Find Academic Supervisors

Jacques DesrosiersView profile

Professor

• Operations Research
• Transportation
• Optimization
• +3 more

Jacques Desrosiers is a Full Professor at HEC Montréal in the Department of Management Science since 1989. He has been with the institution since 1978 and is a founding member of the Group for Research in Decision Analysis (GERAD). His research focuses on optimization, transportation systems, and algorithmic methods for solving complex linear and combinatorial problems. He has received prestigious recognitions, including the Grand Prize for Excellence in Research (2015) and Fellowship in the Royal Society of Canada (2001). His work spans contributions to column generation techniques, degenerate linear programming, and vehicle routing problems. Notable achievements include advancing the minimum mean cycle-canceling algorithm, vector space decomposition for large-scale problems, and developing efficient heuristics for logistics challenges. His research bridges theoretical advancements and practical applications, influencing transportation and operations management globally. Desrosiers has authored over 150 peer-reviewed articles, with recent focus on algorithmic optimization, primal degeneracy handling, and decomposition methods. His contributions to software tools like COIN-OR’s CLP further highlight his commitment to advancing computational methods in operations research. Awards: Royal Society of Canada Fellowship (2001), HEC Montréal Grand Prize (2015) Key Research Themes: Column Generation, Network Flow Algorithms, Linear Programming, Vehicle Routing, Degeneracy Resolution Labs/Teams: Co-founder and active member of GERAD, collaborating on optimization projects

Shaahin FilizadehView profile

Professor

• Power Systems
• Power Electronics
• Modeling and Simulation of Power-Electronic Intensive Networks
• +3 more

Shaahin Filizadeh is a Professor in the Department of Electrical and Computer Engineering at the University of Manitoba's Price Faculty of Engineering. He previously served as the Department's Associate Head (Graduate Programs) from 2012 to 2019 and maintains an active research program in power systems and power electronics. His educational background includes a B.Sc. (1996) and M.Sc. (1998) in Electrical Engineering from Sharif University of Technology, Tehran, Iran, and a Ph.D. (2004) in Electrical Engineering from the University of Manitoba. Power Systems Power Electronics Modeling and Simulation of Power-Electronic Intensive Networks HVDC Converters and Modular Multilevel Converters (MMCs) Dynamic Phasor Modeling Techniques Electrified Vehicular Systems and Energy Storage Integration His research focuses on advanced energy conversion systems, power-electronic intensive networks, and specialized modeling methods including average-value models and dynamic phasors. Current work emphasizes low-inertia systems, battery energy storage converters, and MMC topologies with DC fault blocking capability. He directs the Power Electronics and Energy Conversion Atelier for Modeling, Prototyping, and Simulation (PEEC-AMPS), a state-of-the-art facility equipped with PSCAD/EMTDC, RTDS, and modular multilevel converters. Dr. Filizadeh serves as Chair of the IEEE Task Force on Dynamic Phasor Modeling Techniques and holds editorial positions at IEEE Transactions on Energy Conversion and IEEE Power Engineering Letters. He has supervised over 40 graduate students including 9 doctoral candidates and 32 master's students. His research has been supported by grants enabling hardware-in-loop simulation capabilities and advanced modeling tools for power system applications. His laboratory, PEEC-AMPS, features EMT simulation software, real-time simulators, modular multilevel converters, and power-electronic prototyping equipment for rapid development of energy conversion systems.

• Microwave Imaging
• Computational Electromagnetics
• Inverse Problems
• +3 more

Dr. Ian Jeffrey is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Manitoba's Price Faculty of Engineering. He holds a PhD in Electrical and Computer Engineering from the University of Manitoba and leads research in the Electromagnetic Imaging Lab (EIL), focusing on wavefield imaging algorithms for agricultural and biomedical applications. His work integrates computational electromagnetics, optimization, and machine learning. Research interests include time-domain imaging, generative adversarial networks for calibration, and Bayesian machine learning for uncertainty quantification. Dr. Jeffrey's publications emphasize machine learning-enhanced microwave imaging, computational methods, and industrial applications like grain storage safety. He is actively seeking MSc and PhD students with C++ experience. No scientific awards were listed. Graduate opportunities focus on microwave imaging, computational electromagnetics, and GPU computing.

