Find Academic Supervisors

Aaron SmithView profile

Associate Professor

• Applied Probability
• Computational Statistics
• Monte Carlo Methods
• +3 more

Aaron Smith is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa, affiliated with the Faculty of Science. He holds a PhD from Stanford University. His research focuses on applied probability, computational statistics, Monte Carlo methods, and Markov chains, with an emphasis on advancing theoretical understanding and practical applications of these methodologies. Dr. Smith's work includes contributions to community detection algorithms, Markov chain mixing times, and synthetic health data generation. His recent publications explore topics such as nonstandard Dirichlet form representations, perturbation analysis of MCMC algorithms, and sparse Bayesian multidimensional scaling. He advises students in applied probability and has supervised postdoctoral researchers in related fields. His research interests span a wide range of topics, including stochastic processes, statistical inference, and algorithm design. He is particularly known for his analysis of convergence rates in Markov chains and the development of efficient sampling techniques for complex models. His interdisciplinary work bridges theoretical mathematics and practical computational challenges in data science and healthcare. Dr. Smith collaborates on projects involving synthetic data frameworks for privacy-preserving applications and has contributed to foundational work on mixing times and perturbation effects in stochastic systems.

Yujie TangView profile

Assistant Professor

• Internet of Vehicles
• Resource Management in Heterogeneous Networks
• Machine Learning
• +7 more

Dr. Yujie Tang is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Canada, where she has been serving since September 2022. Prior to this, she was an Assistant Professor at Algoma University (2019–2022) and a Post-Doctoral Fellow at the University of Waterloo (2017–2019). Her academic journey includes a PhD from the University of Waterloo and earlier degrees from Harbin Institute of Technology and Lanzhou Jiaotong University. PhD – University of Waterloo (2017) M.E. – Harbin Institute of Technology, Shenzhen, China B.E. – Lanzhou Jiaotong University, Lanzhou, China Her research focuses on intelligent networking and computing technologies for future IoT and 5G/6G systems. Key areas include Internet of Vehicles (IoV), AI-empowered edge computing, resource management in heterogeneous networks, software-defined networking, and UAV-assisted communications. She employs machine learning and optimization techniques to design energy-efficient and high-performance network protocols. The most recent publications reflect a strong trend in applying AI and machine learning to solve complex problems in vehicular networks, edge caching, and spectrum management. Her work spans top-tier IEEE journals such as IEEE Transactions on Vehicular Technology , IEEE Internet of Things Journal , and IEEE JSAC , with a clear emphasis on real-world deployable solutions for next-generation wireless systems. Faculty Research Startup Fund, Dalhousie University, 2022 NSERC Discovery Grant, 2021–2026 Algoma University Research Fund, 2021 Faculty Research Startup Fund, Algoma University, 2019 Best Speaker Award, University of Waterloo, 2017 Faculty of Engineering Award (4 times), University of Waterloo, 2013–2017 University of Waterloo Graduate Scholarship, 2013–2015 International Doctoral Student Award, 2012–2016 Graduate Research Studentship (twice), 2011–2012 Provost Doctoral Entrance Award for Women, 2011 Dr. Tang actively supervises graduate and undergraduate students and has secured competitive research grants, including the NSERC Discovery Grant. She serves on the technical program committees of major IEEE conferences such as INFOCOM, GLOBECOM, and ICC, and regularly reviews for top journals like IEEE JSAC , IEEE TWC , and IEEE TVT . She currently leads a research group focusing on B5G/6G networks, IoV, and edge computing, and she is actively recruiting new students and visiting scholars. Her research group operates within the Faculty of Computer Science at Dalhousie University, where she leads projects in intelligent resource management, AI-driven networking, and integration of space-air-ground networks. She is a member of IEEE, IEEE Communications Society, and IEEE Vehicular Technology Society.

