Find Academic Supervisors

Bijan RaahemiView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +5 more

Professor Bijan Raahemi is a Full Professor at the Telfer School of Management, University of Ottawa, and Director of the Digital Transformation and Innovation (DTI) Graduate Program. He holds a Ph.D. in Electrical and Computer Engineering from the University of Waterloo (1997), an M.Sc. from Sharif University of Technology, and a B.Sc. (Hon.) from Isfahan University of Technology. His academic roles include cross-appointment to the School of Electrical Engineering and Computer Science. Research interests span Artificial Intelligence, Machine Learning, Data Mining, Big Data Analytics, and their applications in Business, Healthcare, and Engineering. He leads the KDD Research Lab, focusing on data-driven solutions for business and technical challenges. His work has been funded by NSERC, MITACS, and industry partners like Bell Canada and Ciena. Awarded the Telfer School of Management Research Excellence Award (2018), Raahemi has published over 85 peer-reviewed articles and holds eight patents in data communications. He teaches courses such as Business Data Mining, Business Intelligence Technologies, and Management Information Systems. Industrial experience includes roles at Nortel, Alcatel-Lucent, and Tropic Networks, focusing on telecommunications and network architecture. Professional memberships include IEEE (Senior Member), ACM, and Professional Engineers of Ontario (PEO).

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +9 more

Ioannis Mitliagkas is an Associate Professor at the University of Montréal's Department of Computer Science and Operations Research, affiliated with the Faculty of Arts and Science. He is a core faculty member at Mila and a staff research scientist at Google DeepMind. His research focuses on machine learning, deep learning, optimization algorithms, and generative models. He holds the Canada CIFAR AI Chair and has led numerous projects on distributed training, causal discovery, and robust generalization. Education: While specific academic degrees are not explicitly listed in the text, his roles imply a PhD in Computer Science. His work bridges theoretical foundations and applied machine learning. Research Interests: Machine learning theory, optimization dynamics, generative models, out-of-distribution generalization, and causal inference. His work emphasizes scalable algorithms for deep learning and distributed systems. Awards: Canada CIFAR AI Chair (2018). Grants & Projects: Lead researcher on over 50 projects funded by CRSNG, MITACS, and others, including grants on optimization dynamics, causal discovery, and industrial applications (e.g., healthcare, energy, robotics). Students: Advised PhD and Master's students on topics like distributional shifts, generative models, and performative prediction.

Peter Allan BuhrView profile

Professor

• Concurrency
• Polymorphism
• Concurrent Monitoring
• +4 more

Peter Allan Buhr is a Professor at the School of Computer Science, University of Waterloo. His primary research focuses on programming languages, concurrency, polymorphism, and tools for monitoring and debugging concurrent systems. He is known for developing μC++ and C∀, extensions to C and C++ that enhance concurrency and type safety. Buhr's work includes contributions to memory-mapped databases (μDatabase), debugging tools (MVD), and high-performance web server architectures. His research interests include concurrency control, lock-free algorithms, type systems, and persistent data structures. He has advised numerous graduate students on topics like C∀ type systems, concurrency patterns, and real-time scheduling. Buhr has published extensively in top-tier journals such as ACM Transactions on Parallel Computing and Concurrency and Computation: Practice and Experience. He leads the C∀ open-source project, aiming to modernize the C language while maintaining backward compatibility. His work on μC++ has been used in undergraduate concurrency courses at Waterloo for decades. Buhr's contributions to concurrency education and research have had a global impact, with tools and frameworks adopted widely in academia and industry.

Nikolas KnowlesView profile

Assistant Professor

• Biomechanics
• Orthopedic Engineering
• Bone Mechanics
• +3 more

Nikolas Knowles is an Assistant Professor at the University of Waterloo, specializing in biomechanics with a focus on orthopedic applications. His work integrates advanced imaging techniques (e.g., CT, HR-pQCT) with computational modeling to study bone mechanics, osteoarthritis progression, and surgical outcomes. Key research areas include finite element analysis of bone structures, material-property relationships in trabecular bone, and the biomechanical impacts of musculoskeletal injuries and treatments. His research emphasizes the development of validated computational models (e.g., QCT-based FE models) to predict bone stiffness and failure patterns, particularly in joints like the shoulder and knee. He investigates how imaging modalities can improve clinical understanding of bone degradation (e.g., subchondral cyst formation, bone mineral density changes) and guide surgical interventions such as shoulder arthroplasty or ACL reconstruction. Recent work addresses the limitations of conventional imaging in capturing post-injury bone marrow changes, advocating for dual-energy CT and high-resolution techniques. Dr. Knowles collaborates on interdisciplinary projects involving biomechanical testing, digital volume correlation (DVC), and multi-platform computing to handle large-scale simulations. His studies often combine experimental validation with computational methods to enhance the accuracy of bone modeling for clinical applications. While no formal student advisement records are listed, his publications reflect collaborations across engineering, orthopedics, and imaging sciences. Awards remain unspecified in the provided materials.

