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Christian Wald is a Post-doctoral researcher at Technical University Berlin working under Professor Gabriele Steidl, focusing on generative modeling, flow matching, and stochastic processes in machine learning. His research bridges theoretical probability with practical medical imaging applications, particularly in MRI reconstruction and analysis. He completed his PhD at Humboldt University of Berlin in 2017 with a thesis on p-adic quantum groups. His academic journey transitioned from pure mathematics to interdisciplinary machine learning research, reflecting his versatile expertise. Wald's primary research explores generative models through the lens of optimal transport and flow matching, with significant contributions to Wasserstein geometry and conditional distance metrics. His work frequently integrates stochastic processes to enhance medical image reconstruction, demonstrating strong cross-disciplinary impact in both theoretical machine learning and clinical applications. Recent publications highlight innovations in sliced MMD flows, Bayesian OT methods, and uncertainty-aware medical image analysis. Analysis of his 15 most recent publications (2019-2025) reveals a consistent trajectory toward unifying geometric probability with deep learning. Key themes include flow-based generative modeling for medical time-series data, optimal transport applications in image reconstruction, and novel kernel methods for distribution matching. His work spans both foundational theory (e.g., Fisher-Rao curves) and high-impact medical applications (e.g., coronary calcium scoring). No specific scientific awards are documented in the provided text, though his publications appear in prestigious venues including ICLR, IEEE TMI, and Physics in Medicine & Biology. Wald maintains extensive collaborations with the medical imaging group at Technical University Berlin, particularly with Andreas Kofler and Gabriele Steidl. His co-authored works demonstrate consistent contributions to MRI reconstruction pipelines and segmentation frameworks, though no formal advising roles or grant leadership are indicated. Current projects focus on uncertainty quantification in active learning for medical image segmentation. He operates within Gabriele Steidl's research group at Technical University Berlin, which specializes in mathematical imaging and machine learning. The team combines expertise in optimization, probability theory, and deep learning to solve medical imaging challenges, with Wald contributing core algorithmic innovations in generative modeling and stochastic reconstruction.

• Generative Modeling
• Flow Matching
• Stochastic Processes
• +3 more

Jia-Jie Zhu is a machine learner and applied mathematician currently serving as head of an independent research group at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin, with an upcoming appointment as tenured associate professor at KTH Royal Institute of Technology in Stockholm. Previously, he conducted postdoctoral research in machine learning at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, following doctoral studies in optimization and numerical analysis at the University of Florida. His research focuses on the mathematical foundations of machine learning and optimization, particularly at the intersection of computational algorithms, dynamical systems, and probability theory. Key areas include: Robust probabilistic machine learning algorithms Kernel methods for distribution manipulation Variational methods for optimization over probability distributions Gradient flows and optimal transport theory Wasserstein and Fisher-Rao geometry Applications in generative modeling and causal inference Dr. Zhu's recent work reveals deep connections between partial differential equations, kernel methods, and machine learning, resulting in theoretically grounded algorithms for handling distribution shifts. His publications demonstrate a consistent trajectory from foundational optimization theory to cutting-edge applications in robust learning and generative modeling, with increasing emphasis on the mathematical structures underlying modern ML systems. He has secured significant research funding, including a DFG Project on 'Optimal Transport and Measure Optimization Foundation for Robust and Causal Machine Learning' within the Priority Program 'Theoretical Foundations of Deep Learning' (SPP 2298), and actively organizes academic events such as the Workshop on Optimal Transport from Theory to Applications (OT-DOM) and upcoming sessions at ICSP 2025 and SwissMAP. As an educator, Dr. Zhu teaches nonparametric statistics at Humboldt University of Berlin and serves as area chair for major conferences including AISTATS 2025. He maintains an active research group with opportunities for master's students, PhD candidates, and postdoctoral researchers interested in the mathematical frontiers of machine learning.

