Florian Kerschbaum is a Professor and NSERC/RBC Industrial Research Chair in Data Security at the Cheriton School of Computer Science, University of Waterloo. His research focuses on data security and privacy, applied cryptography, and confidentiality in data science. Research interests span data collection/preparation management, secure multi-party computation, homomorphic encryption, differential privacy, and machine learning robustness/privacy. His work develops cryptographic solutions for practical data management challenges in distributed systems.
Shai Ben-David is a Professor and University Research Chair at the Department of Computer Science, University of Waterloo. He is affiliated with the Cheriton School of Computer Science and can be reached at shai@uwaterloo.ca . His office is located in DC 2643. Education: Ph.D., Hebrew University, Jerusalem, Israel (1987) M.Sc., Hebrew University Jerusalem, Israel (1979) B.Sc., Hebrew University Jerusalem, Israel (1978) Research interests focus on foundational aspects of machine learning theory, including unsupervised learning (clustering), domain adaptation, fairness, interpretability, and alternative approaches to worst-case computational complexity. He also explores logic applications in computer science theory. His research trends emphasize theoretical challenges in machine learning, particularly clustering, fairness in representations, and the interplay between computational feasibility and learnability. He investigates how unlabeled data and sample compression techniques impact learning robustness and efficiency. No scientific awards are listed. His advising record shows no formal advisees listed here. Grants and funding details are not provided in the text. He has contributed to organizing events like the Dagstuhl Seminar on Foundations of Unsupervised Learning (2017) and co-edited MFCS 2016 proceedings. His work addresses both theoretical questions and practical gaps in ML implementation.
Reihaneh Rabbany is an Assistant Professor at the School of Computer Science, McGill University, and a core faculty member of Mila - Quebec's artificial intelligence institute. She holds the Canada CIFAR AI Chair and is affiliated with the Center for the Study of Democratic Citizenship. Her research focuses on complex data analysis at the intersection of network science, data mining, and machine learning. Research Interests: Network Science Data Mining Graph Representation Learning Unsupervised and Self-supervised Learning Anomaly Detection Social Good Applications Publication Trends show emphasis on temporal graph analysis, community detection, misinformation identification, and interdisciplinary collaborations with political science and criminology experts. Notable Awards Canada CIFAR AI Chair CAIAC 2021 Best Master's Thesis Award (co-supervisor) Advising includes mentoring PhD and MSc students across multiple institutions, with graduated students transitioning to roles at Microsoft Research, Mila, Yale, and Google. Labs & Collaborations: Leads the Complex Data Lab at McGill, collaborates with Mila, and contributes to community evaluation frameworks like CommunityEvaluation and TopLeaders algorithm.
Renée J. Miller is a Professor and Canada Excellence Research Chair in Data Intelligence at the Cheriton School of Computer Science, University of Waterloo. Her research focuses on data integration, data management, and open data systems. She holds a PhD in Computer Science from the University of Wisconsin-Madison and bachelor’s degrees in Mathematics and Cognitive Science from MIT. Her work addresses challenges in data preparation, integration, and curation, aiming to reduce the burden on data scientists. She co-authored foundational papers on data exchange and schema mapping, earning the ICDT Test-of-Time Award (2013) and the Alonzo Church Award (2020). Miller has led major initiatives like the NSERC Business Intelligence Network and the International Very Large Data Base Foundation. Her grants include NSERC Accelerator Awards and funding from IBM, SAP, and Microsoft. Notable students include Ariel Fuxman (SIGMOD Dissertation Award winner) and Oktie Hassanzadeh (IBM PhD Fellow). Her research group, the Miller Lab, develops tools like Clio for schema mapping and systems for data lake exploration (RONIN, JOSIE).
John Zelek is an Associate Professor in the Department of Systems Design Engineering at the University of Waterloo. He co-directs the VIP (Vision & Image Processing) lab and previously served as Associate Graduate Chair (2013-2017). He co-founded two startups: Tactile Sight (haptic navigation for disabled individuals) and Sweep3D (3D modeling technology). His research focuses on autonomous robotics, 3D scene understanding, infrastructure assessment, medical imaging, and sports analytics using AI/deep learning techniques. Education includes a BASc from Waterloo (1985), MASc from Ottawa (1989), and PhD from McGill (1996). He teaches courses like SYDE 283 (Physics), SYDE 572 (Pattern Recognition), and SYDE 675 (Pattern Recognition). Research interests span robotics, computer vision, anomaly detection, and SLAM. His work applies to infrastructure monitoring, sports analytics (hockey/pitcher analysis), medical imaging (OCT/fundus), and assistive technologies. Recent publications emphasize 3D modeling, SLAM enhancements, and sports tracking algorithms. Zelek advises graduate students (SSPS status) and collaborates with companies like Intelligent Health Solutions and EyeCheck through advisory roles. Key innovations include hybrid SLAM systems, puck localization algorithms, and medical robotic swab systems demonstrated on moving phantoms.
