جست‌وجوی استاد و پژوهشگر

Manish Bansalمشاهده پروفایل

دانشیار

• Operations Research
• Game Theory
• Machine Learning
• +۸ مورد دیگر

Manish Bansal is an Associate Professor in the Department of Industrial and Systems Engineering at Virginia Tech , holding the title of Grado Faculty Fellow . He is affiliated with multiple centers, including the Sanghani Center for Artificial Intelligence and Data Analytics (Core Faculty), the National Security Institute (Affiliate Faculty), and serves as President of the Engineering Faculty Organization (2023–2024) . Education: Ph.D. in Industrial & Systems Engineering, Texas A&M University M.S. in Industrial & Systems Engineering, Texas A&M University B.Tech. in Electrical Engineering, National Institute of Technology (India) Research Interests : Focuses on Operations Research , Game Theory , Machine Learning , and Data-Driven Optimization . Methodologies include Stochastic/Distributionally Robust Integer Programming , Graph Neural Networks , and Location Science . Applications span Supply Chain Management , Network Design , and Aerospace Structures . Recent Grant Activities : NSF Grant for Planar Facility Location Problems with Uncertainty (PI, 2018–2023) Automotive Research Center Grant for Autonomous Telerobotics (PI, 2020–2023) NSF Grant for Two-Stage Assembly Lot Streaming (Co-PI, 2021–2024) Labs & Teams : Engaged with interdisciplinary research groups at the Sanghani Center and National Security Institute , contributing to AI-driven solutions for complex systems and national security challenges.

• Continuous Optimization
• Robust Optimization
• Mixed-integer nonlinear optimization
• +۷ مورد دیگر

Prof. Dr. Oliver Stein is a faculty member at the Karlsruhe Institute of Technology within the School of Business , specifically the Department of Operations Research . His research focuses on Continuous Optimization , Non-smooth Optimization , and Multiobjective Optimization , with applications in Operations Research , Game Theory , and Engineering Design . Stein has contributed extensively to semi-infinite programming , bilevel optimization , and mixed-integer nonlinear optimization . His work includes theoretical advancements in constraint qualifications , projected gradient flows , and epigraph reformulations , alongside practical applications in gemstone cutting and modular system design . His 15 most recent publications span topics such as non-convex Nash equilibrium problems , granularity in polynomial optimization , and branch-and-bound algorithms , reflecting a blend of theoretical rigor and real-world impact. Stein has received prestigious awards including the Heisenberg fellowship (2005-2006) and Feodor Lynen fellowship (1999-2000). He serves on editorial boards of journals like the Journal of Global Optimization and Optimization .

• Chemical Engineering
• Energy Systems
• Global Optimization
• +۳ مورد دیگر

Prof. Alexander Mitsos is a Universitätsprofessor for Process Systems Engineering at RWTH Aachen University's Faculty of Mechanical Engineering since 2012. He leads the Systemverfahrenstechnik group and directs the Institute for Energy and Climate Research (IEK-10) at Forschungszentrum Jülich. His research focuses on deterministic global optimization, energy systems, and sustainable chemical processes. Mitsos' academic lineage traces back to Prof. Roger W. H. Sargent via his PhD advisor Paul I. Barton. Education: 1994–1999: Diploma in Chemical Engineering from Universität Karlsruhe (KIT), specializing in Thermodynamics and Mechanics 2001–2006: PhD in Process Systems Engineering at MIT, advised by Paul I. Barton Research Interests: His work integrates advanced mathematical methods with engineering challenges. Key areas include: Optimal design and operation of chemical processes and energy systems Development of global optimization algorithms for nonlinear problems Integration of machine learning in process design and control Energy transition technologies, including power-to-X processes and CO2 utilization Publications: Over 200 articles across 2025–2024 highlight innovations in optimization, electrochemical systems, and sustainable fuels. Recent focus areas include reinforcement learning for control, graph neural networks for molecular design, and energy market scenario modeling. Labs & Teams: Leads the Process Systems Engineering Lab at RWTH and directs IEK-10’s energy systems modeling initiatives. Collaborates internationally on projects like COMANDO (energy systems optimization framework).