Minsu KimView profile

Research Fellow

• Artificial Intelligence
• Deep Learning
• Combinatorial Optimization
• +7 more

Minsu Kim is a CIFAR AI Safety Post-doc Fellow at KAIST and Mila, collaborating with Prof. Yoshua Bengio, Prof. Sungjin Ahn, and Prof. Sungsoo Ahn. His work bridges System 2 Deep Learning, Bayesian posterior inference, and combinatorial optimization. Ph.D., Industrial Engineering, KAIST (2025) M.S., Electrical Engineering, KAIST (2022) B.S., Mathematics and Computer Science (Dual Degree), KAIST (2020) Kim's research focuses on enabling AI systems to measure uncertainty, represent causality, and perform sequential reasoning for safety-guaranteed planning. His methodology integrates GFlowNets and diffusion models with off-policy amortized inference, targeting applications in scientific discovery , hardware design optimization , and large language model alignment . Recent work explores Bayesian posterior inference through GFlowNets, aiming to unify deep learning with probabilistic reasoning. His 15 most recent publications (2025–2024) reveal a trend of combining combinatorial optimization with generative models for tasks like molecular graph discovery, vehicle routing, and neural architecture search. He also investigates diffusion samplers for Bayesian inverse problems and symmetry-based neural methods (Sym-NCO) to enhance sample efficiency. Jang Yeong Sil Fellowship (2025) KAIST Presidential Best Ph.D. Thesis Award (2025) Qualcomm Innovation Fellowship (2023) DesignCon Best Paper Awards (2021–2022) Kim's collaborations span KAIST's Industrial Engineering department and Mila's AI research groups. He actively contributes to academic peer review for top conferences (NeurIPS, ICML, ICLR) and journals (IEEE TNNLS, TPAMI), emphasizing the intersection of AI safety , uncertainty quantification , and systemic reasoning .

Qizhen ZhangView profile

Professor

• Hyperscale Data Processing
• Cloud Computing
• Network-Centric System Design
• +4 more

Qizhen Zhang is a Professor in the Department of Computer Science at the University of Toronto's Faculty of Arts and Science. Specializing in hyperscale data processing systems, Zhang leads research at the intersection of cloud computing, distributed systems, and data center networking. Recent work focuses on network-centric designs for efficient large-scale data processing, including pioneering contributions to disaggregated data center architectures. Research interests center on hyperscale data processing , network-aware system design , and disaggregated infrastructure . Current projects investigate DPU-accelerated systems (dpBento, DPDPU), memory disaggregation (Cowbird, Redy), and blockchain scalability (FlexChain). Zhang's approach systematically integrates network characteristics into distributed system optimization, addressing challenges in trillion-item workloads through novel shuffle layers (TeShu) and compute pushdown mechanisms (TELEPORT). Zhang advises multiple graduate students working on satellite networking (SaTE), federated learning, and DPU-optimized storage (DDS). Professional service includes program committees for SIGMOD, VLDB, NSDI, and EuroSys (2023-2026), plus journal reviews for ACM TODS and IEEE/ACM Transactions on Networking. Industrial collaborations with Microsoft Research have yielded production-oriented systems like Redy and CompuCache. Key contributions include MimicNet for scalable network simulation (SIGCOMM 2021), GraphRex for network-aware graph processing (SIGMOD 2019), and foundational work on disaggregated data centers (CIDR 2020, VLDB 2020). Teaching responsibilities encompass graduate courses CSC2235 (Cloud-native Data Management) and undergraduate CSCC43 (Databases) at the University of Toronto.

Olga VekslerView profile

Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +6 more

Olga Veksler is a Professor at the University of Waterloo's Department of Computer Science, part of the Faculty of Mathematics. She holds a Ph.D. and M.Sc. from Cornell University (1999) and a B.A. from New York University (1995). Her research focuses on computer vision, machine learning, and discrete optimization, with notable contributions to image segmentation, graph algorithms, and deep learning integration. Her work emphasizes semantic segmentation, salient object detection, and efficient optimization techniques for graphical models. Education: Ph.D. in Computer Science, Cornell University, 1999 M.Sc. in Computer Science, Cornell University, 1999 B.A. in Computer Science, New York University, 1995 Her research explores intersections between machine learning and traditional computer vision challenges, particularly leveraging graph-based optimization and CRF models. Recent trends in her work include weakly supervised learning, sparse non-local CRF applications, and test-time adaptation strategies for salient object detection. She has pioneered methods for shape priors in multi-object segmentation and efficient graph-cut algorithms. Her advising and grant activities are foundational to her research, though specific grant details are not listed here. She maintains a lab focused on advancing computer vision through algorithmic innovation, with contributions to both theoretical frameworks and practical applications in medical imaging and scene understanding.

