جست‌وجوی استاد و پژوهشگر

Chaitanya Swamyمشاهده پروفایل

استاد

• Combinatorial Optimization
• Approximation Algorithms
• Algorithmic Game Theory
• +۴ مورد دیگر

Chaitanya Swamy is a Professor and University Research Chair in the Department of Combinatorics & Optimization at the University of Waterloo, Canada. His primary affiliation is within the Faculty of Mathematics, and he holds positions in both the Department of Combinatorics & Optimization and the School of Computer Science. He obtained his Ph.D. in Computer Science from Cornell University under the supervision of David Shmoys, followed by postdoctoral research at Caltech's Center for the Mathematics of Information. Swamy’s research focuses on algorithms, particularly in combinatorial optimization, approximation algorithms, algorithmic game theory, stochastic optimization, network design, scheduling, and online algorithms. His work spans theoretical contributions and practical applications, including algorithm design for facility location, network routing, and mechanism design. He has contributed to foundational results in approximation algorithms, such as the development of primal-dual methods and LP-rounding techniques. Swamy has held significant editorial roles, including as an associate editor for Discrete Optimization and SIAM Journal on Computing . He has organized major conferences like CanaDAM 2021 and sessions at ISMP 2018. His teaching record includes courses on combinatorial optimization, scheduling, and algorithmic game theory. He has advised numerous Ph.D. and Master’s students, many of whom have gone on to prestigious academic and industry positions. Swamy’s research has been recognized through awards for his students, including the University of Waterloo Alumni Gold Medal. He actively contributes to the academic community through committee work for conferences like STOC, APPROX, and SODA, and his publications reflect a deep engagement with both theoretical and applied aspects of algorithms and optimization.

Nidhi Hegdeمشاهده پروفایل

دانشیار

• Privacy-Preserving Machine Learning
• Algorithmic Fairness
• Distributed Systems
• +۲ مورد دیگر

Dr. Nidhi Hegde is an Associate Professor in the Department of Computing Science at the University of Alberta and a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute (Amii). Her research focuses on privacy-preserving machine learning, algorithmic fairness, and robust algorithm design for networked systems. Dr. Hegde's current research investigates differential privacy in bandit algorithms, debiasing frameworks for language models, and long-term fairness guarantees for minority groups. Her work combines theoretical foundations with practical applications in distributed systems and multi-agent learning environments. Recent publications address covariate shift effects in optimization, private matroid optimization, and reinforcement learning with functional noise. She teaches graduate courses on Responsible AI and Ethical Issues in Data Analytics, covering topics including data privacy, fairness in algorithms, interpretability, and accountability. Dr. Hegde maintains active research collaborations and previously led privacy research at Borealis AI (RBC's research institute).

Zukui Liمشاهده پروفایل

استاد

• Mathematical Optimization
• Machine Learning
• Process Systems Engineering
• +۴ مورد دیگر

Zukui Li is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering, where he leads a research group focused on mathematical optimization, machine learning, and process systems engineering. His work spans oil sands extraction, steel production, biomedical applications, and advanced optimization methods. Education: Ph.D. in Chemical Engineering, Rutgers University (2010) M.Sc. in Control Theory and Control Engineering, University of Science and Technology of China (2005) B.Sc. in Automatic Control, University of Science and Technology of China (2002) Postdoctoral Training: Princeton University (2010-2012) Research Focus: Dr. Li's research integrates mathematical optimization and machine learning for complex process systems. His primary areas include: Advanced optimization techniques (robust, stochastic, and distributionally robust optimization) Machine learning applications in process monitoring and biomedical systems Industrial applications in energy, manufacturing, and resource extraction Specific innovations include physics-informed ML for anemia treatment, adaptive optimization for steel production, and distributionally robust methods for uncertainty management. Publication Trends (2019-2023): Recent articles demonstrate a strong focus on uncertainty-aware optimization methods, with increasing integration of machine learning techniques. Dominant themes include distributionally robust optimization, adaptive decision-making under uncertainty, neural network approximations for complex constraints, and applications in industrial process control and biomedical systems. Theoretical advancements are consistently coupled with practical implementations in energy and manufacturing sectors. Research Group: Leads an active team developing optimization frameworks and machine learning solutions for process engineering challenges. Group website: Dr. Zukui Li's Research Group

