Find Academic Supervisors

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

Artur IzmaylovView profile

Professor

• Quantum Dynamics
• Theoretical Chemistry
• Quantum Computing
• +5 more

Artur Izmaylov is a Professor of Theoretical Chemistry at the University of Toronto with dual departmental appointments: the Department of Chemistry at the St. George campus and the Department of Physical and Environmental Sciences at the University of Toronto Scarborough (UTSC). He leads the Izmaylov Research Group and is affiliated with the Center for Quantum Information and Quantum Control. His offices are located at EV356 (UTSC) and LM420C (St. George), and he can be contacted at artur.izmaylov@utoronto.ca or via phone at 416-208-2951 (UTSC) / 416-946-8405 (St. George). Professor Izmaylov's research develops novel theoretical and computational approaches to quantum dynamics in complex systems. Key focus areas include: Quantum processes in organic photovoltaics, biomolecules, and catalytic surfaces Hybrid quantum-classical methodologies for subsystem-environment interactions Renormalization techniques for efficient quantum dynamics simulations Quantum computing applications for chemical problems and electronic structure Nonadiabatic dynamics near conical intersections and spin-charge transfer His recent publications (2023-2025) demonstrate strong emphasis on quantum algorithm development for chemical applications, particularly: Advancements in variational quantum eigensolver (VQE) methodologies Quantum resource optimization and error mitigation strategies Novel Hamiltonian decomposition techniques for efficient simulation Applications in molecular vibrations, electronic structure, and materials science Hybrid quantum-classical approaches for scalable computations The Izmaylov Research Group actively recruits graduate students and postdoctoral researchers, with opportunities through NSERC USRA, CQIQC, CHMD90/91, CHM499Y/PHY479Y courses, and Mitacs Globalink programs. Current research directions emphasize quantum computing implementations for chemical dynamics and surface interactions.

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.

Nandita VijaykumarView profile

Assistant Professor

• Computer Architecture
• Compilers
• Systems
• +5 more

Nandita Vijaykumar is an Assistant Professor in the Computer Science Department at the University of Toronto and the Department of Computer and Mathematical Sciences at the University of Toronto Scarborough. She leads the embARC Research Group, affiliated with the Vector Institute and Robotics Institute. Her work focuses on computer architecture, compilers, and systems, with a particular emphasis on the interplay between programming models, systems, and architectures. She holds a Ph.D. and M.S. from Carnegie Mellon University (2019), advised by Prof. Onur Mutlu and Prof. Phil Gibbons. She has held roles at Intel Labs, AMD, Microsoft, and Nvidia, and was a visiting student at ETH Zurich's Systems Group. Her research bridges computer architecture/systems with robotics, computer vision, and deep learning. Key projects include optimizing GPU algorithms, differential privacy in hardware, and 3D neural graphics. She mentors over 30 graduate and undergraduate students across Ph.D., MSc, and internship programs, fostering innovation in distributed systems and machine learning infrastructure. Nandita’s recent publications address challenges in differentiable rendering, distributed robotics, and energy-efficient computing. Her work on frameworks like DPWatch and DiffusionRenderer highlights her contributions to privacy-aware hardware and neural rendering. Collaborations with industry and academic institutions underscore her commitment to practical, cross-disciplinary solutions.

Mark C. JeffreyView profile

Assistant Professor

• Computer Architecture
• Parallel Computing
• Computer Systems
• +3 more

Mark C. Jeffrey is an Assistant Professor in the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto, cross-appointed to the Department of Computer Science. His research focuses on computer architecture and systems, aiming to simplify parallel programming for software engineers through novel compilers and large-scale multicore architectures. Previously, he was a Research Scientist at Facebook AI Research and earned his PhD from MIT under Daniel Sanchez. He holds a MASc and BASc from the University of Toronto. Education: PhD in Computer Science, MIT (2019) MASc in Computer Engineering, University of Toronto (2011) BASc in Engineering Science, University of Toronto (2009) Research interests include parallel computing, compilers, multicore architectures, and energy-efficient systems. His work bridges hardware-software co-design to enable scalable parallelism in modern systems. Notable awards include the George M. Sprowls PhD Thesis Award (2nd place, 2019), Best Paper at NoCArc 2023, and a Best Paper Award at ARC 2010. He teaches advanced courses in computer architecture and parallel programming (ECE1755, ECE552). His GitHub repository bf-intersect explores Bloom filter configurations for address-set disambiguation, tied to his SPAA 2011 and MASc thesis work.