Mohammadreza KaramadView profile

Assistant Professor

• Electrochemistry
• Heterogeneous Catalysis
• Material Science
• +3 more

Mohammadreza Karamad is an Assistant Professor in the School of Sustainable Energy Engineering at Simon Fraser University (SFU), with a joint appointment in the Sustainable Energy Engineering department. His research focuses on computational materials discovery, leveraging quantum-mechanical methods (e.g., DFT) and machine learning (ML) to design advanced energy materials for clean technologies like hydrogen storage and catalysis. He holds a Ph.D. from the Technical University of Denmark (DTU) and completed postdoctoral research at Stanford University. His academic background includes leadership roles in the CMD Lab (Computational Materials Discovery), where he explores novel materials for electrochemical energy conversion processes. Key research areas include electrochemistry, heterogeneous catalysis, and material science, with a particular emphasis on CO2 reduction, ammonia synthesis, and sustainable energy storage solutions. Dr. Karamad collaborates with industry and academic partners to advance materials discovery through high-throughput computational screening and AI-driven approaches. He actively seeks motivated students (undergraduate and graduate) to join his research program, focusing on developing next-generation energy materials. His lab is located in room B8220, and he can be reached at mkaramad@sfu.ca. Notable technical contributions include pioneering work on transition metal nitrides for CO2 reduction, single-atom catalysts for ammonia synthesis, and machine learning frameworks for predicting material properties. His research bridges fundamental theory with practical applications, addressing global challenges in sustainable energy and environmental technology.

• Wireless Communications
• Signal Processing
• Physical Layer Security
• +3 more

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

Olivia Di MatteoView profile

Assistant Professor

• Quantum Computing
• Quantum Software Engineering
• Quantum Compilation
• +5 more

Olivia Di Matteo serves as an Assistant Professor in the Department of Electrical and Computer Engineering within UBC's Faculty of Applied Science, leading the Quantum Software and Algorithms Research (QSAR) group since her January 2022 appointment. Her academic foundation includes a BSc from Lakehead University and MSc/PhD in Physics (Quantum Information) from the University of Waterloo, completed in 2019. Dr. Di Matteo's research centers on quantum software engineering , with pioneering work in quantum compilation , circuit optimization , and debugging tools . She champions open-source quantum frameworks and develops accessible educational resources to democratize quantum computing. Analysis of her 15 most recent publications (2021-2025) reveals dominant trends in quantum programming infrastructure, particularly circuit analysis (33%), bug classification (20%), and qubit network optimization (15%), with strong emphasis on practical software tooling over theoretical physics. No scientific awards were documented in the source materials. She advises graduate students in the QSAR group while contributing to open-source quantum ecosystems through projects like PennyLane and The Ionizer transpiler, and teaches courses including CPEN 400Q (Gate-model quantum computing) and ELEC 221 (Signals and Systems). The QSAR group operates at the intersection of quantum software development and education, focusing on making quantum programming accessible through visual tools, real-time debugging environments, and hardware-agnostic compilation techniques.

Minyi HuangView profile

Professor

• Mean Field Stochastic Control
• Stochastic Algorithms in Multi-Agent Systems
• Wireless Networks
• +4 more

Minyi Huang is a Professor in the School of Mathematics and Statistics at Carleton University. His research focuses on Mean Field Stochastic Control, Stochastic Algorithms in Multi-Agent Systems, and Wireless Networks. He holds a Ph.D. from McGill University (2003) and has held postdoctoral positions at the University of Melbourne and the Australian National University. Dr. Huang is a Fellow of IEEE and a Member of SIAM. Education: Ph.D. in Electrical and Computer Engineering, McGill University (2003) M.Sc. in Systems and Control, Chinese Academy of Sciences (Beijing) B.Sc. in Mathematics, Shandong University (Jinan, China) Research Interests: Huang's work centers on stochastic control, mean field games, and multi-agent systems. His contributions include theoretical advancements in mean field social optimization, graphon-based control frameworks, and applications in wireless networks and economic models. He has organized workshops on Mathematical Cybernetics and Stochastic Processes, fostering interdisciplinary collaboration. Scientific Awards: Fellow of the IEEE Advising & Grants: Huang has advised numerous graduate students on topics in stochastic control and mean field theory. His grants include funding for international PhD students through Carleton's initiatives. He collaborates on projects involving mean field models for production output and social dynamics. Labs/Teams: Associated with the Ottawa-Carleton Institute for Mathematics and Statistics (OCIMS), contributing to collaborative research in control theory and applied mathematics.