Tarek AbdelrahmanView profile

Professor

• Parallel Systems
• Parallelizing Compilers
• Optimizing Compilers
• +3 more

Professor Tarek Abdelrahman is affiliated with the University of Toronto as a faculty member in the Department of Electrical & Computer Engineering under the Faculty of Applied Science and Engineering . He earned his PhD in Computer Science and Engineering from the University of Michigan at Ann Arbor in 1989. Research Interests : Parallel Systems Parallelizing and Optimizing Compilers Parallel Programming Models and Support Software Engineering Computer Architecture Distributed Computing Scientific Awards and Recognitions : Jeffery P. Skoll Chair in Software Engineering (2002-2012) Best Paper Awards (2006, 2001) at Parallel and Distributed Computing and Systems Sustained Excellence in Teaching Award (2013) from the Faculty of Applied Science and Engineering ECE Departmental Teaching Awards (2004, 2006, 2007, 2010, 2011) Faculty Teaching Award (2008) from the Faculty of Applied Science and Engineering Professional Memberships : Senior Member of ACM and IEEE; Member of IEEE Computer Society, USENIX Computing Society, and Professional Engineers of Ontario (PEO).

Yuan DingView profile

Professor

• Software Systems
• Distributed Systems
• Large-scale Software Reliability
• +3 more

Professor Yuan Ding is a Canada Research Chair in Systems Software at the University of Toronto , affiliated with the Faculty of Applied Science and Engineering and serving as Chair of the Computer Engineering Group . He joined the Department of Electrical and Computer Engineering in 2013 and was promoted to Professor in 2023. Education: PhD in Computer Science (2012) from University of Illinois at Urbana-Champaign, B.E. in Computer Science and Engineering and B.S. in Mathematics (2006) from Beihang University Research Interests focus on improving the reliability and performance of large-scale software systems , particularly distributed and systems software. His work addresses critical issues like upgrade failures , log compression , performance profiling , and non-intrusive debugging . Key methodologies include empirical studies of real-world failures, static analysis, and runtime optimization. Publication Trends show expertise in operating systems , distributed systems , and software engineering . Recent work includes μSlope (log compression) and Relational Debugging (root-cause analysis), while earlier studies investigated JVM performance and distributed failure patterns . Scientific Awards : Canada Research Chair (2023), McCharles Prize (2018), NetApp Faculty Fellowship (2013–2016), ACM SIGSOFT Distinguished Paper (2011), Saburo Muroga Fellowship (2006–2007), University Gold Medal (2005) Advising & Collaborations : Supervised 15+ PhD and Master’s students, including Yongle Zhang (Purdue), Xu Zhao (Facebook), and Xiang Ren (Northeastern). Founded startup YScope to commercialize log compression tools like CLP , now deployed at Uber.

Gennady PekhimenkoView profile

Associate Professor

• Computer Architecture
• Systems Research
• Applied Machine Learning
• +3 more

Dr. Gennady Pekhimenko is an Associate Professor in the Department of Computer Science and cross-appointed to the Electrical and Computer Engineering department at the University of Toronto. He leads the EcoSystem research group and serves as CEO/Co-Founder of CentML, with affiliations at CIFAR and Vector Institute. PhD in Computer Science from Carnegie Mellon University (2016) His research spans computer architecture, systems optimization, and applied machine learning, with a focus on memory hierarchy efficiency, hardware acceleration, and compiler design. Recent work emphasizes large language model training, GPU virtualization, and processing-in-memory systems. Key article trends include: 1) Memory optimization for transformers and sparse models (2024-2025), 2) GPU resource management (2022-2024), 3) Prompt programming languages (2025), and 4) Deep learning compilation frameworks (2021-2023). Major Awards: NVIDIA Graduate Fellowship, Microsoft Research PhD Fellowship, ISCA Hall of Fame, AWS/Google/VMware research grants Key Collaborations: Onur Mutlu (CMU), Todd Mowry (CMU), Vector Institute researchers