Professor Diana Inkpen is a faculty member at the School of Electrical Engineering and Computer Science, University of Ottawa. She holds a Ph.D. from the University of Toronto's Department of Computer Science and degrees from the Technical University of Cluj-Napoca, Romania (M.Sc. and B.Eng.). Her research focuses on computational linguistics, natural language processing (NLP), and artificial intelligence, with specialties in natural language understanding/generation, lexical semantics, and semantic web agents. Education: Ph.D., Computer Science, University of Toronto M.Sc., Computer Science and Engineering, Technical University of Cluj-Napoca B.Eng., Computer Science and Engineering, Technical University of Cluj-Napoca Affiliations: Director, NLP Lab Editor-in-Chief, Computational Intelligence journal Associate Editor, Natural Language Engineering journal Her research has led to roles such as program co-chair for AI 2012 and leadership in organizing international workshops. She has received funding from NSERC, SSHRC, and OCE, and was honored with a Visiting Professor title at the University of Wolverhampton, UK. Her teaching spans courses like Information Retrieval, Natural Language Processing, and Prolog programming. Professor Inkpen's work bridges NLP innovations with societal challenges, including mental health surveillance via social media analysis and bias mitigation in AI systems. She actively contributes to interdisciplinary projects, such as detecting hate speech and legal text entailment, while maintaining a global research network through collaborations and international conference involvement.
Stephen A. Vavasis is a Professor in the Department of Combinatorics and Optimization at the University of Waterloo, part of the Faculty of Mathematics. He holds a PhD in Computer Science from Stanford University (1989) and has held academic positions at Cornell University (1989–2006) before joining Waterloo. His research focuses on continuous optimization, data science, first-order methods, scientific computing, and computational mechanics. Current teaching includes courses on convex optimization and portfolio optimization methods. He has served as Associate Dean of Computing (2017–2020) and Interim Director of Data Science graduate programs. His work is supported by NSERC grants. Notable awards include the Hertz Fellowship, Churchill Scholarship, and Guggenheim Fellowship. Vavasis's research emphasizes applications of optimization to clustering, machine learning, and fracture mechanics. His publications span convex optimization frameworks, algorithmic analysis of gradient methods, and numerical methods in mechanics. Recent work explores unifying analyses of first-order optimization algorithms and robust optimization techniques for high-dimensional data problems.
Geoff Pleiss is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC), affiliated with CAIDA's AIM-SI cluster. He is also a Canada CIFAR AI Chair and faculty member at the Vector Institute. His research bridges deep learning and probabilistic modeling, focusing on uncertainty quantification, Bayesian optimization, Gaussian processes, and ensemble methods. Pleiss earned his PhD in Computer Science from Cornell University (2020), followed by a postdoc at Columbia University. He holds multiple awards, including the AISTATS Top Reviewer and NeurIPS recognitions. His work emphasizes scalable algorithms and open-source contributions, such as the GPyTorch library. Pleiss advises students in Computer Science and Statistics, including Donney Fan (PhD), Tim G. Zhou (MSc), and others. He teaches advanced courses like STAT 547U (Deep Learning Theory) and STAT 520P (Bayesian Optimization). Grants include NSERC Discovery and New Frontiers in Research funding. Pleiss collaborates on interdisciplinary projects, such as astrophysical discovery via machine learning, and actively participates in academic service and outreach. Education: PhD in Computer Science, Cornell University (2020) MSc in Computer Science, Cornell University (2018) BSc in Engineering (Computing with Applied Mathematics), Olin College (2013) Key Research Themes: Uncertainty-aware decision-making with neural networks Scalable Gaussian processes and Bayesian optimization Ensemble methods and their theoretical limitations Recent Grants: NSERC Discovery Grant (2024) New Frontiers in Research Fund (2025, co-PI) His publications span foundational theory to applied machine learning, with over 14,500 citations. He actively mentors students through research internships and advises on open-source software development. Pleiss frequently presents at top conferences and collaborates with industry partners like Microsoft and ASAPP.