• Stochastic Optimization
• Chance Constrained Optimization
• Distributionally Robust Optimization
• +۹ مورد دیگر

Professor Abdel Lisser is affiliated with CentraleSupélec, where he conducts research in Gif-sur-Yvette, France. His work spans multiple disciplines including stochastic optimization, game theory, and machine learning. Research Interests: Stochastic Optimization, Chance Constrained Optimization, Distributionally Robust Optimization, Stochastic Game Theory, Physics-Informed Neural Networks. His recent publications focus on integrating stochastic programming with deep learning frameworks to address complex optimization problems under uncertainty. Key areas include Markov Decision Processes, joint chance constraints, and applications in autonomous vehicle control and network design. In 2025, he published on single-controller stochastic games, convex approximations for Markov processes, and physics-informed neural networks for nonlinear equations. 2024 contributions include distributionally robust Markov decision processes, neurodynamic optimization, and CNN-based equilibrium prediction in games. Email: abdel.lisser@l2s.centralesupelec.fr Institution: L2S, CentraleSupélec Location: 3 rue Joliot Curie, 91190 Gif-sur-Yvette, France

Erick Delageمشاهده پروفایل

استاد

• Optimization under Uncertainty
• Operations Research
• Robust Optimization
• +۷ مورد دیگر

Erick Delage is a Professor in the Department of Decision Sciences at HEC Montréal, holding the Canada Research Chair in Decision Making Under Uncertainty. He is a member of the Group for Research in Decision Analysis (GERAD) and an associate academic member of MILA. His research focuses on optimization under uncertainty, robust and stochastic optimization, machine learning, and risk management, with applications in finance, energy systems, and transportation. Delage holds a Ph.D. in Electrical Engineering from Stanford University, where he worked with renowned scholars like Andrew Y. Ng and Yinyu Ye. His teaching includes courses on Quantitative Risk Management, Decision Analysis, and Robust Optimization at institutions like HEC Montréal, Politecnico di Milano, and EPFL. He has supervised numerous PhD and master's students, leading to impactful contributions in areas like distributionally robust optimization and deep reinforcement learning for financial engineering. Delage's work emphasizes bridging theory and practice, with notable contributions to contextual optimization methods, energy transition pathways, and risk-averse decision-making. His research has been recognized with awards such as the Nicholson Award (2008) and membership in the Royal Society of Canada's College of New Scholars (2020). His laboratories and collaborations include the Supply Chains and Mobility research cluster funded by IVADO, focusing on data-driven decision-making for resilient systems. Key grants include leadership in energy transition optimization and robust supply chain frameworks.

• Optimal Transportation
• Mathematical Economics
• Mathematical Physics
• +۳ مورد دیگر

Brendan Pass is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on optimal transportation theory, with applications in mathematical economics, mathematical physics, and density functional theory. He is a core member of the Kantorovich Initiative, an interdisciplinary group dedicated to advancing optimal transport methodologies and their practical implementations. His work bridges pure mathematics with applied fields, addressing complex problems in multi-marginal transport, economic equilibrium modeling, and geometric analysis. Pass’s academic contributions span theoretical frameworks for optimal transport between spaces of differing dimensions, cyclic cost structures, and robust risk management. He explores intersections with diverse disciplines, including econometrics, financial mathematics, and geometric probability. His publications often address uniqueness conditions for Monge solutions, Wasserstein metrics, and barycenter problems in metric spaces. He has co-authored works on topics ranging from hedonic pricing models to vectorial martingale transport. Recent research trends include leveraging optimal transport for inequality measurement, robust optimization, and data denoising. His methodologies emphasize structural analysis and algorithmic design, with applications in crop root systems, quantum chemistry (via density functional theory), and option pricing. Pass frequently collaborates with institutions globally, contributing to both theoretical advancements and real-world problem-solving. Teaching responsibilities include advanced mathematics courses such as Math 156 (Calculus for Business/Economics) and Math 300 (Boundary Value Problems). His scholarly output includes over 50 peer-reviewed articles, conference contributions, and edited volumes. Current projects explore ODE characterizations of transport problems, distributional robustness, and partial identification in econometric models.

