Integer Programming Professors and Supervisors in Canada | ApplySharif

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استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۱-۱۵ از ۱۰۵ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Kuldeep S. Meelمشاهده پروفایل

دانشیار

University of TorontoCanada

Kuldeep S. Meel is the Stephen Fleming Early-Career Associate Professor at the School of Computer Science, Georgia Institute of Technology, and an Associate Professor at the University of Toronto (on leave). He previously held a NUS Presidential Young Professorship at the National University of Singapore. His research focuses on automated reasoning, aiming to enable computing systems to handle uncertain real-world environments through scalable techniques integrating randomized algorithms, statistical inference, formal methods, distribution testing, and software engineering. Core research areas: Automated Reasoning, Formal Methods, Approximate Model Counting, Probabilistic Inference, Constraint Solving His research group has achieved significant recognition in both individual awards and publications. Key trends in his recent work include advancing model counting algorithms, developing frameworks for probabilistic explanations, and improving scalability in formal verification and constraint satisfaction. His tools have consistently ranked top in international competitions, demonstrating practical impact in automated reasoning. 2019 NRF Fellowship for AI 2022 ACP Early Career Researcher Award 2020 IEEE Intelligent Systems AI's 10 to Watch Top placements in Model Counting, SAT, and CAV competitions He mentors a diverse group of PhD and Master's students and collaborates with institutions worldwide. His group's publications span premier conferences in AI, formal methods, and design automation, reflecting interdisciplinary contributions to theoretical and applied computer science.

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Mariana Resenerمشاهده پروفایل

استادیار

Simon Fraser UniversityCanada

Mariana Resener is an Assistant Professor in the School of Sustainable Energy Engineering at Simon Fraser University (SFU). She holds a Ph.D. in Electrical Engineering (2016) from the Federal University of Rio Grande do Sul, Brazil, alongside M.Sc. (2011) and B.Sc. (2008) degrees in the same field. Her research focuses on optimizing power systems, particularly in distributed energy resources, energy storage, and volt/var control. She teaches courses like Power Electronics and Power Systems Analysis & Design. Her work emphasizes sustainable development in grid planning and energy infrastructure. As a Senior Member of IEEE and an Associate Editor for the Energy Systems Journal (Springer), she contributes to advancing smart grid technologies and renewable integration. Her research spans metaheuristic optimization, stochastic modeling, and grid resilience strategies for distributed systems. Recent projects include hybrid renewable energy systems for substations, EV charging station optimization, and fault analysis in unbalanced grids. She collaborates with industry on practical solutions for grid modernization and reliability enhancement.

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Tamon Stephenمشاهده پروفایل

استاد

Simon Fraser UniversityCanada

Tamon Stephen is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. His research focuses on operations research, with an emphasis on combinatorial optimization, algorithms, discrete geometry, and computational biology. He holds a Ph.D. in Mathematics from the University of Michigan (2002). His work often bridges theoretical and computational aspects, addressing interdisciplinary applications. Stephen is affiliated with the Centre for Operations Research and Decision Sciences (CORDS) and has contributed to software tools for hypergraph transversals and colorful linear programming. He has taught courses such as Math 208W (Introduction to Operations Research) and has advised projects in metabolic network analysis and scheduling optimization. His office is located at the Surrey campus (SRYC 2886). Key research collaborations include studies on firefighter scheduling, nurse rostering, and metabolic pathway analysis. His methodologies often leverage algorithm design, polytope theory, and discrete mathematics. Stephen actively participates in academic service, organizing seminars and contributing to conferences such as the West Coast Optimization Meeting. His work emphasizes practical applications of theoretical results, with a focus on solving real-world optimization challenges.

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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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Michel Gendreauمشاهده پروفایل

استاد

University of MontrealCanada

Michel Gendreau is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal . His research focuses on the application of Operations Research to Transportation , Telecommunications , and Energy Systems , with an emphasis on Stochastic Optimization and Real-time Planning . He co-directs the Intelligent Transportation Systems Laboratory and is affiliated with the CIRRELT , IVADO , and Trottier Energy Institute . Education : Ph.D. in Computer Science (1984), Université de Montréal His work includes developing metaheuristics for complex optimization problems and dynamic transportation systems . Recent projects address smart supply chains and real-time logistics . He has supervised over 40 doctoral and master's students, including notable graduates like Sanchez-Martinez, Guillen Reyes, and Parada Pradenas. Dr. Gendreau has been recognized with prestigious fellowships from IFORS (2022) and INFORMS (2010). His academic contributions span 420 publications, with recent studies appearing in Reliability Engineering and System Safety and Networks , focusing on stochastic programming , multiperiod routing , and UAV network design . He collaborates extensively with industry partners and has secured grants from organizations like FRQNT and CIRRELT . His research integrates machine learning with operations research to solve real-world challenges in transportation , energy , and logistics .

