جست‌وجوی استاد و پژوهشگر

• Singular Stochastic Optimal Control
• Optimal Stopping and Free-Boundary Problems
• Stochastic Games
• +۶ مورد دیگر

Giorgio Ferrari is a Full Professor for Mathematical Finance at the Institute for Mathematical Economics (IMW), Faculty of Economics, Bielefeld University. His research bridges stochastic control theory with applications in economics, finance, actuarial science, and epidemiology. Education: B.Sc. and M.Sc. in Physics and Mathematical Physics from the University of Rome La Sapienza, Ph.D. in Mathematics for Economic-Financial Applications (2012). Academic Appointments: Post-Doctoral Researcher (2012–2015), Substitute Full Professor (2015), Junior Professor (W1) (2016–2017), Associate Professor (2017–2023), and Full Professor (2023–present) at Bielefeld University. Research Interests focus on Singular Stochastic Control , Optimal Stopping , and Stochastic Games , with applications to economic policy, financial markets, and epidemic modeling. His work extends to Mean-Field Games for large-scale strategic interactions and Free-Boundary Problems for investment decision-making under uncertainty. Scientific Contributions include groundbreaking publications in Stochastic Processes and their Applications , Mathematical Finance , and SIAM Journal on Control and Optimization . His research projects, such as the DFG SFB 1283 subproject C4 and the Research Training Group 2865 , address uncertainty in dynamic economies through game-theoretic and stochastic frameworks. Notable Awards: AMASES Best Young Researcher Paper (2014), YITP Research Prize (2017), and multiple research fellowships from the University of Padova. Leadership: Director of the Bielefeld Graduate School in Theoretical Sciences (2023–present) and Principal Investigator in major DFG-funded initiatives.

Martin Schmidtمشاهده پروفایل

استاد

• Mathematical Optimization
• Energy Systems
• Mixed-Integer Nonlinear Optimization
• +۳ مورد دیگر

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.

Armin Nurkanovićمشاهده پروفایل

استاد

• Optimal Control
• Numerical Methods
• Hybrid Dynamical Systems
• +۴ مورد دیگر

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

• Collective Intelligence
• Collaboration & Group Processes
• Network Analysis
• +۲ مورد دیگر

Nikolas Zöller is a research scientist at the Max Planck Institute for Human Development 's Adaptive Rationality department. His work focuses on collective intelligence, human-AI collaboration, and computational modeling of social systems. He combines methods from physics, computer science, and psychology to study decision-making processes in both natural and artificial collectives. Ph.D. candidate at Constructor University Bremen (2020-2024) Research Associate at Potsdam University of Applied Sciences (2015-2022) Master of Science in Physics from Freie Universität Berlin (2014) Bachelor of Science in Physics from Freie Universität Berlin (2010) His research spans: Collective intelligence frameworks Human-AI collaborative diagnostics Agent-based modeling of social networks Technological co-diffusion patterns Thermodynamic optimization in physical systems Recent publications highlight his work on: Diagnostic accuracy in human-AI collectives GitHub collaboration topology Affect control theory in group dynamics Co-diffusion of complementary technologies Scientific recognition includes: European Press Prize (2022) as part of a team Springer Best Masters Award (2014)

Pascal Kerschkeمشاهده پروفایل

استاد

• Exploratory Landscape Analysis
• Black-Box Optimization
• Algorithm Selection
• +۳ مورد دیگر

Pascal Kerschke is a Professor at the Chair of Big Data Analytics in Transportation at TU Dresden, Germany. Previously, he held positions at the University of Münster, including Head of the Research Group for Machine Learning and Data Science. His research focuses on Exploratory Landscape Analysis, Black-Box Optimization, Algorithm Selection, and Multi-Objective Optimization. He earned his PhD in Information Systems from the University of Münster (2013–2017), and Master's and Bachelor's degrees in Data Science and Management from TU Dortmund. Education: PhD in Information Systems, University of Münster (2013–2017) MSc in Data Science, TU Dortmund (2010–2013) BSc in Data Analysis and Management, TU Dortmund (2007–2010) His research interests span algorithm selection, multi-objective optimization, and the application of machine learning in optimization problems. He has contributed to the development of the R package flacco for landscape analysis and co-organized conferences like EMO 2017. Awards include the Dissertation Prize (2018) and PPSN XIV Best Paper Award (2016). He has supervised over 15 students and actively participates in initiatives like the Benchmarking Network and COSEAL. Key projects include work on automated algorithm selection, multimodal optimization, and benchmarking frameworks for iterative heuristics.

