Find Academic Supervisors

• Online Algorithms
• Resource Allocation
• Energy Systems Optimization
• +4 more

Bo Sun is a Postdoctoral Scholar and Sessional Lecturer at the University of Waterloo. His research focuses on online algorithms, resource allocation, energy systems optimization, and machine learning applications. He is affiliated with the Post Doctoral Scholars, Visitors, and Sessional Lecturers groups at the institution. His work bridges theoretical computer science with practical challenges in energy networks, telecommunications, and distributed systems. Key research interests include developing fair and efficient algorithms for dynamic resource allocation problems, integrating machine learning predictions into competitive online algorithms, and optimizing spatiotemporal workloads with carbon-aware strategies. His publications address topics like group fairness in multi-class systems, carbon footprint reduction in workload management, and energy-efficient resource allocation in 5G networks. Recent work has explored Pareto-optimal algorithms for energy markets, robust online mechanisms with switching costs, and uncertainty-quantified predictions in algorithm design. Despite prolific publication activity, no scientific awards or grants are explicitly mentioned in the provided information. Bo Sun advises no formally listed students but has contributed to collaborative projects involving electric vehicle networks, battery swapping stations, and microgrid energy management. His research often involves interdisciplinary approaches combining operations research, computer science, and applied mathematics to solve real-world optimization challenges.

Sirisha RambhatlaView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Representation Learning
• +4 more

Sirisha Rambhatla is an Assistant Professor at the University of Waterloo, holding appointments in the Management Sciences Department (Faculty of Engineering), Systems Design Engineering, and the David R. Cheriton School of Computer Science (Faculty of Mathematics). She leads the CriticalML lab, focusing on reliable AI for healthcare, aviation, and climate change, leveraging deep learning and spatiotemporal analysis. Her work bridges theory and practice, with publications at top venues like NeurIPS, ICLR, and clinical journals. Affiliations include the Waterloo AI Institute and the Waterloo Institute for Sustainable Aeronautics. Education: Ph.D. and M.S. in Electrical Engineering, University of Minnesota (2019, 2012) B.Tech in Electronics and Telecommunication Engineering, College of Engineering Roorkee (2010) Research Interests: Representation Learning, Spatiotemporal Data Analysis, AI for Healthcare, Computer Vision, and provable algorithms. Her work emphasizes interpretable AI and applications in surgery, intelligent automation, and climate modeling. Awards: 2021 Merit Award for Excellence in Postdoctoral Research E. Bruce Lee Memorial Fellowship University Bronze Medalist (2010) Teaching: Courses include Advanced Machine Learning (MSCI 546/MSE 546), Big Data Analytics (MSCI 623), and Special Topics in Management Engineering (MSCI 598/MSE 598). Labs/Teams: Leads the CriticalML lab, focusing on trustworthy AI systems for critical applications.

Hong ZhangView profile

Assistant Professor

• Large-scale data analytics
• Distributed ML training and serving systems
• Application and network scheduling
• +5 more

Hong Zhang is an Assistant Professor at the University of Waterloo, specializing in large-scale data analytics, distributed machine learning systems, and cloud computing. His research focuses on advancing robust and efficient methods for modern AI systems, including topics like adversarial robustness, zero-knowledge proofs for deep learning, and scalable inference techniques for large language models. He holds a Ph.D. in Computer Science from the Hong Kong University of Science and Technology and dual bachelor's and master's degrees in Electronics and Communications Engineering from Huazhong University of Science and Technology. Education: Ph.D., Computer Science and Engineering, Hong Kong University of Science and Technology (2019) B.Sc. and M.Sc., Electronics and Communications Engineering, Huazhong University of Science and Technology, China (2013) Research Interests: Hong's work bridges theoretical foundations and practical applications in distributed ML systems, data center networking, and secure AI. Key areas include optimizing large-scale training/serve pipelines, mitigating adversarial vulnerabilities, and developing privacy-preserving techniques like zero-knowledge proofs for deep learning. His contributions span both algorithmic innovations and real-world system implementations. Publications Trends: Recent work emphasizes scalable inference acceleration (e.g., Eagle series), trustworthy AI (e.g., generative model guidelines), and cryptographic safeguards for ML (e.g., zkdl/zkllm frameworks). He frequently explores intersections between security, efficiency, and scalability in modern AI systems. Awards/Grants: No specific awards or grants mentioned in available texts. Advising & Labs: While student names aren't listed, his research group likely focuses on distributed systems and secure ML. Active collaborations likely involve cloud computing and adversarial machine learning domains.

