Kathrin Flaßkamp is a Professor at Saarland University, specializing in the Department of Systems Engineering. Her work focuses on modeling and simulation of technical systems, with applications spanning robotics, optimal control, and biomedical engineering. She is based at Campus A5 1, Room 1.04, Saarbrücken. Her research integrates control theory, artificial intelligence, and optimization to address challenges in mobile robotics, autonomous vehicles, and medical devices. A key trend in her recent articles involves leveraging model predictive control, neural networks, and Koopman operators for energy-efficient and cooperative trajectory planning. She also explores applications in stereotactic neurosurgery using continuum robots, emphasizing precision and adaptability. Her work frequently bridges theoretical advancements with real-world engineering problems, including systems with symmetries, multi-agent coordination, and data-driven methods for dynamical systems. Despite no explicit awards listed, her contributions to optimal control and robotics are evident in her extensive publication record.
Volker Mehrmann is a full professor at the Technical University of Berlin in the Institute of Mathematics , Faculty II - Mathematics and Natural Sciences. He has held academic positions at Chemnitz University of Technology and RWTH Aachen University . His roles include leadership in research centers: Spokesperson for the DFG Research Center Matheon (2008-2016), President of the European Mathematical Society (2017-2022), and committee member of the Cluster of Excellence MATH+. PhD: Bielefeld University (1982) Habilitation: Bielefeld University (1987) His research interests span Numerical Linear Algebra , Differential-Algebraic Equations (DAEs) , Control Theory , and Industrial Mathematics . Recent work focuses on port-Hamiltonian systems and model order reduction for multi-physics applications. Key scientific contributions include: ERC Advanced Grant (2011-2016) on multi-physics systems Hans Schneider Prize (2019) SIAM Fellow (2011) and AMS Fellow (2022) He serves as editor-in-chief of Linear Algebra and Its Applications and contributes to numerous editorial boards. His leadership roles include presidency in the European Mathematical Society and GAMM .
Simone Ferlin is an Adjunct Senior Lecturer at Karlstad University working with 5G and Internet evolution. She completed her PhD in computer science in 2017 at the Simula Research Lab and Universitetet i Oslo under the supervision of Dr. Ozgu Alay and Prof. Michael Welzl. Her PhD dissertation focused on increasing robustness in multipath transport with MPTCP. Dr. Ferlin's educational background includes a PhD in Computer Science from the Simula Research Lab and Universitetet i Oslo (2017). Her doctoral research centered on enhancing robustness in multipath transport protocols, specifically focusing on MPTCP (Multipath TCP). She also completed undergraduate work that contributed to a book project with Prof. Friedrich Oehme on electronics and circuit technology. Dr. Ferlin's research spans multiple domains at the intersection of networking, systems, and performance engineering. Her primary interests include network and system measurements, performance analysis, security, and congestion control. She investigates how networks like the Internet evolve, examining technology development, adoption patterns, and their impacts on various entities. Additionally, she explores ways to harmonize security and privacy while making them more usable and assessable. Her work particularly focuses on transport layer and multipath transport protocols, examining their performance and security aspects. She also investigates application and transport layer performance, automation, and monitoring. Her research extends to network programming in both Linux kernel and user space, mobile broadband networks from 2G to 5G, and their intersection with the Internet. She is deeply engaged in observability, distributed and system performance monitoring, and automation. Analysis of Dr. Ferlin's recent publications reveals a strong focus on next-generation networking technologies. Her work spans multiple domains including 5G/6G networks, transport protocols (particularly QUIC and MPTCP), network virtualization, container orchestration, and the application of machine learning to networking problems. She has increasingly incorporated large language models into network configuration and automation research. Her publications demonstrate a consistent emphasis on performance measurement, optimization, and security across diverse networking environments from the edge to the cloud. Dr. Ferlin has received notable recognition for her research contributions: Best paper award at IEEE ICIN'21 for 'Learning-based Incast Performance Inference in Software-Defined Data Centers' Applied Networking Research Prize (ANRP)'25 winner for 'NetConfEval: Can LLMs Facilitate Network Configuration?' Dr. Ferlin is actively involved in mentoring the next generation of networking researchers. She has co-supervised numerous Master's and PhD students across multiple institutions including Karlstad University, KTH, TU Berlin, University of Oslo, and universities in Brazil. Her students have worked on diverse topics including NAT64 performance comparison, system tracing visualization, network observability, ML applications to multipath transport, FEC integration with QUIC, high-performance networking for 5G, congestion control, shared bottleneck detection, multipath IoT applications, and container runtime performance. She is also involved in several significant research projects including Vinnova's SEMLA (Securing Enterprises via Machine-Learning-based Automation), Horizon Europe's CODECO (Cognitive Decentralised Edge Cloud Orchestration), and the Knowledge Foundation of Sweden's DRIVE (Data-driven Latency-Sensitive Mobile Services for a Digitized Society). Dr. Ferlin serves as Workshop Chair for ACM SIGCOMM '25, is a member of the ACM/IRTF Applied Networking Research Workshop (ANRW) steering committee, and co-chairs the Internet Congestion Control Research Group (ICCRG) at the IRTF. She previously served as Associate Technical Editor for IEEE Communications Magazine and has been active on numerous program committees for major networking conferences including SIGCOMM, CoNEXT, IMC, and PAM.