• Stochastic Bilevel Optimization
• Stochastic Complementarity Problems
• Stochastic Optimization
• +۳ مورد دیگر

Dr. Johanna Burtscheidt is a researcher at the University of Duisburg-Essen, affiliated with the Faculty of Mathematics. Her expertise lies in stochastic optimization, with a focus on bilevel optimization, complementarity problems, and applied mathematical methodologies. She holds a doctoral degree and contributes to academic discourse through research and teaching. Research interests include advancing stochastic optimization techniques for complex systems, particularly in contexts involving uncertainty and multi-level decision-making. She is a member of the Collaborative Research Center Transregio 154 (03/2020–06/2022), focusing on gas network optimization, and the Graduate School 1855 (10/2016–09/2019), addressing discrete optimization under uncertainty. Teaching responsibilities span multiple semesters, including courses such as Practical Course on Linear Optimization, Exercises in Linear Optimization, and Stochastic Optimization. Recent offerings include seminars on Bilevel Optimization in 2025. Contact: Office WSC-W-4.27, reachable via email at johanna.burtscheidt@uni-due.de . Consultation by appointment only.

• Microeconomics
• Organizational Economics
• Industrial Organization
• +۲ مورد دیگر

Professor Margaret Meyer is a leading microeconomics theorist renowned for foundational contributions to organisational economics and industrial organization. As a founding member of ESSET (European Summer Symposium in Economic Theory at Gerzensee), she has shaped one of Europe's premier economic theory forums since the early 1990s, fostering generations of young economists through high-impact scholarly exchange. Her research spans strategic communication, personnel selection mechanisms, incentive design, and information structures in organizations. Meyer's work integrates game-theoretic modeling with institutional analysis to examine how information asymmetries, strategic behavior, and organizational design affect economic outcomes. Key contributions include analyzing the persistent effects of selection luck, strategic opacity in incentive systems, and robustness properties of communication equilibria. Recent publications reveal consistent focus on information economics applications, particularly examining how strategic actors manipulate selection processes and incentive structures. Her work demonstrates methodological rigor through sophisticated game-theoretic frameworks addressing real-world organizational challenges in labor markets and decision-making systems. Scientific recognition includes: Role Models in Economics Award by European Economic Association (2023) Through ESSET leadership, Meyer has significantly influenced career trajectories of emerging economists while advancing theoretical frontiers in microeconomic organization theory.

• Optimization Theory
• Mathematical Programming
• Operations Research
• +۷ مورد دیگر

Mikhail V. Solodov is a prominent researcher at IMPA (Institute for Pure and Applied Mathematics) in Rio de Janeiro, Brazil, with significant contributions to optimization theory, algorithms, and applications. His work spans theoretical foundations and practical implementations across various domains including energy systems and mathematical programming. Research Interests: Newton and Newton-related algorithms for optimization and variational problems under weaker-than-standard assumptions Augmented Lagrangian and related techniques Nonsmooth optimization, particularly bundle methods Generalized Nash Equilibrium Problems and applications to Energy Models Optimization problems with degenerate constraints and complementarity constraints Perturbation and error-stability analysis of computational algorithms Recent Publication Trends: His recent work demonstrates continued theoretical innovation in optimization methods while increasingly focusing on energy applications. His publications show strong collaboration patterns, particularly with Alexey Izmailov and Claudia Sagastizábal, covering topics from fundamental algorithm development to practical energy market modeling. Editorial Service: SIAM Journal on Optimization (2009-2024) Mathematical Programming, Series A (since 2005) Optimization Methods and Software (since 2002)

Shihao Zhuمشاهده پروفایل

پژوهشگر

• Insurance Mathematics
• Financial Mathematics
• Stochastic Control
• +۳ مورد دیگر

Dr. Shihao Zhu is a postdoctoral researcher at the Institute of Insurance Science (IVW) at Ulm University since February 2024. He completed his Ph.D. in Mathematics at Bielefeld University, Germany (January 2021 - June 2024), supervised by Prof. Dr. Giorgio Ferrari at the Center for Mathematical Economics (IMW). His academic affiliation at Ulm University involves collaboration with Prof. Dr. An Chen. Education : Ph.D. in Mathematics, Bielefeld University (2021-2024) Current Role : Postdoc Researcher at Ulm University's Institute of Insurance Science (IVW) His research focuses on stochastic control models, insurance mathematics, and financial optimization under uncertainty. Key areas include dividend policies, reinsurance strategies, retirement planning, and health-wealth complementarities. The work often involves Markov-modulated environments and ambiguity modeling. Recent publications demonstrate expertise in solving free-boundary problems, stochastic dynamic programming, and actuarial risk modeling. Topics span optimal consumption, life insurance timing, and irreversible healthcare investment frameworks, reflecting interdisciplinary applications of mathematics in finance and insurance.

