Find Academic Supervisors

• Energy Systems Optimization
• Conceptual Process Design
• Life Cycle Assessment
• +8 more

Prof. Dr. André Bardow is a Full Professor in Energy and Process Systems Engineering at ETH Zurich , leading research at the intersection of thermodynamics, machine learning, and sustainable energy systems. Previously, he held professorships at RWTH Aachen University (2010-2020) and TU Delft (2007-2010). He also served as part-time director at Forschungszentrum Jülich (2017-2022) and visiting professor at UC Santa Barbara (2015/16). His work focuses on energy systems optimization , computer-aided molecular design , and CO2 capture & utilization . PhD from RWTH Aachen University Current ETH Zurich affiliation Former roles at RWTH Aachen, TU Delft, Jülich Research Center His research integrates machine learning with thermodynamic modeling to optimize processes like crystallization and electrochemical cooling . Recent publications demonstrate advancements in solvent design, CO2 transport LCA, and ORC working fluid optimization. He chairs the VDI Technical Committee for Thermodynamics (2016-2024) and has received multiple awards including the Covestro Science Award and Arnold-Eucken-Award . Current projects address carbon circular economies , electrified chemical production , and AI-driven process optimization . His lab at ETH Zurich develops cutting-edge technologies like ML-CAMPD frameworks for sustainable separation processes and photoacid-based CO2 capture systems. Funding from the H2020 Systemic Expansion of Circular Ecosystems (grant 101036854) supports these initiatives. 2024 Clarivate Highly Cited Researcher 2022 Inaugural Lecture: "To sustainability and beyond: A computer-animated story on energy & chemicals" Recipient of multiple teaching and research excellence awards

Erik RodnerView profile

Professor

• Machine Learning
• Computer Vision
• Data Science
• +5 more

Prof. Dr. Erik Rodner is a faculty member at the University of Applied Sciences Berlin (HTW Berlin), where he serves as a Professor for Machine Learning and Data Science. He also contributes to the School of Engineering Sciences - Technology and Life. His research spans computer vision, machine learning, and biomedical applications, with a focus on learning with limited data, robust visual recognition models, and medical image analysis. He has developed innovative methods for medical diagnostics, industrial classification, and anomaly detection. Recent publications (2025-2016) highlight his expertise in visual in-context learning, semi-weakly segmentation, and active learning frameworks. He has collaborated with institutions such as ZEISS Group, Friedrich Schiller University Jena, and UC Berkeley. Scientific Awards: Award for Excellent Teaching (2023)

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.

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.

• 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.

• Combinatorial Optimization
• Algorithm Design
• Game Theory
• +3 more

Jannik Matuschke is an Associate Professor of Operations Management at the Department of Decision Sciences and Information Management, KU Leuven (Belgium). He holds affiliations with the KU Leuven Institute for Artificial Intelligence (Leuven.AI) and the KU Leuven Institute for Mobility (LIM). His research focuses on combinatorial optimization, algorithm design, game theory, robustness under uncertainty, and applications in logistics and production systems. Education: PhD in Mathematics (2013) from TU Berlin, advised by Martin Skutella and Britta Peis Postdoctoral positions at Universidad de Chile (2014) and TU München (2016–2018) DAAD P.R.I.M.E. fellowship at University of Rome 'Tor Vergata' (2015) Research Themes: Design of resilient infrastructures using multi-stage optimization Stochastic and robust project scheduling Applications in logistics network analytics and congestion modeling Recent Contributions: Recipient of the 2024 Meritorious Service Reward from Operations Research Journal Co-chair of WAOA 2025 and editorial roles at Omega, Operations Research Letters, and OR Spectrum Active in international workshops like FRICO 2025 and the Santiago Summer Workshop on Combinatorial Optimization Academic Leadership: Supervises 5 current PhD/postdoc researchers across logistics and optimization Coordinates the Master's Thesis program in Production and Logistics at KU Leuven Manages research projects on robust infrastructure design (2022–present) and stochastic scheduling (2020–present)