Utsav Sadanaمشاهده پروفایل

استادیار

• Mathematical optimization
• Machine learning
• Game theory
• +۵ مورد دیگر

Utsav Sadana is an Assistant Professor of Operations Research in the Department of Computer Science and Operations Research (DIRO) at the University of Montréal. He holds affiliations with IVADO, CIRRELT, and GERAD research centers. His academic journey includes a postdoctoral fellowship at McGill University and a PhD in Management Science from HEC Montréal under Prof. Georges Zaccour. His research spans mathematical optimization, machine learning, and game theory with applications in decision-making under uncertainty. Key interests include entropic risk measures, impulse control in differential games, and contextual optimization methods. His work integrates theoretical foundations with practical applications in security, insurance, and technology adoption. His recent publications demonstrate a strong focus on distributionally robust optimization, risk-averse decision making, and game-theoretic approaches to dynamic systems. The 2025 European Journal of Operational Research survey synthesizes advances in contextual optimization, while his 2023 INFORMS Journal paper establishes the value of randomized strategies in network security contexts. As an educator, he teaches Methods of Operations Research (IFT-6575) and Dynamic Programming (IFT-6521) at the University of Montréal, and previously taught Business Statistics at McGill University. Lead researcher on Decision-making in dynamical systems (2024-2030) funded by NSERC Discovery Grant Co-researcher on Integration of Environmental Data in Urban Planning (2024-2026) with MITACS Acceleration funding His research methodology combines theoretical rigor with computational implementation, evidenced by his GitHub repositories containing optimization solvers and experimental code for risk analysis.

Miguel F. Anjosمشاهده پروفایل

استاد

• Mathematical Optimization
• Operational Research
• Conic Optimization
• +۶ مورد دیگر

Miguel F. Anjos is a Professor in the School of Mathematics at the University of Edinburgh, where he serves as Deputy Head of School and Head of the research theme Data and Decisions. He also holds an Associate Professor position at Polytechnique Montréal in the Department of Mathematics and Industrial Engineering. Additionally, he chairs the Mathematical Optimization Society (2023-2025) and serves as Vice-President (International Activities) of INFORMS. His research focuses on mathematical optimization to solve large-scale discrete nonlinear optimization problems in engineering applications, particularly in conic optimization for facility layout problems and optimal power flows in electricity grids. His work addresses critical challenges in smart grid development, including efficient network utilization, renewable energy integration, energy storage, and customer participation in grid operations. He has extensive industry experience working with National Grid ESO, Hydro-Québec, Schneider Electric, Rio Tinto, and EDF. His publications reveal a strong focus on optimization methods applied to energy systems, with recent work spanning bilevel optimization, robust optimization with uncertainty quantification, electric vehicle infrastructure planning, and carbon credit markets. His research consistently bridges theoretical optimization with practical engineering applications. Canada Research Chair in Nonlinear Discrete Optimization in Engineering (2011-2016) NSERC-Hydro-Québec-Schneider Electric Industrial Research Chair in Smart Grid Optimization (2016-2019) INRIA International Chair for Optimizing Smart Grids (2016-2021) Winner of MOPTA 2020 and 2024 competitions Queen Elizabeth II Diamond Jubilee Medal (2012) Fellow of the Humboldt Foundation, EUROPT, and Canadian Academy of Engineering Professor Anjos has supervised numerous PhD students and postdoctoral fellows, with graduates securing positions at academic institutions including University of Tennessee, Western Ontario University, and industry roles at Amazon, CAISO, and major consultancies. He maintains three benchmark datasets (QAPLIB, FLPLIB, and the Jones Benchmark) as a service to the optimization community.

