Find Academic Supervisors

• Convexification
• Quadratic Programming
• Mixed-Integer Programming
• +3 more

Boshi Yang is an **Associate Professor** in the Department of Mathematical and Statistical Sciences at Clemson University. His research focuses on convexification, quadratic programming, mixed-integer programming, and conic programming applications. He holds a PhD from the University of Iowa (2015) and a BS from Zhejiang University (2010). Education: PhD, Applied Mathematical and Computational Sciences, University of Iowa, 2015 BS, Mathematics and Applied Mathematics, Zhejiang University, 2010 His research interests emphasize convexification techniques , quadratically constrained quadratic programming , and conic programming applications . Recent work explores data-driven optimization for energy systems and algorithmic improvements for combinatorial problems. His publications span journals like Mathematical Programming, Operations Research Letters, and IEEE Transactions on Power Systems. Key Awards: 2020 Faculty Teaching Award (Clemson School of Mathematical and Statistical Sciences) 2019 Outstanding Service to Graduate Students (Clemson School of Mathematical and Statistical Sciences) Teaching: Teaches courses including Linear Programming (MATH 4400/6400), Nonlinear Programming (MATH 8110), and Special Topics in Conic Programming (MATH 9880).

Kevin TomsovicView profile

Professor

• Power Systems Engineering
• Renewable Energy Integration
• Smart Grid Technologies
• +6 more

Kevin Tomsovic is the Chancellor's Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, and Director of the CURENT Engineering Research Center. His expertise spans smart grids, power systems optimization, and cyber-physical resilience. He holds a PhD from the University of Washington (1987) and prior roles at Washington State University. Research focuses on renewable integration, microgrid scheduling, and grid security, with notable contributions to IEEE standards. Awards include IEEE Fellow status. Education: PhD/M.S. Electrical Engineering, University of Washington (1987/1984); B.S. Electrical Engineering, Michigan Tech (1982). Research interests include: - Intelligent systems for power distribution design - Electricity market analysis and stability control - Cybersecurity for grid infrastructure - Resilience quantification in energy systems Publications highlight advancements in grid emulation platforms, adversarial ML defenses, and microgrid optimization. His work is supported by NSF/DOE grants (e.g., NSF EEC-1041877). CURENT testbed facilities enable real-time grid control validation. Awards: IEEE Fellow (20XX). Grants & Collaborations: Lead CURENT NSF ERC; industry partnerships include power utilities and tech firms. Active in developing standards for grid resilience metrics. Labs/Teams: CURENT Engineering Research Center, UT Power Systems Lab.

Andrea WaltherView profile

Professor

• Nonlinear Optimization
• Algorithmic Differentiation
• Mathematical Optimization
• +3 more

Andrea Walther is a Professor of Mathematical Optimization at the Humboldt University of Berlin, holding a position within the Faculty of Mathematics and Natural Sciences. She leads the Mathematical Optimization research group at the Institute of Mathematics, focusing on algorithmic differentiation, nonlinear optimization, and applied mathematics. Her academic journey includes a Diploma in Business Mathematics (1996, University of Bayreuth), a PhD (1999, TU Dresden), and habilitation (2008, TU Dresden). She has held roles such as Junior Professor at TU Dresden (2007–2008) and Professor at the University of Paderborn (2009–2019) before joining Humboldt in 2019 as a MATH+ Professor. Education : 1991–1996: Studies in Business Mathematics, University of Bayreuth 1996: Diploma in Business Mathematics, University of Bayreuth 1999: PhD in Mathematics, TU Dresden 2008: Habilitation, TU Dresden Her research interests center on optimization methods, particularly algorithmic differentiation (e.g., ADOL-C software), nonsmooth optimization, and applications in engineering and machine learning. She leads initiatives like the Cluster of Excellence MATH+ and contributes to projects such as the Transregio 154. Key Projects: Co-PI of DFG Project 'Mixed-integer non-smooth optimization for gas market problems' (2020–2022) Principal Investigator in MATH+ Projects (EF3-7, AA2-7) Co-developer of ADOL-C, a widely used tool for algorithmic differentiation Notable awards include being a SIAM Fellow. Her work bridges theoretical advancements and practical applications, with contributions to energy sector optimization, inverse problems, and computational frameworks for solving complex systems.