Jan-Joachim Rückmannمشاهده پروفایل

استاد

• Non-linear Programming
• Mathematical Optimization
• Semi-infinite Programming
• +۱۲ مورد دیگر

Jan-Joachim Rückmann is a Professor in the Department of Informatics at the University of Bergen, Norway. His research lies at the intersection of mathematical optimization, nonlinear programming, and their applications in finance and engineering. He is actively engaged in theoretical and applied research, with a focus on generalized semi-infinite programming and complementarity constraints. His research interests include non-linear programming, parametric optimization, robust optimization, data envelopment analysis, and the application of topological and differential geometric methods to optimization problems. He has made significant contributions to the theory of stability and stationarity in mathematical programs with complementarity constraints (MPCC) and semi-infinite programming. The recent publications highlight a strong trend in theoretical optimization, particularly in MPCC, stability of stationary points, and generalized semi-infinite programming. Several works explore duality, constraint qualifications, and convexification techniques. There is also interdisciplinary work applying optimization to mathematical finance, gas explosion modeling, and computational social choice using machine learning. While no scientific awards are listed, his extensive publication record in top journals such as Mathematical Programming , SIAM Journal on Optimization , and Optimization underscores his scholarly impact. He has served as guest editor for multiple special issues on parametric optimization, reflecting his leadership in the field. Though no students or grants are explicitly mentioned, his long-standing collaboration with researchers such as H. Günzel, D. Hernandez Escobar, F. Guerra Vazquez, and O. Stein indicates active research advising and team involvement. His work with the European Space Agency on trajectory analysis and space applications also points to applied research collaborations. Dr. Rückmann is affiliated with the Department of Informatics, contributing to both theoretical advances and practical applications of optimization methods. His work integrates deep mathematical analysis with real-world problem solving in engineering and economics.

Mirjam Dürمشاهده پروفایل

استاد

• Mathematical Optimization
• Copositive Programming
• Quadratic Optimization
• +۷ مورد دیگر

Mirjam Dür is a Full Professor (W3) at the Department of Discrete Mathematics, Optimization and Operations Research within the Faculty of Mathematics, Natural Sciences and Technology at the University of Augsburg (since 2017). Previously, she held Full Professor positions at the University of Trier (2011-2017) and other academic roles in Groningen, Darmstadt, and Vienna. Her research focuses on mathematical optimization, particularly copositive programming, quadratic optimization, matrix theory, and conic optimization. Born in Vienna, Austria M.Sc. in Mathematics (1996) and PhD in Applied Mathematics (1999) from University of Trier Positive Habilitation evaluation at TU Darmstadt (2005) Research Interests : Global optimization, quadratic and combinatorial optimization, conic optimization and matrix theory, copositive programming, and applications to graph theory and discrete problems. She has pioneered methods like factorization-based approaches for completely positive matrices and cutting plane techniques in copositive programming. Scientific Awards : 2013 Optimization Letters Best Paper Award 2010 VICI Grant (NWO) 2012 GIF Research Grant (German-Israeli Foundation) Key Contributions : Development of algorithms for copositive optimization, theoretical advances in matrix cones, and novel applications to problems like graph stability and discrete optimization. She serves as Senior Editor for Optimization Methods and Software and editorial board member for multiple optimization journals.

Luiz Chamonمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Constrained Optimization
• +۳ مورد دیگر

Luiz Chamon is an Assistant Professor (tenure-track) and Hi!PARIS Chair holder at the Center for Applied Mathematics (CMAP) , École polytechnique, Paris, France. His research bridges machine learning with control theory and signal processing, focusing on intelligent systems that extract, process, and act on information under constraints. Research Interests: He specializes in constrained learning frameworks for machine learning, with applications to reinforcement learning, statistical signal processing, and optimization. His work includes adversarial robustness, safe policy design, and duality analysis in constrained settings. Article Trends: Recent publications span differential equation solving (ICLR 2025), primal-dual sampling (NeurIPS 2024), and resilient reinforcement learning (IEEE Trans. Autom. Control 2024). Common themes include constrained optimization, robustness, and bridging theory with practical applications in wireless systems and graph signal processing. Scientific Awards: Hi!PARIS Chair (2025) ELLIS Scholar (2025) Best Student Paper Award (ICASSP 2020) Best Paper Award (ICASSP 2020) Top 50 Most Accessed Articles in IEEE TSP (2019) Grants & Projects: Currently leads a 4-year ANR JCJC project on "Beyond (Robust) Learning: Striking Compromises in Machine Learning" (2024). Previously involved in technical reports on aircraft vibroacoustics and wireless power systems. Labs & Teams: Heads a research group at École polytechnique developing tools for constrained learning, collaborating with institutions like University of Pennsylvania and IMPRS-IS. Organizes tutorials and panels on constrained learning and career development.