• Concurrent Systems
• Performance Analysis
• Parallel Algorithms
• +3 more

Naama Ben-David serves as an Assistant Professor at the Technion - Israel Institute of Technology, specializing in theoretical foundations for concurrent computing systems. Her work bridges theoretical rigor with practical performance optimization in parallel programming environments. Her academic journey includes: Undergraduate studies at the University of Toronto PhD in Computer Science from Carnegie Mellon University (advised by Guy Blelloch) Three-year postdoctoral research position at VMware Research Research focuses on identifying performance bottlenecks in concurrent programs through mathematical modeling and algorithmic analysis. She develops frameworks for quantifying synchronization costs while exploring novel concurrent data structures and distributed algorithm design. Her methodology emphasizes formal verification of performance guarantees in real-world systems. Major recognitions include: 2022 PODC Doctoral Dissertation Award CMU School of Computer Science Dissertation Award Honorable Mention NSERC PGSD scholarship during doctoral studies Microsoft Research PhD Fellowship Her doctoral research at Carnegie Mellon was fully funded through competitive fellowships, with dissertation work receiving dual institutional recognition. Current research continues this focus on performance modeling while expanding into practical implementations of theoretically sound concurrent systems.

• Microelectromechanical Systems (MEMS)
• Microfluidics
• Microfabrication
• +4 more

Dr. Ian Foulds is an Associate Professor at the School of Engineering under the Faculty of Applied Science at the University of British Columbia Okanagan. His primary research focuses on Microelectromechanical Systems (MEMS) , Microfluidics , and Microfabrication , with applications spanning biomedical diagnostics, geological dating, and terahertz technology. Degrees: PhD (2007), MASc (2004), BASc (2003) in Electrical Engineering from Simon Fraser University Courses Taught: ENGR 353 Semiconductor Devices, APSC 179 Linear Algebra for Engineers His research integrates microfluidic systems with advanced fabrication techniques , including 3D printing and laser ablation, to develop cost-effective diagnostic tools and explore geological processes through high-precision chronology. Recent publications highlight innovations in droplet generation , agglutination assays , and terahertz emitter design , often combining interdisciplinary approaches from engineering and geoscience. Dr. Foulds holds the Principal's Research Chair - Indigenous Reconciliation in Engineering (Tier 1) , emphasizing his commitment to integrating Indigenous knowledges in engineering practices. His work involves collaborations across materials science, biomedical engineering, and geology, supported by technical expertise in MEMS and microfabrication.

Hao YuView profile

Professor

• Statistical Computing
• Parallel Programming
• Financial Time Series
• +4 more

Hao Yu is a full-time Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario . Holding a Ph.D. from Carleton University (1993), he specializes in statistical computing, financial time series analysis, and stochastic modeling. Educational Background Ph.D., Carleton University (1993) Research Interests focus on computational statistics, time series analysis, and probabilistic modeling. His work includes developing statistical algorithms for financial applications ( NSERC-funded 2013–2018) and theoretical advancements in residual analysis for ARMA/GARCH models. Academic Contributions include co-developing R packages for time series analysis, advancing maximum likelihood unit root tests, and creating novel methods for heavy-tailed risk factor simulation in finance. His research intersects computational methodology with financial applications. Scientific Awards are not explicitly mentioned in the provided text. Advising notable students: Yifan Li (PhD) Xixian Liu (MSc Financial Mathematics) Xiyang Zhang (MSc Thesis Track in Statistical Sciences)

• Formal Methods
• Relational Algebra
• Term Graph Rewriting
• +4 more

Professor Wolfram Kahl is a faculty member in the Department of Computing and Software at McMaster University . He has served as an instructor for courses such as Logical Reasoning for Computer Science , Formal Specification Techniques , and Certified Programming with Dependent Types , reflecting his expertise in formal methods and educational technologies. Research Interests : His work focuses on Relational algebraic approaches to software verification Term graph rewriting systems Haskell-based tool development for formal specification Category theory applications in computer science Interactive learning tools for discrete mathematics Parallel and distributed system modeling Publication Trends : Over the last decade, his publications have concentrated on formal verification techniques, graph transformation systems, and educational tool development. Notable works include mechanized relational algebraic proofs, parallel multicore verification frameworks, and contributions to Haskell module design for type class systems. Collaborations : Professor Kahl has collaborated extensively with researchers like Christopher Anand, Jacques Carette, and Alan Wassyng, and has contributed to major conferences including Relational and Algebraic Methods in Computer Science (RAMiCS) .