Matthias BeckerView profile

Professor

• Human-Computer Interaction
• Simulation and Modeling
• Distributed Systems
• +6 more

Matthias Becker is a Professor at the Institute for Practical Computer Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover, where he has been a core member of the Human-Computer Interaction group since 2019. He serves as Internship Coordinator for Computer Science and Computer Engineering and holds key roles in the Computer Science Examination Board and Selection Committee, actively shaping academic governance and student development. His academic journey began with PhD studies at the University of Bremen (1996-2000) supported by a DFG grant, followed by a postdoctoral permanent position at Leibniz University Hannover (2000-2019), an Associated Assistant Professor role at École des Mines de Nantes (2000), and a Habilitation in Computer Science in 2013. This foundation enabled his transition to a full professorship in 2019. Becker's research spans Human-Computer Interaction, Simulation and Modeling, and Bio-inspired Computing, with applications in agriculture, renewable energy, and manufacturing. His work integrates distributed systems, optimization algorithms, and wireless sensor networks to solve complex real-world problems, such as greenhouse monitoring, wind farm logistics, and tire noise reduction. Recent publications reveal a strategic focus on practical validation of simulation models and cross-domain applications of nature-inspired algorithms. His 15 most recent publications (2018-2024) demonstrate consistent innovation in applying simulation techniques to offshore wind farm installation, agricultural pest management, and sports science. These works emphasize real-world validation, collaborative problem-solving, and the development of domain-specific optimization frameworks that bridge theoretical algorithms and industrial implementation. As Internship Coordinator, Becker facilitates critical industry-academia connections for students, while his examination board responsibilities ensure rigorous academic standards. His leadership in the Human-Computer Interaction group drives research on interactive systems for agriculture, energy, and health, with particular emphasis on user-centered design in complex operational environments like wind farm logistics and greenhouse automation.

Ayan MukhopadhyayView profile

Researcher

• Multi-Agent Systems
• Cyber-Physical Systems
• Smart Cities
• +5 more

Dr. Ayan Mukhopadhyay serves as a Senior Research Scientist in the Department of Electrical Engineering and Computer Science at Vanderbilt University's School of Engineering. Previously, he was a Post-Doctoral Research Fellow at Stanford Intelligent Systems Lab where he received the 2019 CARS post-doctoral fellowship. His academic journey includes a Ph.D. from Vanderbilt University's Computational Economics Research Lab with a doctoral thesis nominated for the Victor Lesser Distinguished Dissertation Award 2020. His research spans critical domains in smart infrastructure systems with particular focus on: Developing robust decision-making frameworks for cyber-physical systems under uncertainty Creating multi-agent solutions for emergency response optimization Designing machine learning approaches for urban mobility and energy management Building proactive incident detection pipelines using heterogeneous data sources Analysis of his recent publications reveals strong thematic continuity in applying artificial intelligence to real-world infrastructure challenges, particularly in transportation systems, emergency response, and energy management. His work consistently bridges theoretical AI advances with practical implementation in smart city contexts, demonstrating expertise in both algorithmic innovation and systems integration. Award highlights include: CARS Post-Doctoral Fellowship (2019) Best Paper Award at ICLR's AI for Social Good Workshop Victor Lesser Distinguished Dissertation Award Nomination (2020) Dr. Mukhopadhyay leads significant research initiatives through ScopeLab, focusing on creating deployable solutions for public transit, emergency response, and energy systems. His work on vehicle-to-building charging, traffic incident localization, and equitable transit network design demonstrates commitment to solving high-impact urban challenges through rigorous computational methods. Current projects involve developing simulation environments for non-stationary environments (NS-Gym) and explainable planning frameworks integrating formal logic with large language models.

• Mixed-Integer Optimization
• Combinatorial Optimization
• Discrete Mathematics
• +3 more

Timm Oertel is a Professor in the Department of Data Science at Friedrich Alexander University Erlangen-Nuremberg (FAU), holding the Chair of Analytics & Mixed-Integer Optimization. His office is located in Room 03.344 at Cauerstraße 11, Erlangen, and he can be contacted via email at timm.oertel@fau.de or phone at +49 9131 85-67313. His research focuses on mixed-integer optimization, combinatorial optimization, and discrete mathematics, with significant contributions to sparse solutions in lattices and semigroups, integer Carathéodory rank, knapsack polyhedra, and parametric integer optimization. His work bridges theoretical computer science, operations research, and discrete geometry, emphasizing structural properties of integer solutions and algorithmic efficiency. Professor Oertel's publication record (2013-2025) reveals consistent trends in theoretical integer programming, with recent work (2020-2025) concentrating on sparsity patterns, approximation in algebraic structures, and complexity bounds. He frequently collaborates with leading researchers including Iskander Aliev, Robert Weismantel, and Joseph Paat, publishing in top venues like Mathematical Programming and SIAM Journal on Optimization. His research demonstrates deep connections between combinatorial geometry and optimization theory.

