Find Academic Supervisors

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.

Fantahun M. DefershaView profile

Professor

• Manufacturing system analysis
• Flexible and cellular manufacturing systems
• Reconfigurable manufacturing systems
• +7 more

Fantahun M. Defersha is a Full Professor in the Department of Mechanical and Industrial Engineering at the University of Guelph, Ontario, Canada. He holds a PhD in Mechanical Engineering from Concordia University (2006) and has over 28 years of academic experience, including roles as Area Head in Mechanical Engineering. His research focuses on manufacturing systems optimization, cellular manufacturing, supply chain modeling, meta-heuristics, and parallel computing applications. Education: B.Sc. Mechanical Engineering (1995), Addis Ababa University MEng. Mechanical Engineering (2000), University of Roorkee (IIT Roorkee) PhD Mechanical Engineering (2006), Concordia University Research Interests: Manufacturing system analysis, flexible/cellular manufacturing systems, reconfigurable manufacturing systems, supply chain optimization, meta-heuristics, parallel computing, and additive manufacturing sustainability. His work integrates computational methods like genetic algorithms and machine learning to solve complex industrial problems. Publications: Over 50 peer-reviewed journal articles, emphasizing sustainable manufacturing, optimization algorithms, and industrial systems. Recent work includes hybrid machine learning approaches for additive manufacturing and cloud-based digital twin systems. Honors: Campaign for a New Millennium Graduate Scholarship (2004–2005) Concordia University International Tuition Fees Remission Award (2004–2005) Concordia University Graduate Fellowship (2004–2005) Teaching & Advising: Taught over 25 courses, including Optimization in Engineering, Discrete Event Simulation, and Manufacturing Systems Design. Actively advises graduate students in mechanical and industrial engineering. Current research funding includes NSERC grants for Industry 4.0 integration and digital twin technologies. Labs/Teams: Leads research on smart manufacturing systems, digital twins, and sustainable production processes through collaborations with industry partners like AVL Manufacturing Inc.

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.

Pooya Moradian ZadehView profile

Professor

• Artificial Intelligence
• Public Health
• Social Network Analysis
• +4 more

Prof. Pooya Moradian Zadeh is a Professor in the School of Computer Science at the University of Windsor, specializing in Artificial Intelligence and its applications in public health, social networks, and pandemic response. His research focuses on leveraging AI to address challenges like social isolation, disease surveillance, and healthcare accessibility. He has led interdisciplinary projects such as the $15 million INSPIRE initiative for pandemic preparedness and contributed to the WE-Spark Health Institute's COVID-19 Dashboard . Recognized for his teaching excellence, he received the 2024 Alumni Association Distinguished Teaching Award . His work involves collaborations with institutions like the Great Lakes Institute for Environmental Research and WE-Spark, addressing topics ranging from recommender systems to compassionate community approaches for vulnerable populations. He mentors students like Saghi Khani and Kameswara Peddada, guiding projects such as Simplified SCO on Wheels , which improves retail checkout processes. Research & Grants : Prof. Zadeh has secured grants totaling over $15M for pandemic response, AI-driven health solutions, and social network analysis. His teams develop algorithms for social isolation detection, personalized healthcare recommendations, and agent-based models for public health policy evaluation. He is also involved in advancing conversational AI for healthcare accessibility and optimizing team formation in digital networks. Labs & Collaborations : He leads projects at the University of Windsor’s 300 Ouellette Ave. campus and collaborates with industry and academic partners to bridge AI innovation with real-world health challenges. His work emphasizes ethical AI practices and community-focused solutions.

Prasad DaggupatiView profile

Associate Professor

• Water Resources Engineering
• Environmental Engineering
• Agricultural Management
• +5 more

Prasad Daggupati is an Associate Professor in the School of Engineering at the University of Guelph, specializing in Water Resources Engineering. He holds a Ph.D. from Kansas State University and has held academic positions since 2016, including Assistant Professor and Associate Professor roles. His research focuses on watershed processes, climate change impacts, and the application of remote sensing technologies. Key areas include ephemeral gully erosion, critical source area identification, and hydrological modeling. He has been recognized with awards such as the 2022 Young Engineer Award (NABEC) and the 2021 Outstanding Associate Editor Recognition (ASABE Journal). Education includes a Ph.D. (2012) and MS (2012) in Biological and Agricultural Engineering from Kansas State University, and a BS (2004) from Acharya N.G. Ranga Agricultural University. His teaching spans undergraduate courses like ENGG 4360 (Soil-Water Conservation Systems Design) and graduate courses such as ENGG 6800 (Deterministic Hydrological Modeling). Research interests emphasize agricultural management's impact on sediment and nutrient transport, climate change adaptation, and precision agriculture. He leads a team of over 30 graduate students and has collaborated on projects funded by agencies like USDA-NIFA and NRCS. His work integrates machine learning, geospatial tools, and hydrological models to address water security and environmental sustainability challenges. Professional contributions include over 100 peer-reviewed publications, with a focus on SWAT modeling, non-point source pollution, and UAV applications. His lab develops decision frameworks for sustainable watershed management, particularly in Ontario's agricultural regions.