• Stochastic Modeling of Communication, Information and Service Systems
• Queueing Theory
• Stochastic Processes
• +۹ مورد دیگر

Alexander Stolyar is Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign , where he has taught courses on stochastic processes, queueing systems, and service engineering. Before joining Illinois in January 2017, he was the Timothy J. Wilmott Endowed Chair Professor at Lehigh University (2014–2017) and spent nearly two decades in industrial research at Bell Laboratories , Motorola, and AT&T Labs-Research. Stolyar’s research centers on stochastic modeling of large-scale communication, information, and service systems , including cloud data centers, communication networks, and call/contact centers. He develops mathematical frameworks for dynamic resource allocation, scheduling, congestion control, and distributed algorithms , leveraging tools from queueing theory, stochastic processes, fluid and diffusion limits, large deviations, and mean-field analysis . His recent publications (2021–2025) reveal sustained focus on asymptotic optimality in complex systems: designing lightweight algorithms that become provably optimal as system scale grows. Key themes include greedy and randomized policies for inventory systems with stochastic lead times , parallel-server systems with redundancy and cancel-on-completion , particle systems with mean-field interactions exhibiting traveling-wave behavior , and packing-constrained service systems motivated by cloud data-center operations . Scientific honors include election as INFORMS Fellow (2021) , the INFORMS Applied Probability Society Best Publication Award (2004) , and the ACM SIGMETRICS Best Simulation Paper Award (1996) . He serves or has served on the editorial boards of Queueing Systems (Editor, 2008–present), IEEE Transactions on Information Theory (2019–2021), Operations Research (2006–2011), Advances in Applied Probability / Journal of Applied Probability (2008–2015), and as Technical Program Committee member for leading conferences such as SIGMETRICS and IFIP Performance . Stolyar’s research has generated more than 30 U.S. patents spanning wireless network scheduling, cloud resource allocation, dynamic load balancing, and packet-data network control—many of which have been incorporated into commercial cellular and cloud systems. His work is supported by NSF and industry grants, and he has advised numerous PhD students and postdocs who now hold faculty and research positions worldwide.

Evren Mert Turanمشاهده پروفایل

مدرس

• Process Systems Engineering
• Machine Learning
• Optimization
• +۳ مورد دیگر

Dr. Evren Mert Turan is a Lecturer at ETH Zürich's Department of Energy and Process Systems Technology. His academic background includes a Bachelor's and Master's in Chemical Engineering from the University of Cape Town, followed by a PhD in Process Systems Engineering at the Norwegian University of Science and Technology. His research focuses on integrating machine learning and optimization techniques to address decision-making challenges under uncertainty in energy systems and process engineering. Evren's expertise spans model predictive control, real-time optimization, and data-driven approaches for complex systems. He has contributed to advancements in semi-infinite programming, feedback control policies, and steady-state detection algorithms. His work emphasizes practical applications in sustainable energy systems and industrial process optimization. Key research trends include the development of neural network-based control strategies, convex optimization methods for reduced computational complexity, and experimental validation of novel algorithms. His publications highlight interdisciplinary approaches blending machine learning with traditional engineering methodologies. Evren currently teaches the course 'Introduction to Modeling and Optimization of Sustainable Energy Systems' and actively engages in collaborative research at ETH Zürich. His contributions to scientific machine learning aim to enhance robustness and reliability in dynamic systems analysis.

Charlotte Chanمشاهده پروفایل

استادیار

• Representation Theory
• Number Theory
• Algebraic Geometry
• +۴ مورد دیگر

Charlotte Chan is an Assistant Professor at the University of Michigan in the Department of Mathematics within the College of Literature, Science, and the Arts. Her research focuses on representation theory, number theory, and algebraic geometry, particularly exploring connections between Deligne-Lusztig theory, supercuspidal representations, and the Langlands program. She has received significant funding including NSF grants DMS-2101837 and DMS-2401114, as well as a Sloan Research Fellowship. Her work involves geometric realizations of L-packets, cohomology of Deligne-Lusztig varieties, and interactions between automorphic forms and Galois representations. She has collaborated extensively with mathematicians such as M. Oi and R. Bezrukavnikov, and has contributed to quadratic forms, local-global principles, and Fourier analysis over finite fields. NSF grant DMS-2101837 NSF grant DMS-2401114 Sloan Research Fellowship She has organized special semesters at institutions like the Sydney Mathematical Research Institute and contributed to educational programs including the Arizona Winter School and Women+ and Mathematics.

