Submodular Functions Professors and Supervisors in Canada

Nils Wildeمشاهده پروفایل

استادیار

Dalhousie UniversityCanada

Nils Wilde is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He specializes in robotics, AI, and human-computer interaction, with a focus on cognitive robotics, multi-robot systems, and human-robot interaction. His research integrates planning, optimization, control, and machine learning to develop interactive and adaptive robotic systems. His educational background includes: BSc and MSc in Computer Science or related field from Technical University Berlin (2012, 2016) PhD in Electrical and Computer Engineering from the University of Waterloo (2016–2020), co-supervised by Dana Kulić and Stephen L. Smith Postdoctoral Fellow at TU Delft (2021–2024) in the Autonomous Multi-Robots Lab with Javier Alonso-Mora Postdoctoral Fellow at the University of Waterloo’s Autonomous Systems Lab (until August 2021) Nils Wilde's research centers on enabling robots to learn from human feedback and adapt to user preferences in dynamic environments. His work spans preference learning , multi-objective planning , motion planning , task assignment in multi-robot systems , and human-robot interaction . He develops algorithms that allow robotic systems to balance competing objectives such as efficiency, safety, and user comfort, particularly in service robotics applications like hospitals and industrial facilities. His recent publications (2020–2024) demonstrate a strong trajectory in top robotics venues (T-RO, RA-L, ICRA, IROS, CoRL, CDC, WAFR), with a focus on multi-objective optimization, dynamic vehicle routing, sensor scheduling, and learning user preferences. A key theme is improving the quality of service in robotic systems by optimizing metrics like waiting times, statistical distinctness of plans, and user satisfaction, often through novel cost functions and learning frameworks. Nils is actively building a new robotics lab at Dalhousie University, with funded PhD positions and an interdisciplinary research environment. He is involved in organizing academic workshops, such as the upcoming 2025 RSS workshop on Multi-Objective Optimization and Planning in Robotics. He mentors prospective students and encourages applications from diverse backgrounds.

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Ismail Ben Ayedمشاهده پروفایل

دانشیار

École de Technologie SupérieureCanada

Ismail Ben Ayed is an Associate Professor at École de technologie supérieure (ETS) in Montreal, Canada, holding the ETS Research Chair on Artificial Intelligence in Medical Imaging. His research bridges computer vision, optimization, and medical image analysis to develop advanced algorithms for clinical applications, with particular focus on cardiac and neurological imaging. His research program centers on medical image segmentation using novel optimization techniques, graph-based methods, and deep learning models. He pioneers approaches for handling volumetric bias, shape compactness, and distribution matching in MRI and cardiac imaging, directly addressing clinical challenges in spine labeling, ventricle segmentation, and tumor detection. His work emphasizes mathematical rigor combined with practical medical relevance. Analysis of his 15 most recent publications (2014-2017) reveals dominant themes in medical image segmentation (80% of works), particularly for cardiac MRI (35%) and neurological applications (25%). Key methodological contributions include distributed optimization frameworks (20%), advanced graph cut techniques (30%), and deep learning architectures (25%), published consistently in top-tier venues including CVPR, MICCAI, and TPAMI. His scientific recognition includes: MICCAI travel award (2017) Outstanding Reviewer Award at CVPR (2015) GE innovation award (2010) He actively mentors researchers as evidenced by his recruitment of PhD students and postdocs, with research supported by the ETS Research Chair and multiple patents. His service includes chairing MICCAI 2017/2015 and IPTA 2017, plus continuous program committee roles at CVPR, ICCV, and MICCAI since 2011. Leading the ETS Research Chair on AI in Medical Imaging, he directs a collaborative team working on clinical translation of computer vision techniques. Current projects focus on cardiac motion analysis, brain tumor segmentation, and spine labeling systems with direct applications in radiology workflows.