Peter WestView profile

Assistant Professor

• Natural Language Processing
• Artificial Intelligence
• Large Language Models
• +3 more

Peter West is an incoming Assistant Professor at the University of British Columbia (UBC) Computer Science Department, specializing in Natural Language Processing (NLP) and AI. His research focuses on understanding the capabilities and limitations of large language models (LLMs) and generative AI systems, emphasizing their divergence from human intuition and alignment challenges. He holds a PhD from the University of Washington (2024), supervised by Yejin Choi, and a BSc (Honours Computer Science) from UBC (2017). His work has been recognized with awards including Best Method Paper at NAACL 2022 and Outstanding Paper awards at ACL 2023 and EMNLP 2023. He conducted internships at the Allen Institute for AI and Microsoft Research’s NLP group. Research interests include analyzing LLM behavior through a natural sciences lens, exploring model capabilities versus human expectations, and developing decoding algorithms to infuse models with algorithmic logic. His recent publications address generative AI paradoxes, constrained text generation, and symbolic knowledge distillation. He serves on panels for NeurIPS workshops and is beginning a postdoc at Stanford with Chris Potts. His research group at UBC seeks students interested in generative AI’s analytical frontiers.

Sid FelsView profile

Professor

• Human Computer Interaction
• 3D displays
• Biomechanical modeling
• +7 more

Sid Fels is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), affiliated with the Faculty of Applied Science. He leads the Human Communication Technologies Laboratory and was the director of the Media and Graphics Interdisciplinary Centre (MAGIC) from 2001 to 2012. His academic rank is Professor, and he is actively involved in faculty roles without any indication of retirement or former status. Education: BASc in Electrical Engineering (University of Waterloo, 1988), MSc and PhD in Computer Science (University of Toronto, 1990 and 1994 respectively). He has been at UBC since 1998 and was recognized as a Distinguished University Scholar in 2004. Research Interests: Focuses on human-computer interaction, biomechanical modeling, neural networks, medical applications, speech synthesis, and interactive arts. His lab explores topics like 3D displays, surgical simulation, and vocal tract modeling. Key projects include mandibular reconstruction simulations and biomechanical modeling for speech production. Publications: Over 150 articles, including recent works on mandibular reconstruction efficacy, AI in education, and vocal tract acoustics. Notable articles from 2025 include studies on surgical planning and chatbot-mediated critical thinking. Awards: Distinguished University Scholar (UBC, 2004). His work bridges computer science, engineering, and biomedical applications, with a strong focus on interdisciplinary projects. Students: Supervised over 20 graduate students, including those in biomechanical modeling, medical imaging, and human-computer interaction. Notable advisees include authors of theses on video-based learning tools and surgical simulation systems. Labs/Teams: Leads the Human Communication Technologies Lab and co-founded MAGIC. Collaborates with clinical partners on projects like mandibular reconstruction and sleep apnea modeling.

William ZergesView profile

Professor

• Molecular Biology
• Cell Biology
• Plant Biology
• +5 more

William Zerges is a Professor in the Department of Biology at Concordia University , specializing in chloroplast biogenesis and photosynthesis mechanisms using the model organism Chlamydomonas reinhardtii . Education: PhD from Princeton University Research Focus: Investigating molecular processes of chloroplast development, including novel compartments analogous to endoplasmic reticulum and stress granules, and translational control mechanisms in chloroplasts. Publication Trends: Recent work explores RNA-Seq analysis of metal nanoparticle impacts, reciprocal regulation of protein synthesis and carbon metabolism, and mRNA localization in organelle targeting. Teaching: Offers courses in Cell Physiology and Advanced Genetics .