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Samir Elhedhliمشاهده پروفایل

استاد

University of WaterlooCanada

Samir Elhedhli is a Professor in the Department of Management Sciences at the University of Waterloo, within the Faculty of Engineering. His research focuses on Large-scale Optimization, Logistics, Supply Chain Design, Healthcare Operations, Airline Scheduling, and Data Analytics. He has held grants from NSERC, CFI, OCE, and MITACS, collaborating with industries in aircraft manufacturing, airline scheduling, and warehouse management. Education: PhD in Management Science, McGill University (2001) Master's in Industrial Engineering, Bilkent University (1996) Bachelor's in Industrial Engineering, Bilkent University (1994) Research Interests: Data Analytics & Data Science Large-scale Optimization (Interior-point methods, decomposition, column generation) Supply-chain Analytics (Logistics, warehousing, routing, scheduling) Environmental Sustainability in Supply Chains Key Awards: CORS Service Award (2013) University of Waterloo Distinguished and Outstanding Performance Awards (2005–2019) Grants & Advising: Active grants from NSERC, CFI, OCE, and MITACS Currently accepting graduate student applications Administration & Service: Chair, Department of Management Sciences (2014–2018) President, Canadian Operational Research Society (2011–2012) Co-Editor-in-Chief, INFOR Journal (2014–present) Labs & Teams: Leads the WanOpt research group focused on optimization methodologies and applications.

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Zukui Liمشاهده پروفایل

استاد

University of AlbertaCanada

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

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Miguel F. Anjosمشاهده پروفایل

استاد

Polytechnic School of MontrealCanada

Miguel F. Anjos is Professor and Chair of Operational Research at the School of Mathematics, University of Edinburgh , and holds the NSERC-Hydro-Québec-Schneider Electric Industrial Research Chair on Optimization for Smart Grids at Polytechnique Montréal. He received his B.Sc. (1992), M.S. (1994), and Ph.D. (2001) from McGill, Stanford, and Waterloo respectively. Research Theme Head of Data and Decisions at Edinburgh Founding Director of Trottier Institute for Energy Editor-in-Chief of Optimization and Engineering Research Interests: His work bridges mathematical optimization with smart grid applications , focusing on conic optimization, optimal power flow, demand response, and facility layout. He applies these techniques to energy storage, electric transportation, and industrial systems. Scientific Awards: Méritas Teaching Award (2012) Humboldt Research Fellowship (2009) Queen Elizabeth II Diamond Jubilee Medal (2013) Elected Fellow of EUROPT and Canadian Academy of Engineering Academic Service: Served on Mathematical Optimization Society Council, SIAM Activity Group on Optimization, INFORMS Optimization Society Vice-Chair, and Mitacs Research Review Committee. Hosts benchmark datasets: QAPLIB, FLPLIB, Jones Benchmark.

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Amir Ardestani-Jaafariمشاهده پروفایل

دانشیار

University of British ColumbiaCanada

Amir Ardestani-Jaafari is an Associate Professor at the Faculty of Management , University of British Columbia Okanagan, with affiliations to the Institute for the Sustainability (IGS) . His office is located at EME 4111, and he can be reached at amir.ardestani@ubc.ca or 250.807.8108. Postdoctoral Fellow in Operations Management, McGill University Ph.D. in Management Science, HEC Montreal M.Sc. and B.Sc. in Industrial Engineering, Tehran Polytechnic Dr. Ardestani-Jaafari’s research focuses on Healthcare Operations , Supply Chain Management , and Business Analytics , with particular emphasis on robust optimization techniques for facility location, demand uncertainty, and network design. His work integrates mathematical modeling with real-world healthcare and logistics challenges. Recent publications demonstrate expertise in Two-stage Robust Optimization for equity-driven facility location, Text Mining in telemedicine appointment systems, and Decision-dependent Uncertainty in network interdiction. He has explored applications in Home Healthcare , Cancer Screening Networks , and Global Sourcing Compliance using advanced optimization frameworks. Dr. Ardestani-Jaafari is affiliated with key research centers including the UBC Optimization Center (COCANA) , Materials and Manufacturing Research Institute (MMRI) , and GERAD Research Center . While student advising information isn't explicitly stated, his technical contributions span algorithm development for inventory problems, network design methodologies, and computational solutions for complex operations management challenges.