• Cold Spray Technology
• Additive Manufacturing
• Materials Durability
• +5 more

Hamid Jahed is a Professor and University Research Chair in Cold Spray Technology at the University of Waterloo's Mechanical and Mechatronics Engineering Department. He directs the Fatigue and Stress Analysis Laboratory (FATSLab), focusing on cold spray technology, materials durability, and structural life enhancement. His research spans additive manufacturing, residual stress analysis, and EV battery durability, with strong industry partnerships. He has received numerous awards, including the International Magnesium Association 2021 Award of Excellence and multiple University of Waterloo distinctions. Education: PhD (1997, University of Waterloo), MEng (1982, University of Houston), BEng (1981, University of Houston). Research interests include cold spray applications (coatings, 3D printing), fracture mechanics, digital twins, and magnesium alloy processing. Recent work explores hydrophilic Teflon coatings, fatigue behavior of additively manufactured alloys, and antimicrobial surface treatments. He supervises 11 graduate students and has trained 100+ researchers. Awards highlight his teaching and research excellence, including the Sandford Fleming Teaching Award and multiple Distinguished Performance Awards. Current projects involve EV battery durability and low-carbon manufacturing innovations. FATSLab investigates fatigue properties of materials under multiaxial loads, residual stress measurement, and cost-effective magnesium alloy fabrication. He collaborates with automotive OEMs and Tier 1 suppliers on large-scale industry projects.

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

Matthias SchonlauView profile

Professor

• Survey Methodology
• Natural Language Processing
• Data Visualization
• +3 more

Matthias Schonlau is a Professor in the Department of Statistics at the University of Waterloo. He previously worked as a statistician at the RAND Corporation (1999-2011), where he led the RAND Statistical Consulting Service. He holds a PhD from the University of Waterloo (1997) and a Master's from Queen's University (1993). His research focuses on survey methodology, natural language processing for open-ended questions, data visualization, and statistical software development. Key contributions include the Hammock Plot for mixed data visualization and automated classification algorithms for open-ended survey responses. His work spans algorithmic innovation (e.g., occupation coding, multi-label classification) and statistical software tools (e.g., HAMMOCK and RFOREST modules for Stata). Recent projects address semi-automated classification, one-shot learning, and text dataset distillation. He has held sabbaticals at the University of Auckland (2015-2016) and DIW Berlin (2009-2010), collaborating with the Max Planck Institute. Major awards include the Humboldt Research Prize (2022) and ASA Fellowship. His publications emphasize bridging statistical methods with practical applications, including books like Applied Statistical Learning (2023) and peer-reviewed articles in computational statistics and machine learning.

David YevickView profile

Professor

• Photonics
• Optical Systems Design
• Machine Learning Applications in Optics
• +3 more

David Yevick is a Professor at the University of Waterloo, affiliated with the Photonics and Atomic, Molecular, & Optical Physics research group. He leads the Advanced Optical Systems Lab and specializes in integrating machine learning with photonics, nonlinear optics, and quantum systems. His work spans optical communication systems, fiber nonlinearity mitigation, and quantum state analysis. Research interests include applying neural networks and deep learning to optical device performance prediction, material science simulations, and signal processing. Recent trends in his publications focus on combining ML techniques with optical systems, such as using random forests for CNT TFET analysis and variational autoencoders for phase transition modeling. His lab explores cutting-edge topics like self-phase modulation compensation in WDM systems and entropy-regulated data balancing. The Advanced Optical Systems Lab also develops novel photonic crystal designs and fiber compensation algorithms.

• 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.