Prof. Wolfgang Blochinger is a Professor in the Department of Computer Science at Reutlingen University, specializing in Services Computing and IT Security. He leads teaching programs in Wirtschaftsinformatik (Business Informatics) at both Bachelor and Master levels, focusing on foundational topics such as Programming Basics, Operating Systems, IT Security, Cloud Computing, and Big Data Technologies. His research emphasizes Cloud Computing and High Performance Computing, particularly in elasticity control, parallel processing, and cloud resource optimization. His research projects include developing elastic parallel systems for HPC applications, cloud migration strategies, and automated cloud service generation. Notable contributions involve frameworks like TASKWORK for elastic task parallelism and the Elasticity Description Language for cloud applications. He has published extensively on serverless computing, cost-efficient cloud resource utilization, and container-based isolation techniques. Prof. Blochinger collaborates with industry partners through the university's labs, including the AI-Reallabor AIDA and Cloud Lab. His work bridges academia and industry, addressing real-world challenges in distributed systems and cloud infrastructure. Current research trends focus on self-tuning cloud services and adaptive parallel algorithms for scalable computing environments.
Joscha Gedicke is a Professor at the Institute for Numerical Simulation (University of Bonn), specializing in Numerical Analysis , Finite Element Methods , and Scientific Computing . His research focuses on adaptive algorithms, error estimation, and computational methods for partial differential equations (PDEs) and optimal control problems. Contact: gedicke@ins.uni-bonn.de | +49 228 73-69835 Teaching: Lectures on Hybrid High-Order Methods (V5E1), Adaptive Finite Element Methods (S4E1), and Discontinuous Galerkin Methods (V5E5). Research Trends: Gedicke's work spans Numerical Methods for PDEs , Adaptive Finite Element Analysis , Mixed and Discontinuous Galerkin Formulations , and Error Estimation . His recent publications emphasize Virtual Element Methods and Robust Discretizations for magnetostatic and optimal control problems. Collaborative Networks: He collaborates with researchers in computational mathematics, including institutions like TU Munich, University of Milano-Bicocca, and the University of Bonn's research seminar on Mathematics of Computation .
Marc Toussaint is Full Professor leading the Learning & Intelligent Systems Lab at TU Berlin's EECS Faculty. His research integrates machine learning, optimization, and AI reasoning to solve fundamental robotics problems like physical reasoning and human-robot interaction. He holds a physics diploma from University of Cologne and PhD from Ruhr-Universität Bochum. Key research themes include: Task-motion planning integration Reinforcement learning for robotics Physical simulation and control Probabilistic inference methods Recent publications focus on efficient kinodynamic planning, belief space planning under uncertainty, and neural policy learning. He develops open-source robotic tools like the 'robotic python package' used in academic courses worldwide. Toussaint collaborates with Amazon Robotics and MIT CSAIL, and has held positions at Max Planck Institute and University of Stuttgart.
Francis de Véricourt is Professor of Management Science and the founding Academic Director of the Institute for Deep Tech Innovation (DEEP) at ESMT Berlin, where he also holds the Joachim Faber Chair in Business and Technology. He has held faculty positions at Duke University and INSEAD and was a post-doctoral researcher at MIT, reflecting a global academic footprint across France, the USA, Germany, and Singapore. His educational background includes a PhD from Université Paris VI and an engineering degree from ENSIMAG (Grenoble Institute of Technology), establishing a strong foundation in applied mathematics and computer science. Francis's research focuses on decision science, analytics, and operations, with impactful applications in healthcare, sustainability, and human-AI interaction. He investigates how mental models—'framing'—enable individuals and organizations to transcend data and generate better alternatives for decision-making. His work emphasizes cognitive agility, translational innovation, and the role of human intuition in the age of artificial intelligence. The analysis of his recent publications reveals a consistent trajectory in understanding cognitive frameworks in decision-making, the integration of AI in human contexts, and the ethical and strategic dimensions of deep-tech innovation. His writings bridge academic rigor with practical insight, targeting both scholarly and industry audiences. ENRE Best Publication Award, INFORMS MSOM Best Publication Award, INFORMS He has been a Department Editor for Operations Research and MSOM , and his academic leadership includes establishing the Center for Decisions, Models, and Data at ESMT. He has received multiple teaching awards for his work with MBA and Executive MBA students and is deeply engaged in executive education and corporate learning solutions. His book Framers , published by Penguin Random House and listed among the Financial Times' Best Books, has amplified his influence in both academic and public spheres. Francis leads DEEP—the Institute for Deep Tech Innovation—which fosters research, education, and entrepreneurial action in areas like AI, quantum computing, and biotechnology. The DMD Center, now integrated into DEEP, explores how modeling and representation enhance decision-making beyond data. These initiatives reflect his commitment to cultivating cognitive and entrepreneurial capabilities within scientific and business communities.