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Christopher Thomas Ryanمشاهده پروفایل

دانشیار

University of British ColumbiaCanada

Christopher Thomas Ryan is an Associate Professor in the Operations and Logistics Division at the UBC Sauder School of Business. He holds a PhD and BA from UBC in Management Science and Mathematics, respectively. His research spans optimization theory, game theory, theoretical economics, video game design, and pedagogy for business education. He has held positions at the University of Chicago Booth School of Business and is affiliated with the Colibri Learning Foundation. Education: BA (Mathematics) and PhD (Management Science) from UBC; BA (Sociology and Economics) from University of Guelph. He has authored numerous textbooks and case studies, including works on research careers, operations management, and teaching methodologies. His work integrates human-centric approaches to operations and education systems. Research Interests include small business operations, education operations, theoretical economics, and the design of video games. He has published in top journals like Management Science, Econometrica, and Mathematical Programming. His grants include SSHRC and NSERC awards. Ryan advises students across multiple institutions and has mentored prominent faculty in marketing, operations, and MIS fields. Teaching focuses on core operations management for MBA programs and pedagogical innovation. He has developed cases on AI in education, early childhood development, and small business sustainability. Ryan’s work bridges academic research with practical applications in business and education sectors.

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Michel Gendreauمشاهده پروفایل

استاد

Polytechnic School of MontrealCanada

Michel Gendreau is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a B.Com. from McGill University, and both an M.Sc. and Ph.D. from the University of Montreal. His research focuses on operational research with applications in logistics, transportation, energy systems, and telecommunications. He is affiliated with several prestigious research centers including the Institute for Data Valorization (IVADO), the Trottier Energy Institute (IET), and the Interuniversity Research Center on Enterprise Networks, Logistics and Transport (CIRRELT). Professor Gendreau's research interests span operational research, with particular emphasis on stochastic optimization methods applied to transportation and logistics problems, energy systems management, and telecommunications. His work often addresses real-world challenges through mathematical modeling and algorithm development, with applications ranging from bike-sharing systems to emergency response planning and electricity grid management. The analysis of his recent publications reveals a strong focus on vehicle routing problems under uncertainty, maintenance optimization, and the integration of stochastic programming with machine learning techniques for improved decision making. Professor Gendreau has received numerous prestigious awards recognizing his contributions to the field of operations research. In 2022, he was named a Fellow of the International Federation of Operational Research Societies (IFORS). In 2010, he was awarded Fellow status by INFORMS (Institute for Operations Research and the Management Sciences). Most notably, in November 2015, he received the Robert M. Herman Lifetime Achievement Award from the Transportation Science and Logistics Society of INFORMS, which is considered the most prestigious distinction for operational researchers working in logistics and transportation. Throughout his career, Professor Gendreau has supervised 25 doctoral students and 18 master's students, contributing significantly to the development of the next generation of operations research experts. His research has been supported by numerous grants from organizations including NSERC (Natural Sciences and Engineering Research Council of Canada), with expertise recognized in Operational Research and Management Science (NSERC subject 1601) and Logistics (NSERC subject 1603). Professor Gendreau is actively involved in several research teams and laboratories, particularly those focused on data valorization, energy systems, and transportation logistics. His current work continues to push the boundaries of stochastic optimization and its applications to complex real-world problems, with recent publications addressing challenges in urban transportation, energy management, and emergency response systems.

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Tommaso Schettiniمشاهده پروفایل

استادیار

Concordia UniversityCanada

Dr. Tommaso Schettini is an Assistant Professor in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University. His research focuses on transportation systems optimization, including electric vehicle charger location, metro timetabling, and demand-driven scheduling strategies. He holds an ORCID identifier (0000-0003-2578-1539) and supervises Master's and PhD students in Industrial Engineering. His work integrates operations research techniques like Benders decomposition and metaheuristics to solve real-world transportation and manufacturing challenges. Research Areas: Electric Vehicle Infrastructure Planning, Metro Timetabling Strategies, Integer Programming, Discrete Simulation-based Optimization, and Combinatorial Optimization. Notable contributions include developing pattern-based algorithms for short-turning metro lines and optimizing micro-mobility integration with public transit. His articles span 2016–2024, emphasizing demand-driven approaches in transportation and manufacturing systems.

