Computer Science and Computational Mathematics, University of São Paulo (USP), 2017 BSc in Computer Science, São Paulo State University (UNESP), 2015 Research Interests: Gabriel Spadon’s research lies at the intersection of data science, machine learning, and geoinformatics . He specializes in spatiotemporal forecasting , complex network mining , and trajectory modeling , particularly in maritime and urban environments. His work integrates physics-informed AI, graph-based learning, and environmental data to improve predictive accuracy and decision support. Key domains include vessel movement prediction, environmental monitoring, and urban intelligence. Recent Publication Trends: His latest publications (2024–2025) focus on maritime mobility , using AIS data and physics-informed neural networks (PINNs) for vessel trajectory forecasting. He applies deep learning, clustering, and probabilistic fusion to model complex, multi-modal movement patterns. Other themes include fishing activity detection, port network analysis, and AI for public health and climate matching. Scientific Awards: Advising and Grants: Dr. Spadon supervises multiple graduate students and research assistants in the MAPS Lab, working on mobility data mining, physics-informed AI, and geospatial analytics. His research is supported by major grants from the Ocean Frontier Institute (OFI) , Canadian Space Agency (CSA) , Mitacs , and IMT Atlantique . He leads projects such as smartWhales , AISViz , and Physics-Informed Mobility Forecasting , involving international collaborations with institutions in Brazil and France. Labs and Teams: He is the Director of the MAPS Lab , which develops cutting-edge computational methods for predictive systems. The lab collaborates with the smartWhales initiative (DHI, WSP, Fisheries and Oceans Canada), MERIDIAN , and the Institute for Big Data Analytics . His team includes PhD and MCS students working on AI, data mining, and signal processing for real-world ocean and urban challenges.

• Spatiotemporal Data Management
• Spatial Crowdsourcing
• Routing Algorithms
• +3 more

Mario Nascimento is a Professor at the University of Alberta 's Department of Computing Science within the Faculty of Science. He previously served as department Chair from 2014-2021 (with a 2019-2020 leave) and holds affiliations with institutions including the National University of Singapore , Aalborg University , and others. His current leave-of-absence status allows focusing on specific research initiatives.

Niv DayanView profile

Assistant Professor

• Database Systems
• Data Structures
• Algorithms
• +6 more

Niv Dayan is an Assistant Professor in the Department of Computer Science at the University of Toronto, holding this position since 2023. His research focuses on designing efficient data structures and algorithms for modern hardware, with applications in databases, cryptography, machine learning, and virtual reality. He collaborates closely with industry partners to bridge theoretical advancements to real-world systems. Education: PhD in Computer Science (IT University of Copenhagen, 2015), MSc in Software Development (IT University of Copenhagen, 2012), BSc in Computing and Economics (University of Dundee, 2010). Prior to his faculty position, he held roles as a Research Scientist at Pliops (2019–2022) and postdoctoral researcher positions at Harvard (2015–2019) and Copenhagen University (2022). Research Interests: Data structures for modern hardware, LSM-trees, key-value stores, probabilistic filters (e.g., Bloom, Cuckoo), and adaptive storage systems. His work emphasizes practical implementations and rigorous experimentation, addressing challenges in scalability, efficiency, and robustness. Teaching: Instructors for courses CSC443/CSC2234 (Database System Technology) and CSC2525 (Research Topics in Database Management), emphasizing hands-on projects, paper presentations, and research-oriented learning. Courses require strong programming skills in C/C++, Rust, or Java. Awards: Received the Best of SIGMOD 2017 award for the paper Monkey: Optimal Navigable Key-Value Store . His work has been cited as one of the best four papers at conferences, reflecting its impact on database systems research. Lab and Collaborations: Leads a research lab focused on advancing database system fundamentals. Collaborates with industry on projects like Pliops' storage solutions and startup partnerships to ensure practical relevance of his research.

Ladan TahvildariView profile

Professor

• Software Engineering
• Cloud Computing
• Self-Adaptive Systems
• +5 more

Ladan Tahvildari is a Full-time Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. Her research focuses on software engineering, self-adaptive systems, cloud computing, and test case prioritization. She leads the Software Technologies Applied Research Laboratory (STAR Lab), emphasizing hands-on industry collaboration and practical software solutions. Her work spans over two decades, with notable contributions to adaptive software systems, runtime adaptation frameworks (GRAF), and cloud modeling (StratusML/Adoop). She has pioneered techniques for flaky test detection (FlaKat), spatiotemporal auto-scaling (STaleX), and POMDP-based uncertainty management for security systems. Key research areas include: Self-protecting software systems Component-based software evolution Defect detection and prioritization Autonomic computing decision models Cloud infrastructure optimization Her recent work bridges academia and industry through hands-on learning approaches and frameworks like Semeru Cloud Compiler for performance enhancement. She has served as workshop chair for ACSOS 2023 and contributed to IBM tool integration in educational curricula. Her lab focuses on transforming theoretical concepts into practical tools like StarMX for self-managing systems and ReLACK for VoIP steganography. Current efforts emphasize adaptive machine learning frameworks and scalable microservice architectures.

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.

• Encrypted Search Security
• Cryptanalysis
• Privacy-Preserving Systems
• +1 more

Abdelkarim Kati is a Sessional Lecturer and Postdoctoral Scholar at the University of Waterloo's Cheriton School of Computer Science. His research analyzes security vulnerabilities in encrypted search systems, developing frameworks like MAPLE (Markov-based leakage attacks) to evaluate real-world data risks. As a PartTime Lecturer, he contributes to computer science education while advancing cryptanalysis methodologies. Kati's work establishes standardized evaluation models for leakage attacks, emphasizing practical threats to encrypted databases. His publications include systematic reviews and attack prototypes addressing query recovery and privacy breaches.