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Roie Levinمشاهده پروفایل

استادیار

McGill UniversityCanada

Roie Levin is an Assistant Professor at Rutgers University's Department of Computer Science. He received his PhD in Algorithms, Combinatorics and Optimization from Carnegie Mellon University in 2022, advised by Anupam Gupta. Prior to that, he worked at the Allen Institute for Artificial Intelligence (2015-2017) and earned dual BSc degrees in Computer Science/Applied Mathematics and Mathematics from Brown University (2015). Before joining Rutgers, he was a Fulbright Postdoctoral Fellow at Tel Aviv University under Niv Buchbinder. Current Role: Assistant Professor in Computer Science Academic Training: PhD (2022) CMU, BSc (2015) Brown University Postdoctoral: Fulbright Fellow at Tel Aviv University Levin's research focuses on approximation algorithms for uncertain environments (online/dynamic/streaming models) and submodular function optimization. His work spans theoretical foundations and practical implementations across distributed systems, geometric constraints, and reinforcement learning paradigms. Teaching includes graduate and undergraduate algorithms courses (CS 344, CS 513) with emphasis on problem-solving techniques, computational complexity, and modern algorithmic trends. His publications showcase expertise in online algorithms, submodular optimization, and approximation theory with applications in clustering, caching, and machine learning. The 2025 articles demonstrate continued exploration of online consistency and contention resolution, while 2023-2024 works focus on submodular optimization under uncertainty and dynamic environments. Earlier publications (2015-2017) cover semantic parsing, geometric approximation, and planar graph optimization. Fulbright Postdoctoral Fellow Levin's research connects theoretical guarantees with practical implementations, bridging classical algorithm design with modern machine learning applications. His recent work explores primal-dual methods in online settings and robust subspace approximation techniques for streaming data environments.

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Stephen L. Smithمشاهده پروفایل

استاد

University of WaterlooCanada

Stephen L. Smith is a Professor and Tier 2 Canada Research Chair in the Department of Electrical and Computer Engineering at the University of Waterloo. He serves as Co-Director of the Waterloo Artificial Intelligence Institute, leading research in robotics and AI. His work focuses on autonomous systems, multi-robot coordination, and human-robot interaction, with applications in navigation, control systems, and collaborative task planning. Smith's research explores challenges such as path planning in uncertain environments, persistent monitoring, and adaptive decision-making. His contributions include algorithms for robot navigation in ice-covered waters, real-time replanning under constraints, and optimizing multi-agent systems for efficiency and safety. Key scientific awards include the Tier 2 Canada Research Chair designation. His research also addresses human-centric aspects, such as studying user preferences in collaborative tasks and developing frameworks for learning user intent through interaction.

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Max Nendelمشاهده پروفایل

دانشیار

University of WaterlooCanada

Max Nendel is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a PhD in Mathematics from the University of Konstanz (2017), advised by Robert Denk and Michael Kupper. Prior to his current role, he was a junior professor at Bielefeld University’s Center for Mathematical Economics, where he was a Principal Investigator in the Collaborative Research Center 1283 and the Research Training Group 2865. His research focuses on model uncertainty in economics, finance, and actuarial science, emphasizing valuation of financial/insurance products under uncertainty using nonlinear partial differential equations. He also explores mathematical aspects of regulatory policy, risk measures, and mean field games. His work bridges theoretical analysis and practical applications in quantitative finance and risk management. Nendel’s recent research trends include advancing viscosity solution techniques for abstract Cauchy problems, optimal control under Lévy processes, and mean field game equilibria. He has contributed to foundational topics like convex semigroups, operator theory in mixed topologies, and risk aggregation under dependence uncertainty. He has held research roles in Germany and Canada, with a strong record of interdisciplinary collaborations. His work is supported by grants from major research councils, reflecting his expertise in both pure and applied mathematics.

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Nicholas Harveyمشاهده پروفایل

استاد

University of British ColumbiaCanada

Nicholas Harvey is a Professor in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. He holds affiliations as an Associate Member in the Department of Mathematics and serves as Director of the Mathematics of Information, Learning, and Data (MILD) research cluster. His research focuses on randomized algorithms, convex optimization, and machine learning theory, with notable contributions to online learning, submodular functions, and algorithmic aspects of discrepancy theory. Harvey's academic journey includes significant work on problems such as prediction with expert advice, learning mixtures of Gaussians, and graph algorithms. He has supervised numerous graduate students, including notable researchers like Huang Fang and Victor Sanches Portella. His work has been recognized with awards such as the Best Paper Award at NeurIPS 2018 and the Excellence in Teaching Award in 2024. His research outputs span theoretical computer science, optimization, and machine learning, with key contributions in algorithms for submodular functions, online convex optimization, and probabilistic methods. He has also contributed to practical applications, such as algorithms for network coding and efficient failure notification systems in distributed networks. Harvey is actively involved in teaching, including pioneering courses on randomized algorithms and machine learning theory. His research group collaborates on projects related to the MILD cluster, focusing on interdisciplinary applications of data science and mathematics.

