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Martin SchmidtView profile

Professor

• Mathematical Optimization
• Energy Systems
• Mixed-Integer Nonlinear Optimization
• +3 more

Martin Schmidt is a Full Professor (W3) for Nonlinear Optimization at the Department of Mathematics, Trier University, since 2019. He has held leadership roles in research training groups and international committees, focusing on mathematical modeling and optimization of energy systems, gas networks, and market equilibria. His work bridges mixed-integer nonlinear optimization , bilevel optimization , and robust methods with applications to real-world energy challenges. Education: PhD in Mathematics (2013), Diplom in Mathematics (2008), both from Leibniz University Hannover. Editorial Roles: Editorial Board member of Journal of Optimization Theory and Applications and Optimization Letters , Associate Editor for OR Spectrum and EURO Journal on Computational Optimization . His research integrates complex physical systems (e.g., gas transmission networks) with game-theoretic models to analyze energy markets. Recent publications emphasize robust optimization , decomposition techniques , and machine learning integration in bilevel frameworks. Awards highlight his contributions to gas market feasibility , linear bilevel optimization , and practical applications in energy systems. Collaborations span institutions like Universidad Zaragoza, Sapienza University, and Forschungszentrum Jülich.

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.

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.

• 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.

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.

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.

Sajjad Fattaheian DehkordiView profile

Researcher

• Power Systems Engineering
• Smart Grids
• Renewable Energy Integration
• +3 more

Sajjad Fattaheian Dehkordi is a Postdoctoral Researcher in the Department of Electrical Engineering and Automation at Aalto University, Espoo, Finland. His research focuses on advanced energy management systems for modern power grids, with emphasis on distributed and transactive control approaches for resilient and efficient grid operations. His core research interests include: Power system resilience under high renewable penetration Microgrid and energy community optimization Distributed energy resource integration Electric vehicle-grid interaction and V2G systems Real-time congestion and ramping management Analysis of his 2022-2024 publications reveals a dominant trend toward multi-agent transactive frameworks addressing grid stability challenges. His work consistently targets voltage regulation, asymmetrical power flows, and ramping events in distribution systems, leveraging optimization techniques like MILP while incorporating flexibility concepts for renewable integration. Key innovations include distance-driven P2P/P2G transactions and incentive-based congestion management. No scientific awards are documented in the available records. Information regarding student advising, research grants, or leadership roles is not provided in current documentation.

• Mining Automation
• Autonomous Systems
• Robotics
• +5 more

Dr. Kevin Austin is a Research Fellow at the School of Mechanical and Mining Engineering, Faculty of Engineering, Architecture and Information Technology at the University of Queensland. His work is affiliated with the Future Autonomous Systems and Technologies research group, where he focuses on automation and robotics applications in mining engineering. Dr. Austin received his academic qualifications from the University of Queensland, including a Bachelor (Honours) of Engineering and a Doctor of Philosophy. Dr. Austin's research centers on mining automation and autonomous systems for heavy machinery operations. His work spans several key areas including dragline operation planning, excavation sequencing, terrain mapping for autonomous bulldozers, and hyperspectral imaging for ore grade discrimination. He has made significant contributions to the development of algorithms for mining equipment automation, particularly in the areas of Monte-Carlo Tree Search for dragline operation planning and Iterative Closest Point variants for terrain scan matching. His research bridges theoretical advances in robotics and artificial intelligence with practical applications in the mining industry, focusing on improving efficiency, safety, and productivity. Dr. Austin has been involved in numerous research projects funded by industry partners including Caterpillar Inc and the Australian Coal Association Research Program (ACARP). His current research focuses on coal stockpile management for remote bulldozers, semi-autonomous bulldozers for mine site rehabilitation, and articulated truck automated systems. His work demonstrates a strong industry connection with practical applications in mining operations. As an academic supervisor, Dr. Austin has served as an Associate Advisor for multiple PhD and Master's students at the University of Queensland. His supervision portfolio includes research on real-time terrain mapping for autonomous bulldozers, mission planning for autonomous excavation, dragline excavation sequencing, and scan matching for terrain mapping in open-pit mining. His collaborative approach is evident in his work with other faculty members, particularly Professor Ross McAree. Dr. Austin's laboratory and research team work closely with industry partners through the Future Autonomous Systems and Technologies group. They maintain strong connections with mining equipment manufacturers and coal mining operations, ensuring their research addresses real-world challenges in the mining sector. Their facilities likely include simulation environments for mining equipment operation, testbeds for autonomous systems, and data analysis platforms for mining process optimization.