Igor ShinkarView profile

Associate Professor

• Computational Complexity
• Property Testing
• Discrete Mathematics
• +3 more

Igor Shinkar is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on theoretical computer science, discrete mathematics, and probability theory, with particular interests in computational complexity, property testing, and local-to-global phenomena in combinatorial objects. He holds a PhD in Mathematics and Computer Science from the Weizmann Institute of Science (2014), an MSc from the same institution (2009), and a BSc in Mathematics and Computer Science from Tel Aviv University (2005). Shinkar has taught numerous courses at SFU, including CMPT 125 (Introduction to Computing Science and Programming II), CMPT 225 (Data Structures and Programming), CMPT 405/705 (Design and Analysis of Algorithms), and CMPT 706 (Design and Analysis of Algorithms for Big Data). He has also co-taught courses at UC Berkeley and NYU, focusing on topics like coding theory and approximation algorithms. His research explores the interplay between theoretical computer science and mathematics, with contributions to areas such as randomized algorithms, approximation algorithms, and probabilistically checkable proofs. He actively seeks MSc and PhD students interested in theoretical computer science.

John Paul MindaView profile

Professor

• Cognitive Psychology
• Category Learning
• Concept Formation
• +3 more

Dr. John Paul Minda is a Professor in the Department of Psychology at Western University and an associate director of the Western Institute for Neuroscience. He holds a PhD from the University of Buffalo and was a Beckman Fellow at the University of Illinois. His research focuses on categorization, concept formation, and their influence on decision-making. He has authored influential books like *The Psychology of Thinking* and *How to Think*. Dr. Minda is renowned for his teaching excellence, receiving the 2020 Dean’s Award for Teaching and the 2023 OUSA Teaching Excellence Award. He directs the Minda Lab, a cognitive psychology group affiliated with the Brain and Mind Institute. The lab studies how people learn categories and how these concepts shape behavior. Funded by NSERC, SSHRC, and Mitacs, the lab has produced over 100 publications. Recent work explores implicit learning, cultural differences in categorization, and mindfulness interventions. Key lab members include PhD students Anthony Cruz and Chelsea McKenzie, and postdoc Priya Kalra. Dr. Minda advises over 20 graduate and undergraduate students, mentoring through grants and collaborative projects. His teaching emphasizes accessibility and innovation, reflected in courses on cognitive psychology. He lives in London, Ontario, and balances academia with running and family life.

• Geophysics
• Inverse Problems
• Computational Methods
• +5 more

Eldad Haber is a Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia (UBC), affiliated with the Faculty of Science. He holds memberships in the Institute of Applied Mathematics and the CAIDA: UBC ICICS Centre for Artificial Intelligence Decision-making and Action. His research focuses on advancing computational methods at the intersection of geophysics, machine learning, and optimization, with applications in inverse problems, electromagnetic imaging, and reservoir characterization. Key areas of investigation include geophysical inversion techniques, machine learning for data-driven modeling, and the development of efficient algorithms for large-scale problems. Haber’s work integrates disciplines such as geophysics, computer science, and applied mathematics to address challenges in subsurface imaging, environmental monitoring, and resource exploration. Recent research trends in his publications emphasize the application of neural networks to inverse problems, optimization under uncertainty, and multiscale methods for improving computational efficiency. His work on ‘Synthetic Geology’ and ‘Graph Neural Networks’ highlights innovative approaches to geoscientific challenges using deep learning. Collaborations with industry and academic partners have yielded practical solutions for airborne electromagnetic data inversion and mineral prospectivity mapping. His contributions span theoretical advancements in adjoint methods, stochastic optimization, and data assimilation, alongside applied projects in hydrogeology and mineral exploration. Haber’s publications reflect a commitment to bridging computational innovation with real-world geophysical applications.

• Geophysics
• Inverse Theory
• Data Science
• +6 more

Lindsey Heagy is an Assistant Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia (UBC), Faculty of Science. Her research focuses on inverse theory, machine learning, and geophysical data analysis, with applications in mineral exploration, carbon sequestration, and groundwater systems. She leads a group developing open-source software like SimPEG for geophysical simulations and inversions, alongside GeoSci.xyz for geoscience education resources. Key research areas include geophysical inversions of electromagnetic and potential field data, detection of unexploded ordnance (UXO) using machine learning, and collaboration with industry partners on carbon sequestration projects. Heagy actively recruits graduate students and postdoctoral researchers, emphasizing interdisciplinary approaches to geophysical challenges. Her work emphasizes open science practices, with contributions to open-source tools for data processing and reproducible workflows in geophysics. Recent publications highlight advancements in electromagnetic modeling, magnetic vector inversion, and the integration of neural networks in geophysical parameterization. Her lab's projects often involve large-scale datasets and cutting-edge computational methods, reflecting a commitment to both theoretical innovation and practical applications in environmental and resource geoscience.