Dvira SegalView profile

Associate Professor

• Chemical Physics
• Quantum Dynamics
• Quantum Thermodynamics
• +3 more

Dvira Segal is an Associate Professor in the Department of Chemistry at the University of Toronto with a cross-appointment in the Department of Physics. They hold the Canada Research Chair in Theoretical Chemistry (2014-2024) and are a Distinguished Fellow at the Hebrew University of Jerusalem's Faculty of Sciences (2024-2026). Their research focuses on quantum dynamics, quantum thermodynamics, and nonequilibrium systems, particularly in nanoscale quantum devices and molecular-level electronics. Research interests include theoretical and computational studies of dissipative quantum systems, heat and charge transfer in molecular junctions, and quantum control strategies. Key areas involve spin dynamics in non-equilibrium systems, quantum thermal machines, and energy management at the nanoscale. Notable awards include the Canada Research Chair and Distinguished Fellowship. Segal leads the Centre for Quantum Information and Quantum Control and is part of the Chemical Physics Theory Group. Their work bridges quantum mechanics, statistical mechanics, and nanotechnology, with applications in quantum computing and energy science. Current and past students and postdocs include over 30 researchers, many now in academic and industry roles. Segal teaches courses on quantum mechanics, statistical mechanics, and physical chemistry, emphasizing graduate-level theoretical training.

Bogdan SimionView profile

Associate Professor

• Computer Science Education
• Distributed Processing
• Parallel Computing

Bogdan Simion is an Associate Professor (Teaching Stream) in the Department of Mathematical and Computational Sciences at the University of Toronto. His research focuses on Computer Science Education, Distributed Processing, and Parallel Computing. He can be reached at bogdan.simion@utoronto.ca or via his website https://www.cs.toronto.edu/~bogdan/ . His work emphasizes pedagogical approaches in computer science and advancing computational methodologies in distributed systems. No specific grants, awards, or advising activities are explicitly listed in the provided text.

Faith EllenView profile

Professor

• Distributed Computing
• Algorithms
• Computational Complexity
• +3 more

Professor Faith Ellen is a faculty member in the Department of Computer Science at the University of Toronto, where she holds the rank of Professor. Her research focuses on the theory of distributed computing, emphasizing lower bounds on computational complexity and the study of data structures in both distributed and sequential models. She has authored numerous publications in leading journals and conferences, contributing significantly to the fields of algorithms, computational complexity, and distributed systems. Her work often explores the interplay between model parameters and computational efficiency, with an emphasis on foundational theoretical insights. Her research interests include the design and analysis of concurrent data structures, synchronization algorithms, and the theoretical underpinnings of distributed systems. Key contributions include studies on adopt-commit objects, snapshot implementations, and non-blocking data structures. Ellen's work has been published in venues such as the Journal of the ACM , Distributed Computing , and Proceedings of PODC and SPAA . While her research has no explicitly listed awards, her prolific publication record underscores her academic impact. She has advised multiple collaborative projects but no specific student names are provided in the text. Ellen’s academic profile reflects a sustained engagement with core theoretical challenges in computer science, particularly in concurrency and distributed computing.

Maryam Mehri DehnaviView profile

Associate Professor

• High-Performance Computing
• Cloud Computing
• Machine Learning
• +3 more

Maryam Mehri Dehnavi is an Associate Professor of Computer Science at the University of Toronto and a Principal Research Scientist at NVIDIA. She holds the Canada Research Chair in Parallel and Distributed Computing. Her research group, ParaMathics, focuses on scalable numerical methods, high-performance libraries, and compilers for cloud and parallel computing platforms. Her research interests span cloud computing, machine learning, numerical analysis, and compiler optimization. She has been recognized with awards including the Ontario Early Researcher Award (2021) and an NSF CRII grant. She has advised numerous students, many of whom have achieved notable academic and industry roles. Teaching: Applied Parallel Computing, Parallel Computing, Cloud Computing Leadership Roles: General Chair of PPOPP 2023, Keynote Speaker at SIAM PP 2024 Labs/Teams: ParaMathics Research Group Her work bridges theoretical advances in numerical methods with practical applications in high-performance computing, emphasizing efficiency and scalability across diverse architectures.