Marco Pedersoli serves as an Assistant Professor at École de technologie supérieure (ETS) in Montreal since February 2017, where he leads research in computer vision and machine learning. His work focuses on reducing computational costs and annotation requirements for deploying vision algorithms on embedded devices, positioning ETS at the forefront of Montreal's AI ecosystem. His academic journey includes: Ph.D. from Autonomous University of Barcelona (UAB) under Jordi Gonzàlez and Juan José Villanueva Post-doctoral research at INRIA Grenoble with Cordelia Schmid and Jakob Verbeek (2015-2016) Research at KU Leuven with Tinne Tuytelaars (2012-2015) Dr. Pedersoli's research tackles deep learning bottlenecks through weakly-supervised methodologies and computational efficiency innovations . His three core projects address: Reduced Supervision : Developing weakly/semi-supervised learning for images, video, audio and text Exploration Learning : Optimizing data selection in unstructured environments Efficient Computation : Accelerating deep learning training and inference These efforts enable vision algorithms to run on resource-constrained portable devices. Publication trends (2014-2022) reveal consistent focus on weak supervision (60% of works) and computational efficiency (30%), with recent expansion into medical imaging and multimodal emotion recognition. Key venues include CVPR, ICCV, NeurIPS and ECCV. His accolades include: Best Paper Award at ICIAR 2019 NVIDIA Titan X Pascal hardware donation Dr. Pedersoli actively mentors 18 graduate students across PhD and MSc programs, with notable placements at Huawei and Radio Canada. His lab secures competitive tax-free funding for projects with international collaborations, including Element AI and European institutions. Current openings emphasize Python/C++ proficiency and deep learning expertise. He leads a dynamic research group at ETS developing open-source tools for Roi-Pooling, weakly-supervised detection, and 3D object recognition, maintaining active GitHub repositories with community contributions. Recent WACV 2023 acceptances demonstrate ongoing productivity following medical leave.
Georges Kaddoum is a Professor at École de technologie supérieure (ÉTS) , specializing in Electrical Engineering. He holds the Canada Research Chair in Unlocking the Power of IoT 6G-Networks and the LACIME – Communications and Microelectronic Integration Laboratory affiliation. His work bridges wireless communications, IoT, and machine learning. Research Interests include wireless communication systems, physical layer security, machine learning for networking, and 6G technologies. He focuses on optimizing network performance in challenging environments (impulsive noise, underwater, non-terrestrial networks) and developing AI-driven solutions for jamming mitigation, resource allocation, and secure IoT frameworks. Recent Publications highlight 6G-enabled vehicular networks, quantum-safe blockchain integration, federated learning for transportation systems, and deep learning-based receivers for chaotic communication systems. His work emphasizes semantic communication, interference management, and digital twin applications. Awards IEEE TCSC Award for Excellence in Scalable Computing (2022) Prix d’excellence de la relève (Université du Québec, 2018) Prix d’excellence en recherche (ÉTS, 2018) Multiple IEEE Exemplary Reviewer and Best Paper Awards (2014–2022) Supervision includes 15+ PhD/Master’s students working on topics like index modulation, physical layer security, UAV communications, and intelligent resource management. Research Units Ultra Research Chair on Intelligent Tactical Wireless Networks LACIME Laboratory Canada Research Chair in IoT 6G-Networks
Dr. Saeed Gazor is a full Professor in the Department of Electrical and Computer Engineering at Queen's University. He holds a cross-appointment in the Department of Mathematics and Statistics. His research focuses on signal processing applications in electrical energy systems, communications, and medical imaging. He has supervised postdoctoral fellows Babak Ghaffari and Yaser Esmaeili Salehani. Professional affiliations include Senior Member IEEE and membership in the Institution of Engineering and Technology. Education: PhD (1994) in Signal and Image Processing from Télécom ParisTech; M.Sc. (1989) and B.Sc. (1987) from Isfahan University of Technology with highest honors. Academic roles include former Assistant Professor at Isfahan University of Technology (1995–1998) and research associate at University of Toronto (1999). Research interests span detection theory, smart energy systems, hyperspectral imaging, and medical signal processing. Notable contributions include innovations in radar signal processing, sparse signal reconstruction, and adaptive filtering. Active in academic service, including editorial roles in IEEE journals. Awards: Professional Engineer designation from Professional Engineers Ontario. Over 200 peer-reviewed publications with recent focus on AI-driven hyperspectral analysis, robust beamforming, and energy-efficient communication systems. Labs/Teams: Leads signal processing research initiatives at Queen's, collaborating on projects involving smart energy grids, distributed radar networks, and biomedical signal analysis. Current work emphasizes integrating deep learning with traditional signal processing techniques.
Anup Basu is a Professor in the Department of Computing Science at the University of Alberta. His research focuses on computer graphics, computer vision, and multimedia communications. He holds an B.S. in Math & Statistics from the Indian Statistical Institute (1980), an M.E. in Computer Science from the Indian Statistical Institute (1983), and a Ph.D. in Computing Science from the University of Maryland (1990). His work emphasizes Quality of Service (QoS) in multimedia delivery for e-commerce and telelearning, adaptive bandwidth monitoring, and 3D visualization tools. He pioneered foveated image compression and stereo visualization techniques, contributing to MPEG-4 coding standards. He leads major initiatives like the ASRA/TelePhotogenics/IBM 3D Medical Imaging project ($2M+ funding) and developed patented SHR Stereo/3D scanning technologies. Awards include the American Neurological Association Fellowship. He has held leadership roles as General Chair for IEEE International Conferences on SMC (2017), Multimedia & Expo (2013), and SMC (2014). His research integrates interdisciplinary collaborations across universities and industry partners, leveraging advanced equipment like the CAVE system for immersive visualization.