• Optimization methods
• Metaheuristics
• Heuristics
• +12 more

Mina Mirhosseini is a Research Fellow at the Faculty of Logistics, Molde University College, Norway. She holds a PhD in Computer Science from Shahid Beheshti University of Tehran, Iran, and has prior academic experience as a faculty member and lecturer in Iran and as a remote teaching assistant at the University of Hertfordshire, UK. Her primary research interests include Optimization Methods, Metaheuristics, Heuristics, Linear Integer Programming, Parallel Processing, Machine Learning, Artificial Intelligence, and Logistics. She has made significant contributions to solving complex computational problems such as the n-similarity problem and Mixed Integer Linear Programming (MILP) models using hybrid and parallel algorithms, particularly in the context of high-level synthesis and wireless sensor networks. The analysis of her recent publications reveals a strong focus on developing and applying advanced optimization techniques, especially quantum-inspired gravitational search algorithms and parallel genetic algorithms, to real-world engineering and computational challenges. Her work consistently emphasizes performance improvement, scalability, and load balancing in distributed and heterogeneous computing environments. Mina Mirhosseini has an extensive publication record in high-impact journals such as IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing, Journal of Supercomputing, and Computers and Electrical Engineering. Her research has been published across a range of venues, reflecting interdisciplinary work at the intersection of computer science, electrical engineering, and applied optimization. She has actively contributed to the academic community through roles such as program committee member and executive committee member for conferences on fuzzy systems, swarm intelligence, and evolutionary computation. Her academic journey includes teaching and research roles in Iran, demonstrating a sustained commitment to higher education and scientific inquiry. Mina Mirhosseini is part of the research group focused on Planning, Optimization and Decision Support at Molde University College. Her current work continues to advance the state-of-the-art in parallel and metaheuristic optimization methods, with applications in logistics, synthesis, and sensor network design.

Mohammad MonfaredView profile

Senior Lecturer

• Power electronics
• Renewable energy systems
• Power quality
• +4 more

Dr. Mohammad Monfared is a Senior Lecturer in the Department of Electronic and Electrical Engineering at Swansea University's School of Aerospace, Civil, Electrical and Mechanical Engineering within the Faculty of Science and Engineering. He joined Swansea University in 2022 after previously working at Ferdowsi University of Mashhad in Iran, where he received the Best Researcher Award in 2015. His academic journey began with a PhD in electrical engineering from Amirkabir University of Technology (Tehran Polytechnic) in 2010, which he completed with honors. Dr. Monfared's research focuses on the control and utilization of renewable energy generation systems and power electronics in the context of distributed generation, microgrids, and smart energy systems. His work is particularly relevant to Sustainable Development Goals 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure), 11 (Sustainable Cities and Communities), and 13 (Climate Action). He collaborates extensively with the Energy and Power Research Group (EPRG) at Swansea University, particularly with Dr. Meghdad Fazeli and Professor Mike Jennings, who holds a Royal Academy of Engineering Research Chair in 'Advanced Semiconductors for Electric Vehicles'. His research has practical applications and includes experimental verification in nearly all of his published works. Dr. Monfared's recent publications demonstrate a strong focus on power converter topologies, control strategies for renewable energy integration, and smart grid technologies. His work spans from high-boost impedance networks and DC-DC converters to grid-forming inverters and energy management systems. A notable trend in his research is the emphasis on practical implementation with experimental validation, bridging the gap between theoretical concepts and real-world applications in power electronics and renewable energy systems. Best Researcher Award from Ferdowsi University of Mashhad (2015) Department's Best Teacher Award at Swansea University (2023-24) Outstanding Reviewer Award for IEEE Transactions on Power Electronics (2023) IEEE Senior Member (2015) Dr. Monfared actively supervises PhD and MSc students, with current research projects focusing on gate-oxide reliability of wide bandgap power MOSFETs, single-phase PV-battery grid-forming inverters, and day-ahead PV generation forecasting. He collaborates with industry partners including National Grid, ABB, Siemens, and Vishay Intertechnologies on high-impact research projects. His future research direction involves establishing stronger industry collaborations to bridge the gap between academic innovation and real-world application, with a focus on advancing renewable energy technologies and power electronics for distributed generation and smart energy systems. Dr. Monfared also serves as an Associate Editor for both IEEE Transactions on Industrial Electronics and IEEE Transactions on Power Electronics, contributing to the scholarly community in his field.