Christian KundeView profile

Researcher

• Process Optimization
• Global Optimization
• Surrogate Modeling
• +5 more

Dr.-Ing. Christian Kunde is a PostDoc researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in the Process Synthesis and Process Dynamics group since 2021. Previously, he served as a PostDoc at the Chair for Automation/Modeling at Otto-von-Guericke University (OVGU) from 2017-2021, and as a Research Assistant and PhD Student at the same institution from 2009-2017. His educational background includes a Diploma in Systems Engineering and Engineering Cybernetics from OVGU (2004-2009) and a PhD dissertation titled "Global optimization in conceptual process design" completed in 2017. His formal engineering doctorate (Dr.-Ing.) reflects his specialized expertise in process systems engineering. Kunde's research focuses on physics- and data-based hybrid modeling for process optimization and state estimation, with particular emphasis on global optimization techniques and surrogate modeling. His work bridges theoretical computational methods with practical chemical engineering applications, addressing challenges in distillation, crystallization, and fuel cell systems. He has developed innovative approaches combining first-principles models with machine learning for improved process design and control. His publication record demonstrates consistent contributions to chemical process optimization, with recent work expanding into machine learning applications for Power-to-X processes and fuel cell modeling. The research shows a clear trajectory from fundamental optimization methods toward increasingly complex hybrid modeling approaches that integrate physics-based and data-driven techniques. Kunde actively contributes to academic knowledge dissemination through teaching, having instructed courses including Distributed Parameter Systems, State Estimation, and Systems and Control. His teaching portfolio reflects his research expertise in process dynamics and control systems, providing students with both theoretical foundations and practical applications in modern process engineering.

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

Claudia GotzesView profile

Researcher

• Stationary Flows in Gas Networks
• Optimization
• Stochastic Optimization
• +1 more

Dr. Claudia Gotzes is a researcher in the Faculty of Mathematics at the University of Duisburg-Essen, specializing in optimization problems related to gas network infrastructure. Her work combines mathematical modeling with practical applications in energy transportation systems, focusing on stochastic optimization methods for network reliability and capacity planning. Recent publications address mixed integer programming formulations, load feasibility in pipeline networks, and probabilistic approaches to infrastructure optimization. She has contributed to collaborative research projects funded by BMWI examining technical capacities in gas networks.

Frauke LiersView profile

Professor

• Optimization under Uncertainty
• Distributionally Robust Optimization
• Quantum Computing
• +5 more

Prof. Dr. Frauke Liers holds the Professorship of Optimization under Uncertainty & Data Analysis at the Department of Data Science (DDS), Friedrich-Alexander-University Erlangen-Nürnberg. Her research focuses on robust and distributionally robust optimization, mathematical programming, and applications in energy systems, healthcare logistics, and quantum computing. Email: frauke.liers@fau.de ResearchGate: Frauke Liers Research Interests span optimization under uncertainty, data-driven mathematical programming, and interdisciplinary applications. Key areas include: Distributionally robust optimization with scenario reduction and chance constraints Quantum computing optimization for gate routing and noise suppression Energy system modeling (photovoltaics, gas networks, electricity networks) Healthcare logistics (patient transport scheduling under uncertainty) Nanoparticle technology and chemical process optimization Recent Publications emphasize: Advancements in quantum circuit optimization (2025) Explainable optimization methods (2024) Robust approaches for particle precipitation control (2024) Dynamic trajectory optimization (2023) Time-expanded models for network flows (2022)

Paul MannsView profile

Assistant Professor

• Mathematical Optimization
• PDE-Constrained Optimization
• Integer Programming
• +1 more

Paul Manns is an Assistant Professor of Optimization at TU Dortmund University's Department of Mathematics, appointed in 2021. His research specializes in mathematical optimization involving partial differential equations and integer constraints, with emphasis on regularization techniques and trust-region algorithms. Education includes: Ph.D. in Mathematics, TU Braunschweig (2019) Computational Engineering studies, TU Darmstadt Prior research experience includes positions at Heidelberg University, TU Braunschweig, and Argonne National Laboratory (USA), including a James H Wilkinson Fellowship. Recent publications develop novel methods for mixed-integer control problems, domain decomposition, and convergence analysis in non-convex optimization spaces.

Danial Hamedi JamaliView profile

Researcher

• Hydrogen Infrastructure
• Renewable Energy Systems
• MINLP Optimization
• +3 more

Danial Hamedi Jamali is a doctoral researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany, affiliated with the Process Systems Engineering group under Prof. Kai Sundmacher. He holds an M.Sc. from the University of Tehran and was awarded scholarships for his bachelor’s and master’s studies, along with the Dean’s Scholarship from the University of Saskatchewan. His research focuses on topology optimization of pipeline networks using mixed-integer nonlinear programming (MINLP), with recent emphasis on hydrogen infrastructure design and retrofitting of natural gas grids. His expertise spans MINLP optimization, energy-exergy analysis, and sustainable system design. He has authored over ten peer-reviewed publications (over 1,000 citations) and serves as a reviewer for Elsevier and Springer journals. Collaborations include international research teams addressing renewable energy systems and low-temperature solar technologies. Research highlights include work on hydrogen pipeline networks, biogas utilization frameworks, and solar-based multi-generation systems. Awards include scholarships for academic excellence and the prestigious Dean’s Scholarship. His contributions bridge theoretical optimization with practical applications in energy transition and infrastructure sustainability.