Jun Caiمشاهده پروفایل

استاد

• Actuarial Science
• Risk Management
• Quantitative Finance
• +۲ مورد دیگر

Jun Cai is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, specializing in actuarial science, quantitative risk management, and insurance mathematics. His research develops mathematical frameworks for risk assessment and management in insurance and finance. Dr. Cai's work focuses on developing risk measures and optimization techniques for reinsurance contracts, capital allocation, and portfolio management. Recent research explores distributionally robust optimization, worst-case risk analysis under uncertainty, and applications of behavioral economics to risk assessment. His work has practical applications in insurance regulation and financial risk management. His publication record includes foundational contributions to risk measure theory, reinsurance contract design, and multivariate risk assessment. Recent methodological innovations address complex problems in capital allocation and portfolio optimization under uncertainty.

• Continuous Optimization
• Machine Learning
• Design and Analysis of Optimization Algorithms
• +۳ مورد دیگر

Jiajin Li is an Assistant Professor in the Operations and Logistics Division at the UBC Sauder School of Business, concurrently affiliated with the Institute of Applied Mathematics (IAM) at the University of British Columbia. He holds a BSc in Statistics from Chongqing University and a PhD in Systems Engineering and Engineering Management from the Chinese University of Hong Kong (CUHK). Prior to joining UBC, he was a postdoctoral researcher at Stanford University's Department of Management Science and Engineering. His research focuses on continuous optimization , design and analysis of optimization algorithms , and machine learning , with emphasis on nonsmooth/nonconvex optimization, minimax problems, and distributionally robust optimization. His work bridges theoretical foundations with applications in graph data analysis and algorithmic stability. He teaches courses including Logistics and Operations Management (BCom), Seminar on Theoretical Developments in Management (PhD), and Optimization Theory and Applications (PhD). He actively seeks PhD students with strong mathematical or coding backgrounds through UBC Sauder or IAM programs. Key research contributions include advancements in primal-dual balancing for minimax optimization, stability evaluation via distributional perturbation analysis, and outlier-robust algorithms for graph data. His methodologies often integrate optimal transport theory and subdifferential analysis. He is affiliated with multiple research communities, including the IEEE and conferences like NeurIPS, ICML, and ICLR. His work emphasizes algorithmic robustness and scalability in complex optimization landscapes.

Aiqi Zhangمشاهده پروفایل

استادیار

• Robust Optimization
• Data-Driven Analytics
• Socially Responsible Operations
• +۳ مورد دیگر

Aiqi Zhang is Assistant Professor of Operations and Decision Sciences at Wilfrid Laurier University's Lazaridis School of Business and Economics, with adjunct appointment at University of Waterloo. Holding PhD in Operations Research from Chinese University of Hong Kong and BS in Statistics from University of Science and Technology of China, his research develops optimization methodologies for socially responsible operations. Research focuses on: Distributionally robust optimization frameworks Socially responsible and sustainable operations Networked distribution systems Data-driven decision models Publications appear in Management Science and award-winning conference proceedings. Recognized with MSOM Data Driven Research Challenge award for distribution center operations research. Current projects examine stormwater management for climate resilience and optimization in group testing contexts.

Yossiri Adulyasakمشاهده پروفایل

استاد

• Supply Chain Analytics
• Optimization under Uncertainty
• Stochastic Optimization
• +۶ مورد دیگر