Christophe DubachView profile

Associate Professor

• Data-Parallel Language Design
• Hardware Design Space Exploration
• Heterogeneous Computing
• +4 more

Christophe Dubach is an Associate Professor at McGill University, jointly appointed in the Department of Electrical and Computer Engineering (ECE) and the School of Computer Science (CS). He holds a Canada CIFAR AI Chair at Mila. Previously, he was a Reader (Associate Professor) at the University of Edinburgh until 2019. His research focuses on data-parallel language design, high-level code generation for parallel hardware (GPUs/FPGAs), hardware design space exploration, and applying machine learning to these areas. He has advised numerous PhD and MSc students on topics like FPGA-based high-level synthesis, neural networks, and compiler optimization. Education: PhD in Informatics (2009, University of Edinburgh), MSc in Computer Science (2005, EPFL, Switzerland). Teaching includes courses on compiler design, computer organization, and high-level synthesis. His projects include Lift (a functional IR for GPU code generation) and High-Level Synthesis of Neural Networks on FPGAs. Collaborators include Michel Steuwer, Lu Li, and others in the fields of compilers, machine learning, and parallel computing.

Xu WangView profile

Professor

• Automated Statistical Learning
• Data Mining
• Stochastic Process
• +4 more

Xu (Sunny) Wang is a Professor in the Department of Mathematics at Wilfrid Laurier University, affiliated with the Faculty of Science. Their research focuses on automated statistical learning, stochastic processes (particularly self-exciting point processes), mixture models, and applications of applied statistics in health and economics. They teach various mathematics courses and actively engage in interdisciplinary research collaborations. Research interests include data mining methodologies for healthcare and business analytics, with recent emphasis on modeling terror attacks using self-exciting processes, sleep apnea diagnosis via neural networks, and analyzing periodic communication patterns. Their work spans statistical theory (e.g., nonparametric methods) and practical applications like emergency call clustering and economic efficiency studies in Ghana. Publications reflect a strong computational-statistical foundation, combining mathematical rigor with real-world problem-solving across disciplines. Supervision opportunities are available for students interested in statistical modeling, data science, and interdisciplinary research projects.

Ilias S. KotsireasView profile

Professor

• Computer Algebra
• Combinatorics
• Optimization Algorithms
• +6 more

Dr. Ilias S. Kotsireas is a Professor in the Department of Computer Science and Physics at Wilfrid Laurier University (WLU), Canada. He serves as Director of the CARGO (Computer Algebra Research Group) lab and Editor-in-Chief of the Journal of Algebraic Combinatorics . His research focuses on computational mathematics, combinatorics, optimization algorithms, and symbolic computation. Kotsireas is a Fellow of the Institute of Combinatorics and its Applications (ICA, 2004) and has held leadership roles in organizations like ACM SIGSAM and the ACA Working Group. He maintains adjunct appointments at the Athena Research Center (Greece) and the University of Waterloo, and is a Principal Scientist at ORCCA (Ontario Research Centre for Computer Algebra). His work integrates computer algebra systems with SAT solvers to address complex combinatorial problems, such as Hadamard matrices and Golay pairs. Recent research explores neurosymbolic frameworks, blockchain economics, and machine learning applications in algebraic geometry. Education: Advanced degrees in Mathematics/Computer Science (not explicitly listed, inferred from roles). Research Interests: Computer algebra systems, combinatorial search, algorithm portfolios, cryptographic protocols, and computational neuroscience applications. His contributions span theoretical advancements (e.g., Legendre pair algorithms) and practical tools (e.g., Maple-based software). Kotsireas collaborates internationally, organizing conferences like ACA and publishing in venues such as Journal of Symbolic Computation and SpringerPlus . His lab, CARGO, emphasizes supercomputing and interdisciplinary projects. Awards: Fellow of the Institute of Combinatorics and its Applications (ICA), 2004 Labs & Teams: Leads the CARGO lab and affiliated with ORCCA, focusing on computational algebra and high-performance computing.