Michael SchusterView profile

Research Fellow

• PDE Constrained Optimization
• Optimization under Uncertainty
• Probabilistic Constraints
• +3 more

Michael Schuster is a Research Fellow at the Chair for Dynamics, Control, Machine Learning and Numerics (DCN-AvH) at Friedrich-Alexander University Erlangen-Nuremberg (FAU), operating under the Alexander von Humboldt Professorship. He actively contributes to the CRC TRR154 project focused on gas network optimization. His academic foundation includes: Industrial Mathematics studies at FAU PhD in Mathematics from FAU (2021) with dissertation on nodal control and probabilistic constrained optimization for gas networks His research specializes in PDE constrained optimization, optimization under uncertainty, and probabilistic robustness, with direct applications to gas network systems. He has pioneered work on the turnpike phenomenon in optimal control under uncertain conditions, establishing critical theoretical frameworks for long-horizon control problems. Analysis of his publication record reveals a consistent focus on mathematical optimization of energy infrastructure, particularly addressing compressor station placement, boundary control of wave equations, and probabilistic constraints in flow networks. His interdisciplinary approach bridges pure mathematics with engineering applications in gas transport systems. Supported by the German Research Foundation (DFG) through the CRC TRR154 grant, his work integrates advanced numerical methods with real-world energy market modeling. He collaborates extensively within the FAU MoD (Mathematics and Data Science) initiative and maintains active international research partnerships.

Lei BuView profile

Professor

• Software Verification
• Software Testing
• Software Analysis
• +8 more

Lei Bu is a Professor and Vice Dean of the Software Institute at Nanjing University, China. He has been with Nanjing University since 2010, progressing from Assistant Professor to his current position. His academic career includes a visiting position at Microsoft Research Asia through their StarTrack Program from 2014-2015. Professor Bu received his B.Sc. and Ph.D. degrees in Computer Science from Nanjing University, with additional research experience at Carnegie Mellon University and University of Texas at Dallas during his doctoral studies. His academic journey shows steady progression through the ranks at his alma mater. Lei Bu's research primarily focuses on software verification, testing, and analysis, with particular expertise in model checking, bounded model checking, formal methods, and cyber-physical systems. His work spans theoretical foundations to practical applications in safety-critical systems. Recent publications demonstrate growing interest in integrating machine learning techniques with traditional formal methods, as seen in works like SpecGen that leverage large language models for program specification generation. His research outputs show a consistent focus on verification techniques for complex systems, particularly addressing challenges in hybrid systems, cyber-physical systems, and IoT applications. Professor Bu has developed several verification tools including BACH (Bounded Reachability Checker for Linear Hybrid Automata) and BRICK (Bounded Reachability Checker of Numerically-Intensive C Code), which have been used in international verification competitions. 2023: Zhongchuang Software Talent Award 2022: CCF-IEEE CS Young Computer Scientist Award 2020: The Outstanding Teachers of Computing in Higher Education Award Program 2019: NASAC Young Software Innovation Award 2016: Young Talent Development Program 2014: StarTrack Program Visiting Young Faculty 2007: Full Scholarship under the State Scholarship Fund Professor Bu serves as a Principal Investigator on multiple significant research projects funded by the Natural Science Foundation of China, including a Key Program grant (2023-2027) on dynamic adjustment-control-fault tolerance theory. He has also advised numerous students and taught courses including Formal Languages and Automata, Theoretical Foundation of Software Engineering, and Preliminary Introduction to the Theory of Computation. His laboratory work focuses on developing practical verification tools for industrial applications in cyber-physical and IoT systems.

Falk HanteView profile

Professor

• Optimization of complex systems
• Optimal control of partial differential equations
• Mixed-integer optimal control
• +5 more

Prof. Falk Hante is a Professor of Applied Mathematics at Humboldt University of Berlin's Faculty of Mathematics and Natural Sciences, specializing in optimization of complex systems. He holds positions as Deputy Managing Director of the Institute of Mathematics and spokesperson roles in TRR154 (Mathematical Modeling of Gas Networks). His research focuses on optimal control of dynamic systems, particularly involving partial differential equations, with applications in gas networks and hybrid systems. Education: PhD in Mathematics (2010), FAU Erlangen-Nuremberg Habilitation (2018), FAU Erlangen-Nuremberg Research Interests: He investigates applied analysis, numerical mathematics, and mathematical optimization, with emphases on: Optimization of switching systems Network dynamics control Model predictive control Gas transport network optimization Recent Articles Trends: Recent work emphasizes gas network optimization, stabilization of PDE systems, and general equilibrium problems. Methodologies include mixed-integer programming and relaxation techniques for PDE-constrained systems. Awards: STAEDTLER Promotional Prize (2011) MATCH Postdoctoral Fellowship (2011–2012) Advising/Grants: Supervises research projects including Math+ (Decision-Making for Energy Networks) and DFG-funded TRR154 subprojects. Former advisees include Yan Brodskyi and Fabian Rüffler. Labs/Teams: Leads the Optimization of Complex Systems working group, collaborating with researchers like Michael Hintermüller on algorithmic optimization seminars and gas network modeling.