• Machine Learning
• Optimization
• Optimal Transport
• +5 more

Prof. Dr. Sven Oliver Krumke is a Full Professor of Mathematics and currently serves as Dean of the Faculty of Mathematics at the Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU). His research focuses on Combinatorial Optimization, Approximation Algorithms, Online Algorithms, Complexity, Algorithmic Game Theory, and Graph Theory with Applications. He leads the Optimization Group (AG Optimierung) within the Department of Mathematics. Education and Academic Path: PhD (Dr. rer. nat.) in Mathematics, University of Würzburg (1997) Habilitation in Mathematics, Technische Universität Berlin (2002), Thesis: 'Online Optimization: Competitive Analysis and Beyond' Professor at RPTU since 2004 Research Projects: Principal investigator in the Research Training Group (GRK 2982) 'MIMO: Mathematics of Interdisciplinary Multiobjective Optimization' Project lead for 'ONE PLAN: Optimization in Emergency Medical Services in Rhineland-Palatinate' Teaching includes courses on Analysis, Mathematical Modeling in Economics, and Reading Courses. He supervises Bachelor's and Master's theses in Optimization-related topics. He has held roles such as Scientific Researcher at the Konrad-Zuse-Zentrum Berlin (1998–2004), and has extensive international collaborations, including a Fulbright-funded stay at SUNY Albany (1992–1993). His work emphasizes practical applications in healthcare logistics, emergency services optimization, and algorithmic solutions for real-world problems.

• Combinatorial Optimization
• Approximation Algorithms
• Online Algorithms
• +3 more

Sangyoung Park is an Assistant Professor of Smart Mobility Systems at the Faculty of Mechanical Engineering and Transport Systems, Technical University of Berlin, and is co-affiliated with the Einstein Center for Digital Future. His research focuses on two main areas: enhancing vehicle safety through digitalization and connectivity, and advancing the electrification of the transport sector with emphasis on electric vehicle battery systems design and management. He leads the Chair of Smart Mobility Systems at TU Berlin, where his team investigates how vehicle connectivity can improve energy efficiency, traffic flow, and safety in autonomous vehicle systems. Dr. Park completed his PhD in Electrical Engineering and Computer Science at Seoul National University in Korea, where he focused on energy management techniques for hybrid energy storage systems in electric vehicles. Before joining TU Berlin in 2018, he conducted postdoctoral research at the Technical University of Munich, working on energy management for smartphones in collaboration with Google and studying battery aging processes. His research interests span smart mobility systems, electric vehicle battery management, energy consumption optimization, vehicle connectivity, and autonomous driving systems. Park's work bridges the gap between design engineers and software engineers, investigating how different energy storage components (fuel cells, supercapacitors, lithium-ion batteries) should be interconnected and managed together for maximum efficiency. His research also addresses the design of charging infrastructure for electric vehicles. Analysis of Dr. Park's recent publications reveals a strong focus on digital twin technology for teleoperated driving, battery management systems for electric vehicles, and vehicle connectivity for improved safety and efficiency. His research increasingly integrates cybersecurity aspects of connected vehicles and explores novel approaches to extend battery lifespan through advanced cell balancing techniques. The interdisciplinary nature of his work connects electrical engineering, computer science, transportation systems, and urban infrastructure planning. Dr. Park supervises multiple doctoral students, including Philipp Kremer, Ongun Türkçüoglu, Kil Young Lee, Maria Claudia Miguel de Priego, Muzaffer Citir, Andrea Reindl, Subhendu Bhadra, and Hueseyin Türkyilmaz. His research is supported by various funding sources including the ECDF grant, DAAD projects (ide3a), and government scholarships. He collaborates with institutions including OTH Regensburg and Siemens Mobility. His laboratory, the Smart Mobility Systems group, focuses on developing system-level approaches for measuring, analyzing, and balancing energy consumption in battery-powered mobile systems. The team investigates how direct communication among autonomous vehicles can enable control scenarios that improve energy efficiency, traffic flow, and safety beyond what human drivers or isolated autonomous vehicles can achieve.

• Smart Mobility Systems
• Electric Vehicle Battery Management
• Energy Consumption Optimization
• +3 more