John C. BowmanView profile

Professor

• Statistical closures in turbulence
• Spectral Reduction
• 3D vector graphics
• +10 more

John C. Bowman is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. He has maintained an active teaching career with courses spanning undergraduate to graduate levels, including Honours Calculus (Math 117/118), Real Variables (Math 417), Coding Theory (Math 422), Linear Algebra II (Math 225), and Statistical Theories of Turbulence (Math 655). His office is located in CAB 521, and he has consistently provided regular office hours for student consultation across multiple courses. Dr. Bowman earned his BS in Engineering from the University of Alberta, followed by an MA and PhD from Princeton University. His educational background has informed his diverse research trajectory spanning mathematical physics, computational mathematics, and scientific visualization. His teaching philosophy emphasizes deep conceptual understanding, as evidenced by his development of comprehensive hyperlinked 3D lecture notes for Honours Calculus. His primary research focuses on statistical theories of turbulence, particularly his development of Spectral Reduction as a reduced statistical description of turbulence. His work shows remarkable agreement with full numerical simulations, even in flows containing long-lived coherent structures. Additional research interests include 3D vector graphics (notably the Asymptote language he developed), implicit dealiasing of convolutions, exponential integrators, and exactly conservative integrators for numerical simulations. He has been involved with the University of Alberta's Geophysical Fluid Dynamics Research Group, where his work bridges theoretical mathematics with practical computational applications. Dr. Bowman's publication record demonstrates consistent contributions to computational mathematics and fluid dynamics over several decades. His recent work (2015-2024) shows continued innovation in computational methods for turbulence simulation, with particular attention to dealiasing techniques, efficient convolution algorithms, and novel approaches to spectral methods. His research has practical applications in assessing dissipation mechanisms in large-eddy simulations and modeling high-Reynolds number turbulence. As an educator, Dr. Bowman has developed comprehensive teaching materials and maintains an active engagement with students through office hours and detailed course websites. His commitment to mathematical education extends to high school outreach through contributions to the "Pi in the Sky" mathematics magazine. His work on the Asymptote vector graphics language has created a valuable tool for mathematical visualization and scientific publishing that integrates seamlessly with TeX.

Geoff PondView profile

Adjunct Assistant Professor

• Operations Research
• Optimization
• Fleet Management
• +4 more

Geoff Pond is an Adjunct Assistant Professor at the Smith School of Business, Queen's University, with joint appointments at the Royal Military College of Canada (Associate Professor) and the University of Ottawa. He specializes in operations management and optimization, with a focus on fleet management, military logistics, and healthcare operations. His research applies meta-heuristics like Differential Evolution and Genetic Algorithms to solve complex optimization problems. Education: MPA, Queen's University (2016) PhD in Mechanical Engineering, University of New Brunswick (2006) BSc Eng in Mechanical Engineering, University of New Brunswick (2003) Diploma in Technology Management and Entrepreneurship, University of New Brunswick (2001) Research Interests: Optimization techniques (e.g., meta-heuristics), fleet staging, military vehicle analysis, healthcare logistics, and operations research applications. Recent work includes optimizing air ambulance bases and analyzing corrosion prevention strategies. Key Awards: Best Presentation Award, UK OR Society (2011) Science and Technology Excellence Award, DRDC (2010) Youth Delegate Award, International Federation for Promotion of Mechanism and Machine Science (2004) Teaching & Service: Taught courses in operations management, calculus, and data analysis. Served as Department Head at RMC's Management Department (2018–2019) and reviewer for journals/conferences like OR Insight and Administrative Sciences Association of Canada.