Yossiri Adulyasak is a Full Professor in the Department of Operations and Logistics Management at HEC Montréal, where he was promoted to this position effective June 1, 2025. He also holds an appointment as Associate Professor in the Department of Computer Science and Operational Research at the University of Montreal since May 2022. Adulyasak is a member of the GERAD research center since February 2017 and holds the Canada Research Chair in Supply Chain Analytics. His educational background includes a Ph.D. in Operations and Logistics Management from HEC Montréal, where his doctoral research focused on planning systems in supply chains, and a Master of Civil Engineering in Transportation Engineering from Chulalongkorn University. Professor Adulyasak's research centers on supply chain analytics, with particular emphasis on stochastic and robust optimization under uncertainty. His work bridges operations research, artificial intelligence, and supply chain management, focusing on real-world applications in logistics, inventory management, and sequential decision-making processes. He investigates how to integrate demand forecasting with optimization frameworks and develops algorithms that can predict disruptions and recommend mitigation measures in supply chain operations. His recent publications demonstrate a strong trend toward integrating AI and machine learning techniques with traditional optimization methods to address complex supply chain challenges. The research spans multiple domains including electric vehicle routing, drone delivery, facility location under uncertainty, and pandemic response strategies. His work frequently appears in top-tier journals across operations research, transportation science, and management science. Canada Research Chair in Supply Chain Analytics (2018-present) Prix d'impact en recherche (HEC Montréal, 2024) Prix du meilleur cas de gestion (2024) 1st place for Prix de la pratique (Société canadienne de recherche opérationnelle, 2024) Best Paper Award for 2021 ('The value of aggregate service levels in stochastic lot sizing problems') Professor Adulyasak has supervised numerous doctoral and master's students, with 3 doctoral theses completed or in progress, 5 master's theses, and 37 supervised master's projects. His students have worked on diverse topics including inventory management, demand forecasting, supply chain network design, and optimization of logistics systems. He has collaborated extensively with industry partners including Bombardier, Pratt & Whitney Canada, and Hydro-Québec on research projects addressing real-world supply chain challenges. As an active member of GERAD and holding the Canada Research Chair in Supply Chain Analytics, Adulyasak leads a research group focused on developing advanced optimization techniques for supply chain applications. His team works at the intersection of operations research, artificial intelligence, and business analytics to create innovative solutions for complex decision-making problems under uncertainty.

Borzou Rostamiمشاهده پروفایل

استادیار

• Operations Research
• Supply Chain Management
• Optimization
• +۴ مورد دیگر

Borzou Rostami serves as an Assistant Professor in the Operations and Decisions Sciences department at the Lazaridis School of Business and Economics, Wilfrid Laurier University, and concurrently holds a research position at the Canada Excellence Research Chair in Data Science for Real-Time Decision-Making at Polytechnique Montreal and CIRRELT. His educational background includes: PhD in Information Technology/Operations Research from the Polytechnic University of Milan, Italy Dr. Rostami's research spans operations research and supply chain analytics , with emphasis on integrated planning (location-routing, production-distribution), network design, large-scale optimization techniques (Benders decomposition, column generation), data-driven methods (stochastic programming, distributionally robust optimization), and machine learning applications for real-time logistics decision-making. His work bridges theoretical optimization with practical supply chain challenges. Prior to Laurier, he completed postdoctoral research at Polytechnique Montreal, École de Technologie Supérieure, and TU Dortmund. He actively supervises graduate students and collaborates through CIRRELT, focusing on enterprise networks and transportation systems.

Yossiri Adulyasakمشاهده پروفایل

استاد

• Supply Chain Analytics
• Optimization under Uncertainty
• Large-Scale Optimization
• +۴ مورد دیگر

Yossiri Adulyasak is a Full Professor in the Department of Logistics and Operations Management at HEC Montréal, holding the Canada Research Chair in Supply Chain Analytics. He is also an Adjunct Professor at the University of Montreal's Department of Computer Science and Operations Research and a scientific advisor at IVADO Labs. His research focuses on supply chain optimization, data-driven decision-making, and robust optimization under uncertainty, with applications in retail, transportation, and humanitarian logistics. Education: PhD in Operations and Logistics Management (HEC Montréal), Master of Civil Engineering in Transportation Engineering (Chulalongkorn University). Research Interests : Supply chain analytics, large-scale optimization algorithms, stochastic programming, machine learning integration, and resilient supply chain design. His work emphasizes practical applications in logistics networks, inventory management, and dynamic resource allocation under uncertainty. Recent Articles : Focus on AI-driven retail analytics, robust optimization for lot-sizing and facility location, and humanitarian logistics. Key topics include pandemic response strategies, electric vehicle routing, and drone delivery systems. Awards : Recipient of the 2021 Prix de recherche Chenelière Éducation/Gaëtan Morin for excellence in scientific publications. Supervision : Directed 3 PhD dissertations and over 40 master’s and project supervisions, collaborating with companies like Bombardier, Pratt & Whitney Canada, and JDA. Active in industrial partnerships for supply chain optimization solutions. Lab/Teams : Member of GERAD (Group for Research in Decision Analysis) and IVADO Labs, contributing to cross-disciplinary AI and operations research initiatives.