Ahmad MohammadpanahView profile

Lecturer

• Dynamics
• Vibrations
• Modal Analysis
• +5 more

Ahmad Mohammadpanah serves as a Lecturer in the Department of Mechanical Engineering within the Faculty of Applied Science at the University of British Columbia (UBC). Holding a Professional Engineer (P.Eng.) designation, he earned his PhD and MASc from UBC alongside an MSc from Sharif University of Technology. His academic profile is anchored in mechanical engineering with specialized expertise in dynamic systems and manufacturing technologies. His educational qualifications include: PhD in Mechanical Engineering, University of British Columbia MASc in Mechanical Engineering, University of British Columbia MSc in Mechanical Engineering, Sharif University of Technology Dr. Mohammadpanah's research centers on Dynamics, Vibrations, and Modal Analysis , with pioneering work in Spherical Parallel Robots and Light Weight Lattice Design for Additive Manufacturing . His exploration of Generative Design & Topology Optimization bridges computational methods with practical manufacturing solutions. Current investigations focus on thermal management in metal extrusion additive manufacturing and vibration control in industrial saw systems, demonstrating strong alignment with industry needs in wood processing and advanced manufacturing. Analysis of his 10 publications (2012-2024) reveals consistent focus on vibration phenomena in rotating machinery and additive manufacturing defect mitigation. His work shows progressive evolution from fundamental dynamics research (2012-2018) toward applied industrial solutions (2021-2024), particularly in metal additive manufacturing and smart lumber production. The integration of machine learning in wood quality prediction (2023) highlights his adaptation to emerging computational techniques. No scientific awards were documented in the provided materials. Regarding academic mentorship, Dr. Mohammadpanah actively supervises undergraduate capstone projects (MECH 45X) and laboratory courses, though formal graduate student advisement isn't specified. His teaching portfolio spans foundational courses like MECH 223 (Introduction to Mechanical Design) through advanced topics including MANU 465 (AI in Manufacturing), reflecting comprehensive engagement with mechanical engineering education. His research operates through the "Intelligent Engineering" initiative, focusing on practical industrial applications rather than dedicated laboratory facilities. Current work emphasizes real-world implementation of vibration control systems and additive manufacturing optimization, with strong industry connections evident through Fpinnovations collaboration and patent development.

• Operations Research
• Healthcare Logistics
• Transportation Systems
• +4 more

Onur Ozturk is an Associate Professor at the Telfer School of Management, University of Ottawa. He holds a PhD in Industrial Engineering from Grenoble Institute of Technology (2012), followed by a postdoctoral fellowship at Ivey Business School (2012–2014). Prior to his current position, he worked as an Assistant Professor at ESIEE Paris (2014–2017). His research focuses on large-scale optimization in healthcare and transport sectors, leveraging scheduling theory, simulation modeling, and stochastic processes. He has contributed to projects such as the Efficacity Institute’s urban freight transport optimization in Île-de-France. Research interests include: Integer linear programming and operations research applications Scheduling and simulation modeling for healthcare logistics and transportation Optimization of pandemic-related operations (e.g., staff scheduling in hospitals, patient admissions) Bi-criteria optimization in medical sterilization and manufacturing systems Recent funded research includes a NSERC grant (2019–2026; $169,000) for batch scheduling methods, and multiple internal grants addressing healthcare routing (2021), medical sterilization (2021), and pandemic staffing (2020–2021). His work emphasizes practical solutions for urban logistics, healthcare capacity planning, and crisis management. No scientific awards are explicitly mentioned in the text. His teaching focuses on business analytics, operations management, and stochastic modeling.

Zoha KibarView profile

Professor

• Molecular Genetics
• Neural Tube Defects
• Chiari I Malformations
• +4 more

Zoha Kibar is an Accredited Professor at the Department of Neuroscience, Faculty of Medicine, University of Montreal. She is affiliated with the Research Center of CHU Sainte-Justine. Her research focuses on the molecular genetics of congenital anomalies in the central nervous and skeletal systems, particularly neural tube defects (NTDs) and Chiari I malformations (CMI). She uses genetics, genomics, and molecular biology to study these conditions, emphasizing gene-environment interactions. During her postdoctoral work at McGill University, she identified the Vangl2 gene in the Loop-tail mouse model, linking it to NTDs. Her lab investigates VANGL1/2 genes in human NTDs, studies novel mouse models via N-ethyl-N-nitrosourea mutagenesis, and employs array-based Comparative Genomic Hybridization for copy number variants in patients. For CMI, she examines genetic predispositions using dog models (Cavalier King Charles Spaniels and Brussels Griffon) to parallel human studies. Her research aims to elucidate molecular mechanisms underlying congenital anomalies, enabling better preventive strategies and genetic counseling. Key findings include the first report of pathogenic VANGL1 mutations in human NTDs. Current projects focus on expanding gene discovery in NTDs/CMI and bridging animal models to human clinical insights.