• Algorithms
• Big data analytics
• Blockchain
• +12 more

Dr. Wojciech Golab is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research focuses on distributed systems, concurrency control, and persistent memory algorithms. He has received recognition for his work on shared memory algorithms from ACM Computing Reviews. His academic career includes teaching courses like ECE 252, ECE 356, and ECE 454 in the past five years. Education: PhD in Computer Science, University of Toronto (2010) M.Math in Computer Science, University of Waterloo (2004) BSc in Computer Science, University of Toronto (2002) Dr. Golab's research interests span algorithms, blockchain, distributed systems, fault tolerance, and scalable storage solutions. His work emphasizes practical applications of theoretical computer science, such as optimizing transaction processing and ensuring consistency in distributed environments. His recent publications explore topics like detectable synchronization objects for persistent memory, blockchain sharding protocols, and efficient mutual exclusion algorithms under system failures. These contributions highlight advancements in both theoretical foundations and real-world distributed system design. Awards: ACM Computing Reviews 'Notable Computing Items' recognition (2012) Dr. Golab has collaborated with industry partners like Hewlett-Packard Labs, Google, and Cisco to advance research in distributed computing and cloud systems. He currently oversees graduate studies in his department and welcomes applications from prospective students.

Anwar HasanView profile

Professor

• Cryptography
• Post-Quantum Cryptography
• Secure Multiparty Computation
• +5 more

Anwar Hasan is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He specializes in cryptography, post-quantum cryptography, and secure computation, with a focus on algorithm optimization, cryptographic hardware implementation, and energy efficiency. His work spans theoretical foundations and applied systems, including blockchain technology, fault-tolerant cryptosystems, and secure multiparty computation. His research interests emphasize developing quantum-resistant cryptographic protocols, improving efficiency in cryptographic algorithms (e.g., EdDSA signature verification, CSIDH optimization), and designing secure systems against adversarial attacks (e.g., poisoning in federated learning, covert adversary models). He also explores energy consumption analysis of cryptographic algorithms, particularly in the context of NIST PQC standards and elliptic curve-based systems. Recent work includes contributions to threshold cryptography (isogeny-based schemes), decentralized oracle networks (CountChain), and privacy-preserving blockchain applications (AdChain). His publications frequently address hardware-software co-design for cryptographic primitives, emphasizing both theoretical advancements and practical implementation challenges. Dr. Hasan’s research also extends to applied areas such as fraud detection systems, digital rights management using blockchain, and energy efficiency in embedded cryptographic systems. He collaborates on projects involving secure communication in cloud computing and resilient key management for mobile applications.

Lila KariView profile

Professor

• Biodiversity informatics
• Data science
• Machine learning
• +6 more

Lila Kari is a Professor and Cheriton Faculty Fellow at the School of Computer Science , part of the University of Waterloo in Ontario, Canada. Her research focuses on Biodiversity informatics , data science , and machine learning applications in comparative genomics and metagenomics , particularly for analyzing genomic signatures through Chaos Game Representation (CGR) and alignment-free methods. 2023 : Environment and taxonomy shape genomic signatures of extremophiles 2020 : Machine learning for rapid pathogen classification during pandemics 2019 : Ultrafast DNA sequence classification with ML-DSP 2016 : Additive genomic signatures for enhanced taxonomic differentiation 2015 : Mapping genomic signature spaces for molecular distance analysis 2009-2005 : Foundational work in DNA language theory and computational biology Her work has been instrumental in developing composite DNA signatures that combine nuclear and organellar genomic data for improved species differentiation, and assembled DNA signatures that enable analysis from fragmented sequencing data. She contributes to global initiatives like BIOSCAN for biodiversity surveillance and has created tools such as MLDSP-GUI for accessible DNA sequence analysis.

Naomi NishimuraView profile

Associate Professor

• Combinatorial Reconfiguration
• Parameterized Complexity
• Graph Algorithms

Naomi Nishimura is an Associate Professor at the Cheriton School of Computer Science, University of Waterloo. She holds a Ph.D. from University of Toronto (1991) and researches combinatorial reconfiguration, parameterized complexity, and graph algorithms. Her work develops efficient algorithms for reconfiguration problems, including recent advances in parallel implementations for atom reconfiguration using GPUs. Nishimura's research establishes theoretical foundations for transforming combinatorial structures through step-by-step modifications while maintaining solution properties. Recent publications survey parameterized complexity landscapes for reconfiguration problems and introduce novel algorithms like the assignment-rerouting-ordering technique. Her group analyzes computational boundaries for problems involving independent sets, dominating sets, and graph minors.