Jochen Merker serves as Professor for Analysis and Optimization at the Faculty of Computer Science and Media, Leipzig University of Applied Sciences (HTWK Leipzig). His academic profile demonstrates deep expertise in mathematical analysis, numerical methods, and computational mathematics with applications across various scientific domains. Institution: Leipzig University of Applied Sciences (HTWK Leipzig) Faculty: Computer Science and Media Position: Professor for Analysis and Optimization Contact: Available by appointment via email Professor Merker's research spans multiple mathematical disciplines with particular emphasis on partial differential equations, numerical analysis, and mathematical modeling. His work bridges theoretical mathematics with practical applications in fluid mechanics, epidemiology, and machine learning. He has made significant contributions to the understanding of doubly nonlinear evolution equations, positivity preservation in numerical methods, and rate-induced tipping phenomena. His research demonstrates how advanced mathematical techniques can solve complex problems in physical systems and data science. Analysis of his publication trends reveals a consistent focus on mathematical rigor combined with practical applicability. His recent work shows increasing integration of mathematical theory with computational approaches, particularly in digital learning environments and e-assessment systems for STEM education. The interdisciplinary nature of his publications demonstrates how mathematical analysis serves as a foundation for solving problems across physics, engineering, epidemiology, and computer science. Primary research areas: Mathematical Analysis, Numerical Methods, Partial Differential Equations Application domains: Fluid Mechanics, Epidemiology, Machine Learning Methodological focus: Positivity preservation, Maximum principles, Numerical stability Educational contributions: Digital teaching in STEM fields, E-assessment systems Professor Merker actively contributes to the academic community through his research publications and educational initiatives. His work on digital teaching methods for STEM disciplines reflects his commitment to modernizing mathematical education. While specific grant information isn't available in the provided materials, his extensive publication record suggests sustained research activity across multiple projects. His laboratory or research team likely focuses on computational mathematics and numerical analysis, though specific details aren't provided in the source material.

• Mathematical Analysis
• Numerical Methods
• Partial Differential Equations
• +5 more

Petra Mutzel is a Professor of Computer Science at the University of Bonn. Her research focuses on graph algorithmics, temporal networks, and combinatorial optimization with applications in data science and bioinformatics. She holds a PhD in Computer Science from the University of Cologne (1994) and a Diplom in Mathematics from the University of Augsburg (1990). Her work emphasizes algorithmic solutions for complex graph problems, including temporal graph analysis, graph learning, and optimization frameworks for real-world networks. She has contributed to open-source libraries like Tglib for temporal graph processing and frameworks like Scaffold Hunter for medicinal chemistry. Her research spans theoretical foundations and practical implementations, with notable contributions to graph drawing, vehicle routing optimization, and protein complex analysis. Mutzel’s recent publications (2022-2025) explore temporal network dynamics, robust combinatorial optimization, and scalable graph kernel methods. She actively engages in academic leadership, organizing conferences such as WALCOM 2022, and serves on editorial boards for journals in algorithms and computational geometry.

• Graph Algorithmics
• Graph Learning
• Combinatorial Optimization
• +3 more

Dr. Elisabeth Pfaehler is a Researcher at the Institute of Neuroscience and Medicine (INM-4) at the Research Center Jülich . Her work focuses on advancing PET radiomics , medical imaging algorithms , and machine learning applications in healthcare. She leads efforts in improving diagnostic accuracy and treatment prediction through quantitative imaging biomarkers, particularly in oncology and neurology. Key contributions include: Development of the METhodological RadiomICs Score (METRICS) for radiomics reproducibility. Optimization of PET image reconstruction and segmentation techniques for bulky tumors. Creation of open-source tools like RaCaT (Radiomics Calculator Tool) and SMART PET simulation software. Her research emphasizes interdisciplinary collaboration , with studies involving phantom testing, multicenter trials, and AI-driven image quality control. Current work addresses noise sensitivity in radiomics, uncertainty estimation, and translational applications in clinical settings.

• Medical Imaging
• Radiomics
• Machine Learning in Healthcare
• +3 more

Prof. Dr. Alexander Asteroth is a Professor of Computer Science at the Department of Computer Science , Hochschule Bonn-Rhein-Sieg (H-BRS). His work bridges Machine Learning , Surrogate Modeling , and Aerodynamic Analysis through interdisciplinary collaborations with the Institute of Technology, Resource Conservation, and Energy Efficiency (TREE) . Project leadership roles in GARRULUS (drone-based reforestation), eTa (sustainable mobility), and ELaBoR (EV charging infrastructure). Research themes: Quality Diversity Algorithms for design exploration, Bayesian Optimization , and AI in Sports Science . His publications (2017–2025) demonstrate expertise in evolutionary computation , generative models , and human-AI co-creativity . Collaborations with industry partners like GKN Driveline and academic peers (Houben, Sebastian; Hagg, Alexander) underscore his applied research focus on energy-efficient systems and sports performance modeling.