Prof. Dr.-Ing. Katharina Schmitz serves as Institute Director and Vice Dean at the Institute for Fluid Power Drives and Systems, RWTH Aachen University. Her leadership within the Production Technology Cluster and extensive contributions to fluid power engineering establish her as a leading authority in mechanical engineering research and education. Her research spans fluid power systems, hydraulic component design, tribology, and physics-informed machine learning applications. She pioneers sustainable propulsion solutions through bio-hybrid fuels research while addressing fundamental challenges in polymer material behavior under hydraulic stresses. Current work focuses on carbon-neutral heavy-duty transportation, physics-based neural networks for lubrication modeling, and advanced control systems for electro-hydraulic actuators. Analysis of her 15 most recent publications reveals a dominant trend toward integrating physics-based modeling with deep learning to solve complex engineering problems. Her team consistently develops novel frameworks for cavitation prediction, flow rate determination, and material compatibility assessment - significantly advancing fluid power system reliability, efficiency, and digitalization. Scientific recognition includes: GfT Förderpreis 2023 for experimental and simulative investigation of partially hydrostatic relieved contacts in variable speed axial piston machines As head of the Institute for Fluid Power Drives and Systems, she leads cutting-edge research in sustainable fluid power technologies. The institute maintains strong industry partnerships while driving innovation in hydraulic component design, digital twins for condition monitoring, and next-generation propulsion systems through its position within RWTH Aachen's Production Technology Cluster.
Volkert Paulsen is a Senior Lecturer at the Institute of Mathematical Stochastics at the University of Münster. His career spans institutions including the University of Kiel, where he completed his Habilitation (2000), Dissertation (1994), and Diplomarbeit (1989). He has taught extensively in Financial Mathematics , Stochastic Analysis , and Mathematical Statistics , supervising over 50 Bachelor, Master, and Diploma theses on topics such as risk modeling, portfolio optimization, and derivative valuation. Research Interests: Paulsen's work focuses on Financial Mathematics (continuous-time models, American options, unit-linked insurance), Stochastic Analysis (optimal stopping, martingale methods), and Risk Modeling (credit risk, extreme value statistics). His publications include foundational studies on nonlinear observation costs in optimal stopping problems and stochastic approaches to portfolio management. Scientific Contributions: His research spans journal articles in Stochastic Processes and their Applications and Journal of Applied Probability , with recent seminar topics covering Lévy Processes , Copula Modeling , and Stochastic Volatility . He employs R for statistical applications and integrates mathematical theory with practical finance and insurance contexts. Contact: Email: Volkert.Paulsen@uni-muenster.de Room: 130.010, Orléans-Ring 10, 48149 Münster Phone: +49 251 83-33771
Prof. Dr.-Ing. Andreas Hoppermann serves as Professor of Design Theory at Niederrhein University of Applied Sciences within the Department of Engineering and Computer Science since 2009. He leads the Fluid Power and Tribology Laboratory, conducting research at the intersection of mechanical product development and tribological systems. His academic foundation includes a Mechanical Engineering degree and doctorate from RWTH Aachen University, where his dissertation investigated surface design and material selection for hydraulic components. Prior to academia, he worked as a research group leader in tribology at RWTH Aachen and as a project-leading development engineer at Voith Paper. Hoppermann's research centers on product development , design methodology , fluid power engineering , and tribology , with emphasis on technical product optimization, test rig development, and tribological phenomena in industrial applications. His work bridges theoretical modeling with experimental validation in hydrostatic bearing systems and fluid power components. Recent publications demonstrate a concentrated focus on grease-lubricated hydrostatic bearings, exploring non-Newtonian fluid behavior, pressure distribution, and control concepts. This research trajectory reflects growing industrial demand for efficient, maintenance-friendly bearing solutions in mobile machinery and automotive systems. His scientific recognition includes the prestigious Borchers-Plakette award from RWTH Aachen. Borchers-Plakette With over 100 supervised master's theses from 2009-2026, Hoppermann has guided research on hydraulic system optimization, tribological contact analysis, and mechanical design innovations. His BMBF-funded "Stahl-Schnecke" project advanced steel-based worm gear technology as bronze alternatives. Current research focuses on hydrostatic bearing performance and tribological testing methodologies. The Fluid Power and Tribology Laboratory provides experimental facilities for student projects and industry collaborations, featuring test rigs for hydrostatic bearings, tribological contacts, and fluid power systems. This infrastructure supports Hoppermann's applied research approach connecting academic inquiry with industrial problem-solving.