Tamer ÖzsuView profile

Professor

• Distributed Data Management
• Graph Databases
• RDF Systems
• +2 more

Tamer Özsu is a University Professor at the David R. Cheriton School of Computer Science, University of Waterloo, where he previously served as Director (2007-2010) and currently holds the role of Associate Dean of Research. He is also a Distinguished Visiting Professor at Tsinghua University. Professor Özsu's research spans distributed data management, graph/RDF systems, and database fundamentals. His current work focuses on: Distributed graph processing algorithms SPARQL query optimization for RDF systems Streaming graph analytics Indexing techniques for modern hardware Distributed database architectures He has authored the seminal textbook Principles of Distributed Database Systems and co-edited the Encyclopedia of Database Systems . As founding Editor-in-Chief of ACM Books, he has shaped computing literature. His recent publications demonstrate continued innovation in graph partitioning, streaming graph algorithms, and scalable RDF processing. Özsu maintains active research leadership in database systems with over 30 years of influential contributions.

Justin W.L. WanView profile

Professor

• Scientific Computing
• Medical Image Processing
• Computational Finance
• +4 more

Justin W.L. Wan is a Professor at the David R. Cheriton School of Computer Science, University of Waterloo. He holds a Canada Research Chair in Scientific Computing and is currently Co-Director for the Computing and Financial Management Program. He earned his BSc from Hong Kong (1992), MA (1995) and PhD (1998) from UCLA's Applied Math Program. Research interests include: Scientific computing Medical image processing (CT scans, cell segmentation, image registration) Computational finance (option pricing, regime switching, hedging parameters) Computer graphics simulation of natural phenomena Development of robust multigrid methods for PDEs Recent publications span: 2021: Multigrid methods for mean-field games and optimal mass transport 2020: Deep learning approaches for high-dimensional finance and CT artifact reduction 2019: Multigrid for Monge-Ampere equations and multi-asset options 2018: Cellular image segmentation algorithms Research team includes: PhD students: Hossein Aboutalebi, Andrew Na, Connor Tannahill, Chris West Master's students: Ying Kit (Marco) Hui, Shujie Liu, Lufan Wang Administrative roles held: Director, Centre of Computational Mathematics for Industry and Commerce (2010-2015) Associate Director, Cheriton School of Computer Science (2015-2018) Director of Graduate Studies (2019-2021) Professional service: Secretary, Canadian Applied and Industrial Mathematics Society (2015-2020) Organizer, CAIMS Annual Meeting (2021) Journal editor and program committee member

Stephen WattView profile

Professor

• Symbolic Computation
• Computer Algebra Systems
• Programming Languages
• +5 more

Stephen Watt is a Professor at the University of Waterloo, affiliated with the Department of Computer Science within the Faculty of Mathematics. He holds a PhD in Computer Science (1986), MMath in Applied Mathematics (1981), and BSc in Honors Mathematics and Physics (1979). His research focuses on enabling computers to handle mathematics intelligently, with key areas including programming languages for mathematical software, symbolic computation algorithms, computer algebra system interoperability, mathematical knowledge management, and STEM education technology. His work spans theoretical and applied domains, such as developing algorithms for block matrices and handwriting recognition systems like the Legendre-Sobolev approach. He has contributed to initiatives like the International Mathematical Knowledge Trust (IMKT) and collaborated on projects like InkML for mathematical collaboration. Recent publications explore generative AI in STEM assessments, software portability, and GPU-optimized arithmetic operations. Dr. Watt’s research also addresses historical perspectives on symbolic computation and its evolution, alongside modern challenges in mathematical collaboration tools and digital ink compression. His contributions span conferences such as ISSAC, CICM, and SYNASC, reflecting interdisciplinary engagement with both theoretical and applied computer science.