Roland StullView profile

Professor

• Meteorology
• Numerical Weather Prediction
• Climate Science
• +5 more

Roland Stull is a Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia (UBC), within the Faculty of Science. He directs the Weather Forecast Research Team (WFRT), focusing on numerical weather prediction (NWP) improvements and applications to energy, transportation, and environmental management. His research emphasizes complex terrain meteorology, atmospheric boundary layers, wildfire smoke dispersion, and renewable energy forecasting. He holds fellowships from the American Meteorological Society (AMS) and the Canadian Meteorological & Oceanographic Society (CMOS), and received the Killam Teaching Prize in 2015. Affiliations: UBC since 1995; former roles at University of Wisconsin-Madison (1979–1995). Education: PhD in Atmospheric Science (University of Washington, 1975); BS in Chemical Engineering (University of Washington, 1971). Research Interests: Numerical weather prediction, atmospheric dynamics, wildfire plume modeling, renewable energy meteorology, and climate impacts. His work combines NWP, machine learning, and field observations to enhance forecast accuracy and operational utility. Publications: Over 120 peer-reviewed articles, with recent focus on ensemble methods, analog forecasting, and climate projections. Key topics include precipitation prediction, wind energy forecasting, and fire weather modeling. Awards: AMS Fellow, CMOS Fellow, Killam Teaching Prize. Teaching: Courses include Renewable Energy Meteorology , Weather for Sailing/Flying/Snow Sports , and graduate-level numerical weather prediction. Supervision: Advised over 20 PhD and MSc students, including Elise Legarth on probable maximum precipitation in BC. Research team includes operational NWP forecasting for Canada and international collaborations.

Renjie LiaoView profile

Assistant Professor

• Deep Learning
• Machine Learning
• Computer Vision
• +4 more

Renjie Liao is an Assistant Professor (tenure-track) in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), with an associated appointment in the Department of Computer Science. He is also a Faculty Member at the Vector Institute and a Canada CIFAR AI Chair. Prior to UBC, Dr. Liao was a Visiting Faculty Researcher at Google Brain and held a Senior Research Scientist position at Uber Advanced Technologies Group during his PhD. He earned his B.Eng. (Automation) from Beihang University, M.Phil. (CS) from the Chinese University of Hong Kong, and PhD (CS) from the University of Toronto. His research focuses on probabilistic and geometric deep learning , with key contributions in deep generative models, geometric deep learning, neural algorithmic reasoning, and generalization bounds. Notable areas include 3D point cloud analysis, self-driving systems, and healthcare applications using graph neural networks. His work bridges theoretical foundations (e.g., PAC-Bayes bounds) with practical applications like motion forecasting and medical imaging. Education: B.Eng. in Automation, Beihang University (2011) M.Phil. in Computer Science, CUHK (2015) PhD in Computer Science, UofT (2021) Dr. Liao has received awards such as the RBC Graduate Fellowship and Connaught International Scholarship. His lab (Deep Structured Learning Lab) emphasizes principled mathematical approaches to solving complex problems. He advises students in machine learning, computer vision, and robotics, encouraging applications from those with strong coding/mathematical backgrounds. Labs/Teams: Deep Structured Learning Lab (UBC) Vector Institute Collaboration

• Artificial Intelligence
• Network Science
• Computational Biology
• +3 more

Yong Gao is a Professor of Computer Science, Data Science, and Mathematics at the University of British Columbia (UBC) Okanagan, affiliated with the Irving K. Barber Faculty of Science. He holds a PhD from the University of Alberta and leads research in algorithmic and computational problems in artificial intelligence, network science, and computational biology. His work emphasizes graph theory, probabilistic methods, and applications in social media and biological systems. Educational Background : PhD in Computer Science, University of Alberta Research Interests : Algorithmic foundations of AI and network science Graph-based methods for computational biology and social media analysis Probabilistic modeling of complex systems Awards & Grants : Recipient of multiple NSERC Discovery Grants (2006–2019) UBC Okanagan Startup Grant (2005–2008) Senior Member, Association for the Advancement of AI (AAAI) Professional Roles : Member, Centre for Optimization, Convex Analysis and Nonsmooth Analysis Graduate student supervisor Teaching : Courses in algorithm design, artificial intelligence, discrete mathematics, and network science.