Hassan Z. Ashtiani is an Associate Professor in the Department of Computing and Software within the Faculty of Engineering at McMaster University. His academic profile shows consistent engagement in both teaching and research activities, with evidence of active participation in major machine learning conferences and journals through 2025. Dr. Ashtiani's research focuses on the theoretical foundations of machine learning, with particular expertise in privacy-preserving algorithms, Gaussian mixture models, and adversarial robustness. His work bridges statistical learning theory with practical algorithm design, often addressing fundamental questions about sample complexity and computational efficiency in learning systems. A significant portion of his recent work explores the intersection of differential privacy with statistical learning, developing methods for private density estimation and distribution learning. Analysis of his publication record reveals a strong trend toward increasingly sophisticated theoretical frameworks for private and robust learning. His work consistently appears in top-tier venues including NeurIPS, ICML, COLT, and ALT, with recent contributions extending into agnostic private density estimation and robust learning with tolerance. The research demonstrates progression from foundational work on nearest neighbor search and clustering algorithms toward more complex problems in private learning of high-dimensional distributions. Dr. Ashtiani teaches across multiple levels of computer science education, including undergraduate courses in Automata and Computability (COMPSCI 2AC3) and Principles of Programming (COMPSCI 2S03), as well as graduate-level courses such as Fundamentals of Machine Learning (COMPSCI 4ML3) and Theoretical Foundations of Unsupervised Learning (CAS 775). His teaching portfolio shows consistent involvement in machine learning education since at least 2019, with evidence of teaching multiple sections each academic year. His scholarly impact is reflected in mentions across 3 news outlets, reference in 1 policy source, engagement from 7 X users, and 90 readers on Mendeley, suggesting growing recognition of his contributions to theoretical machine learning.
Joerg Sander is a Professor and Chair of the Department of Computing Science at the University of Alberta's Faculty of Science. His research focuses on knowledge discovery in databases, particularly density-based clustering (e.g., DBSCAN, OPTICS, HDBSCAN*) and outlier detection (e.g., LOF). He is a leading contributor to foundational algorithms in data mining, including the DBSCAN paper which received the 2014 SIGKDD Test-of-Time Award. Education: M.A., Philosophy of Science (University of Munich, 1989) Diploma in Computer Science (University of Munich, 1996) Ph.D., Computer Science (University of Munich, 1998) Research Interests: Design and theoretical analysis of clustering algorithms Outlier detection methodologies Spatial and high-dimensional data mining Algorithm scalability and visualization Key Contributions: DBSCAN (density-based spatial clustering of applications with noise) OPTICS (ordering points to identify the clustering structure) LOF (local outlier factor) Awards: SIGKDD Test-of-Time Award (2014)
Dr. Ting Hu is an Associate Professor in the School of Computing at Queen's University, affiliated with the Faculty of Arts and Science. She leads the Machine Intelligence & Biocomputing (MIB) Laboratory, focusing on bio-inspired AI and bioinformatics. Her research bridges evolutionary computing, machine learning, and biomedical data analysis. Dr. Hu holds a PhD in Computer Science from Memorial University and completed postdoctoral training at Dartmouth College. She teaches courses with strong student evaluations, winning the Howard Staveley Teaching Award (2019-2020) and recognition as a Mental Health Champion (2023). Education: B.Sc. in Computational Mathematics, Wuhan University M.Sc. in Computer Science, Wuhan University PhD in Computer Science, Memorial University Postdoctoral Fellowship, Geisel School of Medicine, Dartmouth College Research Interests: Evolutionary algorithms and genetic programming Interpretable and explainable AI Biomedical data mining (metabolomics, genomics) Complex network analysis Applications in precision medicine and disease prediction Awards & Recognition: Queen's AMS Undergraduate Mentorship Award (2025) IEEE CIBCB Best Student Award (2022) Howard Staveley Teaching Award (2019-2020) NSERC Discovery Grant Reviewer (2019) Memorial University's Best Professor Award (2016) Lab & Collaborations: MIB Lab develops tools like geneDRAGNN (graph neural networks for gene-disease prioritization) Active roles in IEEE Computational Intelligence Society and EuroGP Advances include vaccination strategies via graph-RL and interpretable clustering methods