• Artificial Intelligence
• Smart and Service Industry
• Mobility and Transport
• +5 more

Marco Schutten is an Associate Professor at the Digital Society Institute of the University of Twente, affiliated with the Industrial Engineering & Business Information Systems department. His work bridges Artificial Intelligence , Transportation , and Operations Research , focusing on optimizing complex systems. Expert in Urban Logistics and Freight Transport Specializes in Mathematical Programming and Optimization Research interests include Vehicle Routing , Machine Scheduling , and Agent-Based Simulation . Key trends in his 15 most recent articles (2015–2025) involve: Dynamic scheduling under time constraints Urban logistics and smart city applications Heuristics for combinatorial optimization Integration of MILP and Simulation models No scientific awards, formal supervisory roles, or part-time appointments are explicitly mentioned.

Ted RalphsView profile

Professor

• Mathematical Optimization
• Operations Research
• Integer Programming
• +7 more

Ted Ralphs is a Professor of Industrial and Systems Engineering at Lehigh University’s Rossin College of Engineering. He serves as co-founder and director of the Computational Optimization Research at Lehigh (COR@L) Laboratory, and chairs the INFORMS Computing Society. His research focuses on large-scale computation and optimization, bridging theoretical and practical applications through high-performance computing and mathematical techniques. Ralphs holds a Ph.D. in Operations Research from Cornell University and advanced degrees in Mathematics and Applied Mathematics from Carnegie Mellon University. His expertise spans Supply Chain Management, Grid Computing, Mathematical Optimization, Financial Engineering, and Algorithm Development. He teaches courses in computational methods, discrete optimization, financial optimization, and algorithms in systems engineering. Ralphs has received notable honors including the 2021 Rossin College Outstanding Doctoral Student Advising Award and election as an INFORMS Fellow in 2023. His research contributions include advances in bilevel optimization, decomposition methods, and open-source optimization software (e.g., COIN-OR’s Cbc solver). He has led collaborative projects such as a Naval grant-funded initiative with the University of Pittsburgh on bilevel optimization. Ralphs’ work emphasizes scalable algorithms and their real-world applicability in energy markets, logistics, and combinatorial problems.

Norbert TrautmannView profile

Professor

• Combinatorial Optimization
• Project Management
• Production Planning
• +3 more

Prof. Dr. Norbert Trautmann is a Full Professor of Quantitative Methods in Business Administration at the University of Bern, leading the Group for Business Analytics, Operations Research, and Quantitative Methods. He holds a PhD from the University of Karlsruhe (2000) and a Habilitation degree in Operations Research from the University of Karlsruhe (2004). His research focuses on Combinatorial Optimization , Project Management , Production Planning , and Portfolio Optimization . Prof. Trautmann has extensive professional experience, including roles as Head of the Department of Business Administration (2010–2012), and leadership in academic societies like the Swiss Operations Research Society (President since 2017). He has authored over 100 peer-reviewed publications and supervised numerous PhD students, including Philipp Baumann, Adrian Zimmermann, and Tom Rihm. His research has led to awards such as the 2010 IEEE Honourable Mention Award and the 2001 German Society of Operations Research Doctoral Thesis Award. Key projects include scheduling optimization for assessment centers, production planning in process industries, and portfolio tracking models for small investors.