Michael GodfreyView profile

Professor

• Software Engineering
• Software Evolution
• Empirical Studies
• +5 more

Michael Godfrey is a Professor at the University of Waterloo's David R. Cheriton School of Computer Science, affiliated with the Department of Electrical and Computer Engineering. His work focuses on software engineering, empirical studies of software systems, code review practices, and open-source software ecosystems. He holds a Ph.D., M.Sc., and B.Sc. from the University of Toronto (1997, 1988, 1986). Research interests include software evolution, mining software repositories, provenance tracking, code duplication analysis, and program comprehension. He actively explores how developers interact with code reviews, documentation systems, and modern CI/CD pipelines. Recent work investigates app store software ecosystems, Bash scripting vulnerabilities, and deep learning API testing. Publications span empirical studies of developer communities (e.g., Stack Overflow analysis), architectural teaching methods, and automated testing techniques like documentation-guided fuzzing. His work bridges theory and practice, addressing challenges in large-scale systems maintenance and developer productivity. Leverages tools like Elasticsearch for repository mining and explores anomaly detection in software development. Engaged in curriculum development for software architecture education and has contributed to conferences like MSR and ICSE.

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.

• Compiler Testing
• Program Analysis
• Static Binary Taint Analysis
• +5 more

Chen Sun is an Adjunct Assistant Professor at the Department of Data and Systems Engineering, The University of Hong Kong. His research focuses on compiler testing, program analysis, static binary taint analysis, and software engineering methodologies. Notable areas include leveraging large language models (LLMs) for compiler testing baselines, probabilistic delta debugging, and enhancing program reduction techniques across multiple languages. Recent work addresses ransomware defense mechanisms, fuzzing countermeasures, and database management system (DBMS) bug discovery through configuration transformations. Research interests span compiler optimization, fault localization, cybersecurity, and algorithmic efficiency. His publications from 2023-2025 highlight trends in combining AI with traditional software engineering challenges, such as improving type inference for Java code and developing syntax-guided program reduction frameworks. Articles frequently address practical security concerns like protecting embedded devices from protocol fuzzing and mitigating memory leaks through sanitizer-based localization. No scientific awards are explicitly mentioned. Advising and grants sections remain underdeveloped in available data. Current affiliations emphasize academic-industrial collaboration through adjunct roles focused on cutting-edge software reliability and security research.

William N. SumnerView profile

Associate Professor

• Automated Debugging
• Concurrency & Parallelism
• Program Analysis & Transformation
• +3 more

William N. Sumner is an Associate Professor and Associate Director, Academic and Administration in the School of Computing Science at Simon Fraser University. He holds a Ph.D. in Computer Science from Purdue University (2013) and a B.Sc. in Computer Science from Hope College (2005). His research focuses on automated debugging tools, concurrency, parallelism, program analysis, and software quality. He teaches courses such as Software Development Methods, Software Testing, and Automated Software Analysis. Sumner actively recruits graduate and undergraduate students for research in improving software development processes through automation. His work emphasizes reducing repetitive tasks in software development using techniques like test case reduction and program analysis. Education: Ph.D., Computer Science, Purdue University, 2013 B.Sc., Computer Science, Hope College, 2005 Research Interests: Sumner’s research addresses challenges in software development automation, including automated debugging, test suite assessment, and program hardening. He develops tools like Pardis to optimize test case reduction and improve software reliability. His work intersects with concurrency, parallelism, and program transformation to enhance developer productivity and software quality. Recent Contributions: Recent publications include advancements in type batched program reduction (2023) and leveraging models for test case reduction in software repositories (2021). His Pardis framework (2019) improves test case reduction efficiency by addressing priority inversion in structured inputs. Teaching & Engagement: Sumner teaches courses such as CMPT 373 (Software Development Methods), CMPT 473 (Software Testing), and CMPT 479 (Automated Software Analysis). He emphasizes hands-on projects, such as the Geotagged Meeting Planner in CMPT 276, focusing on teamwork, agile methodologies, and software engineering best practices.