Jessica OlivaresView profile

Assistant Professor

• Supply Chain Management
• Supply Chain Disruptions
• Industry 5.0
• +3 more

Jessica Olivares is an Assistant Professor of Supply Chain Management at the Shannon School of Business, Cape Breton University. Her expertise spans supply chain resilience, digital twins, and Industry 5.0, with a focus on mitigating disruptions in global networks. Dr. Olivares contributes to both academic research and practical solutions for sustainable supply chain management. Her academic credentials include: B.S. in Industrial Engineering, University of the Americas Puebla (UDLAP), Mexico M.S. in Industrial Engineering, University of the Americas Puebla (UDLAP), Mexico Ph.D. in Industrial and Manufacturing Systems Engineering, University of Windsor, Canada Dr. Olivares' research centers on supply chain management, with specific interests in disruption recovery, digital twin applications, and sustainable design. She explores how Industry 5.0 principles can humanize smart manufacturing while enhancing resilience. Her work addresses critical gaps in perishable food supply chains and resource distribution during crises, integrating risk assessment with technological innovation to build robust systems. Her recent publications (2021-2025) show a strong emphasis on digital twins for supply chain resilience, with increasing attention to sustainability and multi-objective optimization. She has pioneered frameworks for recovery from major disruptions, including pandemic impacts, and investigates energy-aware scheduling in manufacturing. Her scholarship bridges theoretical models with real-world applications in food systems and global networks. No scientific awards were documented in the available sources. Information on graduate student supervision and research funding was not provided, though her active publication record suggests engagement in scholarly mentorship and potential grant-supported projects. No details about laboratories or research teams were mentioned.

Armin NurkanovićView profile

Professor

• Optimal Control
• Numerical Methods
• Hybrid Dynamical Systems
• +4 more

Dr. Armin Nurkanović is an interim professor at the Technical University of Braunschweig's Department of Mathematical Optimization, where he teaches courses on dynamic optimization and numerical methods. Previously, he completed his PhD at the University of Freiburg under Prof. Moritz Diehl, focusing on optimal control of nonsmooth dynamical systems. His research emphasizes numerical methods for hybrid systems, real-time optimization, and applications in robotics and renewable energy systems. He has received the IEEE Control Systems Letters Outstanding Paper Award (2022) and was a finalist for the 2024 European Systems & Control PhD Thesis Award. Education: Bachelor's in Electrical Engineering (University of Tuzla, 2015) Master's in Electrical Engineering and Information Technology (Technical University of Munich, 2018) PhD in Control (University of Freiburg, 2023) Research Interests: Optimal control of hybrid and nonsmooth systems (e.g., Filippov systems, switched systems) Real-time optimization for model predictive control (MPC) Robust control theory and stochastic optimization Applications in robotics and renewable energy systems Teaching & Software: Developed open-source tools nosnoc and nosnoc_py for optimal control Teaching courses on numerical optimization and optimal control at TU Braunschweig Collaborations & Students: Open to academic and industry collaborations Supervises Bachelor's/Master's theses in mathematics, engineering, and computer science