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Margarida CARVALHOمشاهده پروفایل

دانشیار

University of MontrealCanada

Margarida Carvalho is an Associate Professor in the Department of Computer Science and Operations Research at Université de Montréal. She holds the FRQ–IVADO Research Chair in Data Science for Combinatorial Game Theory and was awarded the 2018 EURO Doctoral Dissertation Award. Her research integrates algorithmic game theory and integer programming to address strategic decision-making challenges in healthcare, urban planning, and sustainability. Education: Bachelor's and Master's in Mathematics PhD in Computer Science (Universidade do Porto, 2016) Postdoctoral Fellow at Polytechnique Montréal (2017) Research Interests: Algorithmic Game Theory and Combinatorial Optimization Applications in Kidney Exchange Programs, EV Infrastructure, and Urban Decision-Making Development of fair and scalable optimization frameworks Awards & Grants: FRQ–IVADO Research Chair (2018–present) Teaching Excellence Award (2024) Lead on projects funded by CRSNG, MITACS, and Fonds de recherche du Québec Lab & Collaborations: Member of CIRRELT, CRM, Mila, and the AI Cybersecurity Lab (talents). Her team focuses on interdisciplinary solutions for complex socio-economic systems.

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Quentin Cappartمشاهده پروفایل

دانشیار

Polytechnic School of MontrealCanada

Quentin Cappart is an Associate Professor at Polytechnique Montréal since 2020, specializing in artificial intelligence , combinatorial optimization , and constraint programming . His work integrates machine learning and operations research to solve complex industrial problems, particularly in transportation , logistics , and healthcare . Affiliations: Co-founder and Head of the Combinatorial Optimization and Reasoning in Artificial Intelligence Laboratory (CORAIL) Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) IVADO researcher Affiliate member of MILA Researcher at IMC2 institute Research focus: Hybrid algorithms combining logical reasoning , machine learning , and operations research to enhance decision-making tools. His group emphasizes accessibility for non-experts and small companies. Scientific Awards: 2025: Early Career Engineer Award (Ordre des ingénieurs du Québec) 2025: Early Career Professor Award (Polytechnique Montréal) 2025: Prix Méritas (Polytechnique Montréal) 2024: Early Career Researcher Award (ACP) 2024: Best Paper Award (ACP Machine Learning track) 2023: Winning approach for Citylearn challenge (NeurIPS) Teaching: Courses include INF8175: Artificial intelligence - methods and algorithms and INF6102: Local search and metaheuristics . He also teaches at ACP Summer/Winter Schools.

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Chris Kapulkinمشاهده پروفایل

دانشیار

Western UniversityCanada

Chris Kapulkin is an Associate Professor in the Department of Mathematics at the University of Western Ontario. His research focuses on Homotopy Theory, Higher Category Theory, Combinatorics, and Formalization of Mathematics using Homotopy Type Theory, with applications to Cryptography. He is a member of the Canadian Journal editorial boards and serves on scientific advisory boards such as Banff International Research Station. He has organized numerous conferences and workshops, including the Mathematical Congress of the Americas (2025) and HoTTEST Summer School (2022). Kapulkin actively supervises PhD and Master's students, postdocs, and undergraduate researchers, contributing to over 20 academic publications since 2011. His awards include the Coxeter-James Prize (2025) and Florence Bucke Science Prize (2024). He leads the Western Cryptography Research Group and co-founded the HoTT Electronic Seminar Talks, fostering interdisciplinary collaboration in category theory and type theory. Research Interests: Kapulkin’s work bridges abstract mathematics and foundational computer science. He explores synthetic approaches to homotopy theory, categorical structures in discrete mathematics, and the formalization of mathematical proofs using type theory. His cryptographic research applies algebraic methods to secure integer comparisons and multilinear maps. Notable contributions include contributions to the Homotopy Type Theory Book (2013) and foundational papers on simplicial models of univalence (2021). Grants & Funding: Holder of the Distiguished Research Professorship (2022-23), recipient of C$20,000 interdisciplinary funding (2016-17) for cryptographic research, and a member of the Mathematical Sciences Research Institute (2022-23). His work has been supported through multiple conference organization grants and academic fellowships. Labs/Teams: Faculty mentor of the Western Cryptography Research Group and co-founder of the HoTTEST seminar series. Active in organizing the Directed Reading Program and Geometry/Topology Seminar at Western University.

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