• Process Systems Engineering
• Chemical Engineering
• Optimization
• +۶ مورد دیگر

Alexander Mitsos is a Professor at Forschungszentrum Jülich in Germany, where he leads research at the intersection of process systems engineering, chemical engineering, and computational methods. His work spans optimization theory, machine learning applications, and energy systems, with a focus on developing novel methodologies for complex engineering problems across multiple domains. Dr. Mitsos's research interests center on the application of advanced optimization techniques to chemical engineering problems. His primary areas of focus include: Process systems engineering and optimization Machine learning applications in chemical engineering Energy systems and hydrogen technologies Bioprocess engineering and control systems Ammonia energy storage and carbon capture His recent publications reveal a strong trend toward integrating machine learning with traditional chemical engineering approaches. He has pioneered work on graph neural networks for molecular property prediction, reinforcement learning for control systems, and bilevel optimization for energy systems. His research demonstrates a consistent focus on developing computationally efficient methods that bridge theoretical advances with practical engineering applications, particularly in sustainability-focused domains like hydrogen technologies and carbon emission reduction. The analysis of his 15 most recent publications shows a balanced portfolio between theoretical method development (e.g., optimization algorithms) and practical applications (e.g., cement production, hydrogen compression). Dr. Mitsos has mentored numerous graduate students and postdoctoral researchers, as evidenced by his extensive publication record with junior authors. His research has been supported by various grants focused on energy transition, process optimization, and sustainable chemical engineering solutions, with significant collaborations across European institutions. The funding landscape for his work appears to emphasize sustainability transitions and industrial decarbonization, particularly in energy-intensive sectors. His work appears to be conducted within a research group focused on process systems engineering, with strong connections to both computational mathematics and practical chemical engineering applications. The group maintains laboratories for experimental validation of computational models, particularly in bioprocess engineering and hydrogen technologies, while maintaining strong theoretical foundations in optimization and control theory.

Philip J. Morrisonمشاهده پروفایل

استاد

• Plasma Physics
• Fusion Physics
• Hamiltonian Dynamics
• +۷ مورد دیگر

Philip J. Morrison is a distinguished Professor of Physics at The University of Texas at Austin, holding the Texas Atomic Energy Research Foundation Professorship. He maintains dual research appointments as a Research Scientist at the Institute for Fusion Studies and Affiliated Faculty at the Oden Institute for Computational Engineering and Sciences. His career spans over four decades with continuous service at UT Austin since 1981, progressing from Assistant to full Professor. Dr. Morrison earned his B.A. (1972), M.S. (1974), and Ph.D. (1979) in Physics from the University of California San Diego. His academic journey includes postdoctoral work at Princeton University's Plasma Physics Lab and teaching positions at UCSD prior to joining UT Austin. A mathematical and theoretical physicist by training, Morrison's research centers on the intersection of plasma physics, nonlinear dynamics, and computational mathematics. His primary interests include: Computational statistical mechanics and Hamiltonian dynamics Nonlinear chaos theory in finite and infinite degree-of-freedom systems Metriplectic systems for thermodynamically consistent modeling Structure-preserving algorithms for plasma simulations Geophysical fluid dynamics applications His recent publications (2024-2025) demonstrate continued leadership in developing mathematically rigorous frameworks for plasma physics and computational methods. Dr. Morrison's exceptional contributions have been recognized with numerous prestigious awards: 2024 John Dawson Award for Excellence in Plasma Physics (APS) 2016 Alexander von Humboldt Research Award 2013 Agostinelli International Prize (Accademia Nazionale dei Lincei) 1992 Fellow of the American Physical Society Multiple teaching awards including the 2013 College of Natural Sciences Teaching Excellence Award As an educator and researcher, Morrison maintains active collaborations across disciplines through the Institute for Fusion Studies and Oden Institute. His work bridges theoretical physics with practical computational applications, particularly in fusion energy research. The Geophysical Fluid Dynamics Program has benefited from his expertise for over twenty-five years, demonstrating his commitment to interdisciplinary science. His laboratory resources are enhanced through UT Austin's advanced computational infrastructure and partnerships with national laboratories. The metriplectic frameworks he develops provide foundational tools for next-generation plasma simulation codes used in fusion research worldwide.