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Tim Hoheiselمشاهده پروفایل

دانشیار

McGill UniversityCanada

Tim Hoheisel is an Associate Professor at McGill University's Department of Mathematics and Statistics. His research focuses on nonsmooth optimization, variational analysis, and their applications in continuous optimization and machine learning. He is the director of the CRM Applied Math Laboratory and co-chair of the CRM thematic program on Mathematical Foundations of Data Science (2025). He serves as an Associate Editor for *Mathematics of Operations Research* and *Open Journal of Mathematical Optimization*. Hoheisel has organized conferences and seminars, including the CRM Applied Math Seminar in Montreal. He has advised numerous PhD and MSc students and postdocs, collaborating on topics like regularization methods, inverse problems, and convex analysis. Education: PhD in Mathematics from University of Würzburg (2009). Committee Roles: Advisor for Honours Applied Mathematics (B.Sc.) Program, elected member of the Chair's Advisory Committee. His research integrates theoretical analysis with applications, bridging applied and pure mathematics. Recent work includes contributions to the Maximum Entropy on the Mean method for inverse problems, Lipschitz stability in LASSO, and regularization techniques. Hoheisel is a member of SIAM and the MTL MLOpt group, focusing on optimization-machine learning interfaces. Recent presentations include a semi-plenary talk at the 2025 International Conference on Continuous Optimization and lectures on regularized least-squares at the University of Trier. His publications span optimization theory, convex analysis, and applications in image processing and quantum Monte Carlo data analysis. Awards: None explicitly mentioned. Grants/Funding: Not detailed; inferred from editorial and leadership roles. He leads the CRM Applied Math Lab and collaborates with institutions like Concordia University and the University of Ottawa through INTER-MATH-AI. His work emphasizes interdisciplinary approaches, particularly in data science and AI integration with mathematical optimization.

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Kimberly YUمشاهده پروفایل

دانشیار

University of MontrealCanada

Kimberly Yu is an Associate Professor in the Department of Computer Science and Operations Research at Université de Montréal’s Faculty of Arts and Sciences. Her research focuses on nonlinear programming, theoretical computer science, and optimization theory. She leads major research projects on mixed-integer nonlinear programming, funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). Her work bridges foundational algorithmic research and real-world applications, particularly in submodular optimization and computational methods. Dr. Yu’s academic background positions her at the intersection of computer science and mathematics. She teaches advanced courses such as IFT1575 (Operational Research Models) and IFT6551 (Integer Programming). Her research explores cutting-edge topics like DR-submodular minimization, integral invariants in computer vision, and polyhedral approaches to combinatorial optimization. Her research grants emphasize innovation in discrete optimization, with projects like "Theory and Algorithms for Mixed-Integer Nonlinear Programming" (2024–2030) advancing computational methods for complex decision-making systems. Kimberly Yu collaborates actively with researchers in computer science and operations research, contributing to both theoretical advancements and practical applications in fields like algorithm design and machine learning.

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Bruce Shepherdمشاهده پروفایل

استاد

University of British ColumbiaCanada

Optimization
Algorithms
Convex Geometry & Polyhedral Combinatorics
+۵ مورد دیگر

Bruce Shepherd is a Professor in the Department of Computer Science at the University of British Columbia. Previously, he held roles at the London School of Economics (LSE), Bell Laboratories, and McGill University. His academic journey includes a BSc from the University of Victoria, followed by an MSc and PhD in Combinatorics and Optimization from the University of Waterloo under supervision of Dan Younger and Bill Pulleyblank. He has held prestigious fellowships, including NATO Postdoctoral and Von Humboldt, and served as acting director of the Institute of Applied Mathematics at UBC (2019-2020). Research focuses on optimization, algorithms, graph theory, and network design , with applications in real-time systems, scheduling, and pricing. Key contributions include work on multiflow topologies, Gomory-Hu trees, and resilient network design. He has authored over 80 papers and served on editorial boards for journals like Mathematical Programming and SIAM-MPS Series . Awards and Recognition : NATO Postdoctoral Fellowship (1990s) Von Humboldt Fellowship (1990s) Nuffield Foundation Award (1990s) James McGill Professor (McGill University, 2007-2017) Professional Activities : Co-editor of Mathematical Programming Series A (2007-2016). Visiting positions at Microsoft Research, University of Washington, and the Fields Institute. Organized workshops on discrete optimization, data science, and robust network design.

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