Sophie HuibertsView profile

Researcher

• Linear Programming
• Mixed Integer Programming
• Algorithm Analysis
• +4 more

Sophie Huiberts is a CNRS researcher at LIMOS, Clermont Auvergne University in Clermont-Ferrand since fall 2023. Previously, she was a Simons Junior Fellow at Columbia University in New York City, hosted by Tim Roughgarden. She completed her PhD research at Centrum Wiskunde & Informatica in Amsterdam under Daniel Dadush and received her doctorate in 2022 from Utrecht University. Dr. Huiberts specializes in theoretical aspects of mathematical optimization, particularly focusing on the gap between practical performance and theoretical predictions of linear programming algorithms. Her research examines software implementations like Gurobi, CPLEX, SCIP, and HiGHS to understand why these algorithms perform better in practice than worst-case analysis would suggest. She has made significant contributions to smoothed analysis of the simplex method, establishing both upper and lower bounds on its complexity under perturbations of worst-case inputs. Analysis of her publication record shows consistent focus on bridging theoretical computer science with practical optimization methods. Her work spans linear programming theory, integer programming, combinatorial optimization, and computational geometry, with particular emphasis on understanding the geometric properties of optimization problems and the behavior of algorithms on real-world instances. Simons Junior Fellowship Dr. Huiberts maintains active engagement with the research community through social media platforms including Mastodon and Bluesky, and produces high-quality recordings of her research talks available on YouTube. She has made a conscious decision to stop air travel since 2023 due to climate concerns, demonstrating commitment to sustainable research practices while maintaining scientific connections through digital means. She is affiliated with LIMOS (Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes), a research laboratory at Clermont Auvergne University focused on computer science, modeling, and optimization systems, where she continues her investigations into the theoretical foundations of practical optimization algorithms.

Michael MerlinView profile

Senior Lecturer

• Power Electronics
• Modular Multilevel Converters
• Energy Storage Systems
• +5 more

Dr. Michael Merlin is a Senior Lecturer in the School of Engineering at the University of Edinburgh, specializing in power electronics and grid technologies. His research focuses on advanced converter topologies, energy storage integration, and smart grid solutions to enhance grid flexibility and resilience. He leads projects such as the Grid Forming Hybrid Transformer (EPSRC-funded) and Wind2DC (EPSRC and industry collaboration), addressing challenges in offshore wind energy and hybrid AC/DC systems. His work emphasizes modular multilevel converters (MMCs), DC microgrid integration, and high-voltage direct current (HVDC) grids. Recent contributions include innovative approaches to energy balancing in hybrid networks, fault-tolerant converter designs, and SiC MOSFET-enabled high-frequency inverters. Dr. Merlin’s research bridges theoretical advancements with practical applications, supported by experimental validation and industry partnerships. Key projects include: Grid Forming Hybrid Transformer (GIFhT-4-eGrids) : EPSRC-funded initiative to develop next-gen transformers for future grids (2023–2026). Wind2DC : Engineering novel HVDC power take-off systems for floating offshore wind turbines (2023–2026). ELEGANT : Innovate UK-funded project advancing gallium nitride (GaN) technologies in power electronics (2023–2024). His publications highlight trends in MMC-based converters, energy storage optimization, and fault management in low-voltage and HVDC systems. Collaborative efforts with industry and academia ensure his work addresses real-world grid challenges, from offshore wind integration to decentralized energy distribution.

• Supply Chain Optimization
• Supply Chain Analytics
• Freight Transportation
• +5 more

Michael Hewitt is a Professor of Supply Chain Management at the Quinlan School of Business, Loyola University Chicago, where he holds the Ralph Marotta Chair in Free Enterprise. He serves as Executive Director of the Quinlan Business Leadership Hub and Director of the Supply Chain and Sustainability Center, playing key leadership roles in academic and research initiatives. His research focuses on Supply Chain Optimization , Freight Transportation , and Network Design , leveraging advanced mathematical modeling and operations research techniques. His work bridges theoretical innovation with practical applications in logistics and transportation industries. The 15 most recent publications (limited here to 5) reflect a strong trend in continuous-time network modeling , dynamic discretization methods , and large-scale optimization for freight and service network design. His research spans disciplines including Operations Research, Transportation Science, and Supply Chain Analytics, with recurring subfields such as Benders decomposition, time-expanded networks, and resource-constrained pathfinding. Scientific Awards: CSoNet Best Paper Award 2023 INFORMS TSL Freight Transportation & Logistics SIG Best Paper Award 2021 Glover-Klingman Prize 2019 INFORMS TSL Best Paper Award 2018 Loyola Faculty Researcher of the Year 2015 Dr. Hewitt has secured research funding from the National Science Foundation , Material Handling Institute , and New York State Health Foundation . He actively mentors students through research collaboration and has advised projects with real-world impact at companies like Bayer Crop Science , Exxon Mobil , and Schneider . He serves on editorial boards and holds leadership roles in INFORMS , including past presidency of the Transportation Science and Logistics Society. He leads two major centers: the Supply Chain and Sustainability Center and the Quinlan Business Leadership Hub , fostering interdisciplinary research, industry partnerships, and student development in supply chain and leadership education.