William MelekView profile

Professor

• Artificial Intelligence
• Intelligent Control
• Mechatronics
• +11 more

William Melek is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo. He serves as Director of the Laboratory of Computational Intelligence and Automation and RoboHub. His research focuses on intelligent systems, modular robotics, autonomous vehicles, and bioinformatics. He holds a B.A.Sc. in Electrical & Computer Engineering (Zagazig University, 1994), M.Sc. and Ph.D. in Mechanical Engineering (University of Toronto, 1998/2002), and a Post-Doctorate in Computer Science (Ryerson University, 2004). Education: Bachelor's: B.A.Sc in Electrical & Computer Engineering, Zagazig University (1994) Master's: Mechanical Engineering, University of Toronto (1998) Doctorate: Mechanical Engineering, University of Toronto (2002) Post-Doctorate: Computer Science, Ryerson University (2004) His research interests span artificial intelligence, intelligent control, mechatronics, automotive design, and computational intelligence. Recent work includes autonomous vehicle navigation, predictive maintenance in manufacturing, and bioinformatics-driven clinical decision support systems. He teaches courses such as ME 360 (Control Systems), ME 780 (Mechatronics), and MTE 100 (Intro to Mechatronics). Articles from 2023–2025 highlight advancements in autonomous driving decision-making, modular robotics, and 5G-enabled teleoperation systems. His lab develops algorithms for urban autonomous systems and industrial automation. He currently oversees graduate student applications in mechatronics and robotics. Labs/Teams: Laboratory of Computational Intelligence and Automation, RoboHub.

Pooya RonaghView profile

Academic

• Quantum Computation
• Quantum Algorithms
• Quantum Error Correction
• +8 more

Pooya Ronagh is a Research Assistant Professor at the University of Waterloo, affiliated with the Department of Physics & Astronomy and the Institute for Quantum Computing (IQC). He also serves as a Scientific Lead at the Perimeter Institute Quantum Intelligence Lab (PIQuIL) and directs the Hardware Innovation Lab at 1QBit. His work bridges quantum computation, machine learning, and optimal control, focusing on quantum algorithms, error correction, and hybrid quantum-classical systems. Education: PhD in Mathematics (University of British Columbia, 2016), MSc in Mathematics (UBC, 2011), dual BSc in Mathematics and Computer Science (Sharif University of Technology, 2009). Awards include the Benjamin Franklin Fellowship (2009). Research Interests: Quantum algorithms for machine learning, reinforcement learning, fault-tolerant quantum architectures, cryogenic systems, and quantum control. He explores applications of quantum simulation to improve learning efficiency and robustness in AI systems. Recent work includes optimizing quantum error correction decoders, developing scalable superconducting architectures, and advancing neural network-based quantum state tomography. His contributions span theoretical frameworks (e.g., lattice surgery scheduling) and experimental methods (e.g., SFQ pulse control). Teaching: Courses like PHYS 490 (Machine Learning in Physics) emphasize practical coding and interdisciplinary projects. Grants and collaborations involve industry and academic partners in quantum hardware and software development. Labs: Hardware Innovation Lab (1QBit), IQC Quantum Control Group Future Work: Scaling quantum supercomputers, cryogenic neural decoders, quantum-enhanced generative AI

Andrew GoertzenView profile

Adjunct Professor

• Nuclear Medicine
• Medical Imaging
• Biomedical Physics
• +3 more

Andrew Goertzen is an Adjunct Professor in the Department of Physics and Astronomy at the University of Manitoba, serving as Coordinator of the PET CT Program. His research focuses on advancing nuclear medicine technologies, particularly in PET imaging instrumentation, neurodegenerative disease biomarkers, and cardiovascular risk assessment. He leads interdisciplinary projects in medical physics, combining expertise in detector design, imaging algorithms, and clinical applications. Key research areas include: PET scanner optimization (dual-layer detectors, Monte Carlo modeling) Neuroimaging of Alzheimer's and Parkinson's disease Cardiac and metabolic disease diagnostics Bone fracture risk prediction via finite element analysis His work bridges basic physics with clinical translation, emphasizing innovations in hybrid imaging modalities (PET/MR) and radiation safety standards. Recent contributions include developing the CRAX cardiovascular risk tool and improving PET reconstruction algorithms using GPU computing.