M. Tamer ÖzsuView profile

Professor

• Data Science
• Database Systems
• Distributed Data Management
• +3 more

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.

Benjamin AlarieView profile

Professor

• Tax Law
• Artificial Intelligence and Law
• Legal Theory
• +3 more

Benjamin Alarie is a Professor and the Osler Chair in Business Law at the University of Toronto Faculty of Law. He is a leading scholar at the intersection of artificial intelligence, law, and taxation, and the co-founder and CEO of Blue J, an AI-powered tax research platform used by over 3,000 organizations across North America and the UK. Education: LL.M. – Yale Law School (2003) J.D. – University of Toronto (2002), with honours M.A. – University of Toronto (2002) B.A. – Wilfrid Laurier University (1999), with high distinction Research Interests: Alarie’s research spans tax law , legal theory , judicial behaviour , and the applications of machine learning in law . He introduced the concept of the “legal singularity”—the idea that legal systems may evolve toward greater clarity and predictability through AI and computational methods. His work explores how AI can enhance legal reasoning, regulatory design, and tax compliance. He is particularly focused on the ethical and practical implications of generative AI in legal and tax practice, including liability, transparency, and the future role of human practitioners in AI-augmented systems. Scientific Awards: PROSE Award (2024) for The Legal Singularity Donner Prize shortlist (2024) for The Legal Singularity vLex Fastcase 50 Honoree (2023) Top 50 Changemaker, The Globe and Mail (2022) Alan Mewett QC Prize for Excellence (2009) Advising & Grants: Alarie is a frequent advisor to courts, government agencies, and global professional networks. His work at Blue J involves strategic partnerships with CPA.com, CPA Canada, the National Association of Tax Professionals, Big 4 accounting firms, and Tax Notes. He also serves as a faculty affiliate at the Vector Institute for AI and the Schwartz Reisman Institute for Technology and Society. Labs & Teams: As CEO of Blue J, Alarie leads a multidisciplinary team of legal scholars, engineers, and data scientists. The platform is used by tax authorities and accounting firms to deliver precise, defensible, and efficient tax research and decision-making tools.

Piotr ZwiernikView profile

Associate Professor

• Statistical Computing
• Theoretical Statistics
• High-dimensional Statistics
• +7 more

Piotr Zwiernik is an Associate Professor in the Department of Statistical Sciences at the University of Toronto's Faculty of Arts and Science, with a cross-appointment in the Department of Mathematics. Currently on leave from the University of Toronto, he is based in Barcelona following his return in July 2025. His academic journey includes a PhD in Statistics from the University of Warwick (2011), research positions at prestigious institutions including Mittag Leffler Institute, IPAM, TU Eindhoven, UC Berkeley, and the University of Genoa, and an Assistant Professorship at Universitat Pompeu Fabra in Barcelona (2016-2021). His research spans the intersection of statistics, mathematics, and computational methods, with particular emphasis on graphical models, covariance matrix estimation, convex analysis, tensors, and algebraic and combinatorial methods in statistics. Zwiernik's work demonstrates a consistent focus on high-dimensional statistics, mathematical statistics, and elegant theoretical frameworks that bridge abstract mathematics with practical statistical applications. His recent publications reveal a deepening exploration of tensor analysis, algebraic statistics, and the geometric properties of statistical models. Zwiernik serves as an associate editor for leading journals including Biometrika, Scandinavian Journal of Statistics, and Algebraic Statistics. His research program includes the development of the GOLAZO R package for asymmetric regularization of log-likelihood in Gaussian graphical models. As an academic leader, he has served as Associate Chair for Research in his department and actively participates in numerous international conferences and workshops, reflecting his significant standing in the statistical community. His recent publications show a strong trend toward algebraic and geometric approaches to statistical problems, with increasing focus on tensor methods, positivity constraints in statistical models, and the theoretical foundations of graphical models. The work demonstrates remarkable continuity in exploring the mathematical structures underlying statistical models while adapting to emerging challenges in high-dimensional data analysis. Zwiernik is committed to mathematical accessibility and education, guided by Federico Ardila's four axioms which emphasize equitable distribution of mathematical potential, joyful mathematical experiences, mathematics as a malleable tool, and treating every student with dignity and respect. He actively seeks PhD students with strong mathematical backgrounds for research at UPF or the Institute of Mathematics of UPC.