Stefan WeltgeView profile

Professor

• Combinatorial Optimization
• Linear Programming
• Integer Programming
• +3 more

Stefan Weltge is a Professor of Discrete Mathematics at the Technical University of Munich (TUM). His research focuses on combinatorial optimization, linear and integer programming, and polyhedral combinatorics. He has received multiple teaching awards at TUM, including the Best Lecturer in Electrical and Computer Engineering (2019) and Best Advanced Course awards in Mathematics (2020/21, 2019). He also earned the Best Dissertation Award from the University of Magdeburg (2016). Education: PhD in Mathematics (University of Magdeburg), Postdoc (ETH Zurich) Research Grants: Funded by the German Research Foundation (DFG) via an Individual Grant (NextGen) and the PhD Program AdONE Professional Roles: Program Committee member for IPCO 2023, MIP 2022, ISCO 2022, ISCO 2020, ISCO 2018; Organizer of OR 2024, MIP 2022, and Cargese Workshops on Combinatorial Optimization (2024, 2022)

Joseph PaatView profile

Associate Professor

• Discrete Optimization
• Integer Programming
• Combinatorics
• +5 more

Joseph Paat is an Associate Professor at the Sauder School of Business, University of British Columbia, with a strong focus on discrete optimization and integer programming. He is an associate member of the Computer Science department and affiliated with the Institute of Applied Mathematics. BS, Denison University MA, Wake Forest University PhD, Johns Hopkins University His research interests lie at the intersection of discrete optimization, integer programming, and theoretical mathematics, particularly exploring applications of topology, combinatorics, and number theory to optimization problems. He also integrates machine learning techniques into discrete optimization in his teaching and research. Recent publications highlight advancements in proximity bounds, flatness theorems, and algorithmic efficiency for integer programs, alongside theoretical contributions to unimodular hypergraphs, quadratic-free sets, and block-structured optimization. His work often involves collaborations across disciplines and institutions, including ETH Zürich and Deutsche Bahn. Associate Editor, Discrete Optimization (2022–present) Secretary (2022–2025) and Chair (2025–2026) of the Mixed Integer Programming Society Chair of the Organizing Committee, Mixed Integer Programming Workshop (2024) At UBC, Paat teaches courses such as Logistics and Operations Management , Advanced Topics in Optimization , and Discrete Optimization II , emphasizing modeling practices, integer programming, and machine learning integration. He previously taught at ETH Zürich and Johns Hopkins University. Joseph Paat actively participates in research collaborations, supervises graduate students, and contributes to professional service through editorial and organizational roles in optimization societies.

Christopher HojnyView profile

Assistant Professor

• Mixed-Integer Programming
• Symmetry Handling in Optimization
• Combinatorial Optimization
• +3 more

Christopher Hojny is an Assistant Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology , specializing in combinatorial optimization. He contributes to the EAISI Foundational group and co-develops the academic solver SCIP . His research focuses on symmetry handling in mixed-integer programming , theoretical properties of integer programs, and algorithm development for combinatorial optimization. Recent work explores applications in graph neural network verification , clustering problems, and network coding through mixed-integer programming frameworks. Key publication trends show expertise in Symmetry detection and mitigation techniques Relaxation complexity theory Applications to machine learning robustness Decision diagram-based scheduling Scientific contributions include Proof systems for symmetry certification Topological bounds tightening in GNNs Stable set problem symmetry handling SCIP solver extensions He supervises PhD students Cédric Roy (NWO project on Local Symmetries) and Sten Wessel (co-supervised with Frits Spieksma), while Jasper van Doornmalen (2019-2023) investigated symmetry propagation algorithms.

Romain RaveauxView profile

Associate Professor

• Image Analysis
• Machine Learning
• Structural Pattern Recognition
• +5 more

Romain Raveaux is an Associate Professor at the LIFAT Computer Science Laboratory, University of Tours, affiliated with Polytech Tours. His research focuses on Image Analysis, Machine Learning, Structural Pattern Recognition, Graph Matching, Graph Neural Networks, Discrete Optimization, Reinforcement Learning, and Transfer Learning . Email: romain.raveaux@gmail.com , romain.raveaux@laposte.net Address: 64 av. Jean Portalis, Tours, France, 37200 Phone: +33 (0)2 47 36 14 27 Research Interests Graph Matching and Neural Networks Discrete Optimization for Pattern Recognition Transfer Learning in Graph-Based Models Historical Document Analysis Scientific Trends His recent work bridges Graph Neural Networks with Mixed-Integer Programming , focusing on Image Semantic Segmentation and Graph Cycle Detection . Earlier studies emphasize Genetic Algorithms for graph classification and Graph Edit Distance optimization in pattern recognition.