Ivan BeschastnikhView profile

Associate Professor

• Distributed Systems
• Software Engineering
• Cloud Computing
• +5 more

Ivan Beschastnikh is an Associate Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Faculty of Science. He leads research in distributed systems, software engineering, and formal methods, with a focus on privacy-preserving technologies, blockchain systems, and machine learning. His work bridges theoretical foundations and practical system implementations, emphasizing empirical validation and real-world deployment. Research Interests: His primary areas include distributed systems (e.g., consensus algorithms, transaction processing), formal methods for system correctness (e.g., PGo compiler for verified implementations), and privacy in federated learning and blockchain. He also explores interdisciplinary topics like AI ethics, human-computer interaction for developers, and system security. Key Projects: Includes PGo (formal specification to Go compiler), Biscotti (decentralized federated learning), Erlay (Bitcoin transaction relay optimization), and Teleoscope (text corpus analysis tool). His work has practical impact in blockchain scalability, secure multi-party ML, and distributed system validation. Awards: Recognized with the ASPLOS Distinguished Artifact Award, UBC Faculty Teaching Award, and ESEM Best Paper Award. His contributions span academic excellence, pedagogy, and collaborative research. Lab Affiliations: Active in the Systopia Lab and Software Practices Lab (SPL) , and collaborates with UBC’s Data Science Institute and Blockchain@UBC. He emphasizes interdisciplinary research, mentoring students in both theoretical and applied systems work.

Ronald GarciaView profile

Associate Professor

• Programming Languages
• Gradual Typing
• Type Systems
• +6 more

Ronald Garcia is an Associate Professor in the Department of Computer Science at the University of British Columbia, affiliated with the Faculty of Science and the Software Practices Lab. His research focuses on programming language theory, emphasizing gradual typing, type systems, and end-user programming. He teaches courses such as Programming Language Principles (CPSC 509) and Compiler Construction (CPSC 411). Education details are not explicitly stated in the provided texts, but his extensive academic contributions span over two decades. Research interests include formal semantics of programming languages, typestate systems, dependent types, and improving software practices for end-users. He has advised numerous Ph.D. and Master's students, contributing to over 30 academic publications. Recent work explores hybrid programming environments for end-users, runtime type slicing for debugging, and foundational frameworks for gradual dependent types. Garcia actively participates in academic conferences as a PC member and keynote speaker, notably in PLDI, POPL, and ICFP. His lab and collaborations focus on advancing theoretical foundations while addressing practical challenges in software development and education.

Julien ArinoView profile

Professor

• Mathematical Epidemiology
• Mathematical Population Dynamics
• Data Science
• +3 more

Julien Arino is a Professor in the Department of Mathematics at the University of Manitoba , specializing in Mathematical Epidemiology and Mathematical Population Dynamics . He is an active member of the Mathematical Biology group and focuses on the role of movement in disease transmission, with applications to both human and animal health. Department of Mathematics Faculty of Science University of Manitoba His research spans: Metapopulation dynamics with explicit movement Multi-pathogen and multi-species transmission modeling Computational epidemiology with data science integration Evaluation of travel control measures for disease variants Recent work includes publications on: Cholera transmission in Chad Multi-species bovine tuberculosis dynamics Measles resurgence patterns Software tools for R compilation and data visualization He maintains a public YouTube channel with lecture videos on Mathematical Epidemiology and contributes open-source code for scientific computing in R and Python. His methodological contributions include novel approaches to plot formatting and computational efficiency in epidemic modeling.