• Algorithms
• Game Theory
• Automata
• +۵ مورد دیگر

Marcin Jurdzinski is a Professor at the Department of Computer Science, University of Warwick, with a focus on algorithms, games, automata, and logic. His career spans institutions like the University of Aarhus, University of Paris 7, and UC Berkeley. Research interests include Parity games Stochastic games Timed automata Formal verification Game theory . Recent articles highlight work on parity games, stochastic systems, and timed automata, published in venues like SIAM Journal on Computing, LICS, and CAV. Keywords span Computer Science , Game Theory , and Formal Methods . PhD students mentored include Thejaswini K. S., Michail Fasoulakis, and Ashutosh Trivedi. Funded projects such as Solving Parity Games in Theory and Practice (2017-2021) reflect his research priorities.

Moritz Diehlمشاهده پروفایل

استاد

• Optimization
• Control Systems
• Embedded Systems
• +۳ مورد دیگر

Prof. Moritz Diehl is a Professor at the University of Freiburg, leading the Systems Control and Optimization Laboratory within the Department of Microsystems Engineering (IMTEK) and affiliated with the Department of Mathematics. Born in Hamburg, Germany, he holds a Ph.D. from Heidelberg University (2001) and previously served as a professor at KU Leuven (2006–2013), where he directed the Optimization in Engineering Center (OPTEC). His research focuses on optimization and control, emphasizing numerical methods for engineering applications, particularly embedded systems and renewable energy. Key areas include model predictive control (MPC), nonlinear optimization, and real-time control systems. Education: He studied physics and mathematics at Heidelberg University and the University of Cambridge (1993–1999), culminating in a Ph.D. in Scientific Computing. His academic journey includes roles at KU Leuven and Freiburg, where he has developed influential tools like the AWEbox framework for airborne wind energy systems and the acados optimization library. Research Interests: His work spans numerical optimal control, MPC algorithms, and their applications in robotics, energy systems, and automotive engineering. Recent advancements include collision-free motion planning, real-time NMPC with convex-concave constraints, and stochastic control methods for mobile robots. He also explores optimization for hybrid systems, leveraging finite elements and switch detection for nonsmooth dynamics. Publications: His 2023–2025 work highlights contributions to MPC stability, energy-efficient control systems, and software tools like LCQPow for quadratic programming. His research bridges theory and practice, addressing challenges in industrial processes, renewable energy integration, and autonomous systems. Labs & Teams: He leads the Systems Control and Optimization Lab, fostering interdisciplinary projects in optimal control, robotics, and sustainable energy. His group collaborates on tools like acados, emphasizing real-time feasibility and scalability for complex systems.

• Optimization Theory
• Numerical Optimization
• Newton-Type Methods
• +۵ مورد دیگر

Mikhail V. Solodov is a Researcher at the Institute for Pure and Applied Mathematics (IMPA) in Rio de Janeiro, Brazil, where he conducts cutting-edge research in optimization theory and algorithms. His work spans theoretical foundations and practical applications, with significant contributions to Newton-type methods, augmented Lagrangian techniques, and equilibrium problems. Dr. Solodov's educational background includes: Ph.D. in Optimization/Computer Sciences from University of Wisconsin-Madison (1995) M.S. in Computer Sciences from University of Wisconsin-Madison (1992) Diploma (with Honors) in Applied Mathematics from Moscow State University (1991) His research focuses on developing and analyzing optimization algorithms, particularly Newton and Newton-related methods for optimization and variational problems under weakened assumptions, augmented Lagrangian techniques, decomposition methods, and nonsmooth optimization (especially bundle methods). He has made significant contributions to understanding optimization problems with degenerate constraints and has developed relaxed regularity concepts for these challenging problems. Dr. Solodov's recent publications (2022-2025) show a strong trend toward applications in energy markets while maintaining theoretical rigor. His work bridges mathematical theory with practical implementation, demonstrating how optimization theory can solve real-world problems in energy systems and economic equilibrium modeling. The publications reveal increasing focus on equilibrium problems with applications to energy markets, while continuing to advance theoretical understanding of optimization algorithms. His recognition in the field is evident through his editorial appointments: Mathematical Programming, Series A (since 2005) Optimization Methods and Software (since 2002) SIAM Journal on Optimization (2009-2024) Dr. Solodov has also contributed to special journal issues dedicated to prominent figures in optimization, including a special issue on Hierarchical Optimization in Mathematical Programming (2023) and volumes dedicated to the memory of Prof. Naum Shor and Olvi Mangasarian.