Ayush Tewari is an Assistant Professor at the University of Cambridge. Previously, he was a postdoctoral researcher at MIT CSAIL under Bill Freeman, Josh Tenenbaum, and Vincent Sitzmann, and completed his Ph.D. at the Max Planck Institute for Informatics under Christian Theobalt. His research focuses on visual perception, developing methods to infer 3D structured representations from images and videos, aiming to bridge the gap between human perceptual capabilities and machine learning systems. Key research interests include neural rendering, inverse rendering, 3D reconstruction, and generative models. Notable contributions include advancements in Neural Radiance Fields (NeRF), diffusion models for inverse problems, and human-centric perception studies. His work has been published in top venues such as SIGGRAPH, CVPR, ICCV, and NeurIPS. Recent research trends emphasize ambiguity-aware inverse rendering, stochastic inverse problem solving using diffusion models, and integrating forward models for 3D scene inference. His work on Diffusion with Forward Models (NeurIPS 2023) proposes a novel framework for solving inverse problems without direct supervision. Awards: Best Paper Honorable Mention at BMVC 2022 (VoRF: Volumetric Relightable Faces). Labs/Projects: Core contributor to the DFM (Diffusion with Forward Models) project, advancing 3D scene understanding via probabilistic methods.
Thorsten Koch is a Professor for Software and Algorithms for Discrete Optimization at Technische Universität Berlin , with multiple leadership roles including Head of the Applied Algorithmic Intelligence Methods (A²IM) , Digital Data and Information for Society, Science, and Culture (D²IS²C) , Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV) , and Forschungs- und Kompetenzzentrum Digitalisierung Berlin (digiS) . Based at Zuse Institute Berlin and affiliated with TU Berlin's Institute for Mathematics, he focuses on integrating mathematical optimization with high-performance computing and artificial intelligence to solve complex real-world problems. Research Pillars : Mathematical optimization algorithms Quantum computing applications AI/ML integration in decision systems Energy systems optimization Scientific software development Leadership Roles : Head of Applied Algorithmic Intelligence Methods (A²IM) Head of Digital Data & Information for Society, Science, and Culture (D²IS²C) Head of Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV) Head of Forschungs- und Kompetenzzentrum Digitalisierung Berlin (digiS) Key Collaborations : Working with IBM Quantum on quantum optimization Collaborating across institutions for energy system modeling Developing open-source optimization tools like SCIP Contributing to digital library infrastructure Recent Research Trends : Quantum optimization benchmarking Machine learning-aided optimization Multi-objective decision frameworks Energy infrastructure optimization Adaptive algorithm design CO2 network modeling Impact : Advancing hybrid optimization methods Developing open-source tools for scientific computing Building digital infrastructures for libraries and research Exploring quantum-classical algorithm synergies
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.
Lars Grasedyck is a Professor of Numerical Analysis at RWTH Aachen University. His research focuses on hierarchical matrices, tensor approximation, and numerical methods for partial differential equations and matrix equations. He has contributed to applications in biomedical engineering, particularly EEG/MEG inverse problems, and is involved in software development (HLIB, HLIB-pro). Education: Diploma and Ph.D. in Mathematics at Christian-Albrechts-Universität zu Kiel (1998, 2001), Postdoctoral work at Max Planck Institute, Leipzig (2002-2010). Research: Specializes in high-dimensional numerical methods, low-rank matrices, and tensors with applications in PDEs, uncertainty quantification, and biomedical modeling. Projects: Leads DFG-funded initiatives on adaptive tensor networks for parametric PDEs and tumor progression modeling. Advising: Supervises doctoral students including Thong Le, Maren Klever, and Dieter Moser. Software: Developed HLib and HLib-pro for hierarchical matrix computations. Conferences: Active in GAMM Fachausschuss Numerische Analysis, organizing workshops and symposia globally.
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.