Mahmoud El-SakkaView profile

Associate Professor

• Image Compression
• Medical Imaging
• Image Segmentation
• +5 more

Mahmoud El-Sakka is an Associate Professor at the Department of Computer Science, University of Western Ontario since 1999. Previously, he was a faculty member at the University of Waterloo (1997–1999). He holds a B.Sc. and M.Sc. from Alexandria University (Egypt) and a Ph.D. in Systems Design Engineering from the University of Waterloo. His research focuses on medical imaging, image processing, and computer-aided diagnostics. He has served as Chair of the graduate program (2002–2007) and undergraduate program (2017–present) in Computer Science at Western Ontario. El-Sakka is a Senior Member of the IEEE and a licensed Professional Engineer in Ontario. His work spans grants from NSERC, internal university funding, and industry collaborations. Major research areas include image compression, segmentation, and medical applications like vascular analysis and echocardiography. He has led over 20 funded projects since 1999, emphasizing interdisciplinary approaches in healthcare technology. Academic contributions include advisory roles in summer programs, thesis evaluations, and conference participation. His service includes roles as Pro-Chancellor at convocations and involvement in equipment purchasing committees. Collaborations include consulting with NCR Canada and VRP Web Technology.

Mahesh TripunitaraView profile

Professor

• Information Security
• Authorization and Access Control
• Cryptographic Key Transport
• +7 more

Dr. Mahesh Tripunitara is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, serving as Associate Chair for Undergraduate Studies. He holds a PhD (2005) and Master's (1995) in Computer Science from Purdue University, along with a BSc (1993) in Computer Science from Dalhousie University. His research focuses on information security, authorization mechanisms, cryptographic key management, and hardware security, with industry experience at Motorola's R&D labs and Silicon Valley. His work spans theoretical advancements like access control policy analysis and practical applications such as secure payments systems and IoT device reliability. Notable awards include the Best Student Paper at Usenix Security 2013 and Best Paper at ACM SACMAT 2013. He actively serves on program committees for major security conferences including CCS, CODASPY, and SACMAT. Recent publications highlight innovations in cellular security (SUCI-Catchers defense), role-mining optimization, and blockchain smart contract auditing. Teaching includes advanced algorithm design courses (ECE 406/606) and digital computation (BME 121). His research emphasizes balancing security rigor with usability in authorization systems and hardware protection mechanisms.

Soosan BeheshtiView profile

Professor

• Signal Processing
• Statistical Signal Processing
• Information Theory
• +6 more

Soosan Beheshti is a Professor and Program Director in the Department of Electrical, Computer, and Biomedical Engineering at Toronto Metropolitan University. She holds a B.S. from Isfahan University of Technology and M.S./Ph.D. from MIT. Her research focuses on signal processing, statistical learning, and information theory, with applications in biomedical systems, data denoising, and system modeling. She has received awards such as the Dean's Teaching Award (2010) and the EECS Carlton E. Tucker Award (1998). Education: B.S., Electrical Engineering, Isfahan University of Technology (1996) M.S. & Ph.D., Electrical Engineering, MIT (2002) Research Interests: Statistical Signal Processing Information Theory Data Denoising & Compression System Modeling & Control Machine Learning Applications Awards: Dean's Teaching Award (2010) Gold Paper Award (PacRim 2009) Best Paper Award (Remote Sensing 2008) MIT Teaching Excellence Award (1998) Teaching: Courses include Signals and Systems, Control Systems, and Statistical Inference. She has supervised numerous graduate students and postdocs in her Signal and Information Processing (SIP) Lab. Labs/Teams: Director of the SIP Lab, conducting research in signal processing, information theory, and biomedical applications. Collaborates with industry partners like Myant Inc. and Huawei Technologies.