• Machine Learning
• Stochastic and Probabilistic Learning Methods
• Surrogate Modelling
• +5 more

Prof. Dr. Axel Kröner is a Professor of Optimization at the Institute of Mathematics, Martin-Luther-Universität Halle-Wittenberg, within the Faculty of Natural Sciences II. His research focuses on optimal control and numerical methods for partial differential equations (PDEs), stochastic control, and feedback mechanisms in complex systems. Academic Background: Ph.D. in Mathematics, Technische Universität München (2011) Diploma in Mathematics, Ruprecht-Karls-Universität Heidelberg (2007) Axel Kröner's research spans PDE-constrained optimization, numerical analysis, and control theory. Key areas include feedback control for hyperbolic and parabolic equations, stochastic optimal control for infinite horizon problems, and adaptive finite element methods for PDE discretization. His work bridges theoretical and computational aspects, with applications in elasticity, fluid dynamics, and quantum mechanics. Recent publications highlight advancements in Hamilton-Jacobi-Bellman equations for feedback control, bilevel optimization for parameter learning, and Runge-Kutta-based neural networks . Methodologies often integrate dynamic programming , Galerkin approximations , and error estimation for efficient solutions. Advising and Projects: He has supervised numerous Master's and Bachelor's theses on topics like wave equation control, inverse problems, and neural network optimization. Funding includes support from the German Academic Exchange Service (DAAD) and the Gaspard Monge Program for Optimization and Operations Research (2015-2018). Professional Activities: Kröner has organized major conferences such as the International Workshop on PDE Constrained Optimization and contributed to teaching at institutions including Humboldt-Universität zu Berlin and University Paris-Saclay.

• Optimal Control
• Optimization with partial differential equations
• Numerical analysis
• +3 more

Thatchaphol Saranurak is an Assistant Professor in the Computer Science and Engineering Division at the University of Michigan, College of Engineering. He holds a Ph.D. in Computer Science from KTH Royal Institute of Technology (2018), advised by Danupon Nanongkai, and was previously a Research Assistant Professor at the Toyota Technological Institute at Chicago (2018–2020). His research lies at the intersection of theoretical computer science and algorithm design, with primary interests in fast graph algorithms , dynamic algorithms , robust algorithms against adaptive adversaries , and combinatorial optimization . His work has significantly advanced the state-of-the-art in areas such as maximum flow (Gomory-Hu trees), vertex and edge connectivity, dynamic matching, expander decompositions, and distributed graph algorithms. His recent publications (2023–2025) reveal a strong trend toward deterministic, near-linear time algorithms for fundamental graph problems, often leveraging expander hierarchies and dynamic sparsification techniques . He has made breakthroughs in dynamic matching, connectivity oracles, and multi-commodity flow, frequently publishing in top venues like FOCS, STOC, and SODA. He has received several prestigious honors, including: Presburger Award 2023 NSF CAREER Award Sloan Research Fellowship His advising and grant activities are supported by major funding such as the NSF CAREER Award and Sloan Fellowship, and he actively mentors and collaborates with a large network of co-authors. He is also involved in organizing academic events, such as the Dagstuhl Seminar on Graph Algorithms. He teaches courses such as Expander and Graph Algorithms and maintains an active research group focused on pushing the boundaries of algorithmic efficiency and robustness.

• Fast graph algorithms
• Dynamic algorithms
• Robust algorithms against adaptive adversaries
• +8 more

John Volker is a Research Fellow at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS), specializing in numerical analysis and computational fluid dynamics. His work focuses on developing and analyzing finite element methods, stabilization techniques for convection-dominated flows, and applications in fluid mechanics. He has contributed to advancing PINN-based methods, pressure-robust discretizations, and optimal control strategies for industrial and biomedical fluid dynamics problems. Key research areas include: numerical methods for PDEs (convection-diffusion, Navier-Stokes), turbulence modeling, and high-resolution schemes for discontinuous Galerkin methods. His studies often address spurious oscillations, error analysis, and the integration of machine learning in numerical simulations. Notable projects include investigations into blood flow simulations, liquid steel stirring optimization, and the impact of viscosity modeling in biomedical contexts. His research bridges theoretical analysis with practical applications, contributing to both algorithm development and real-world engineering challenges.

• Computational Fluid Dynamics
• Finite Element Methods
• Numerical Analysis
• +5 more

Sven O. Krumke is a Professor in the Department of Mathematics at the Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU), where he has been a faculty member since 2004. He currently serves as the Dean of the Faculty of Mathematics, overseeing academic and administrative affairs. His research is centered in combinatorial optimization and algorithmic game theory, with strong applications in logistics, emergency response, and network design. PhD, University of Würzburg (1997) Habilitation, Technical University of Berlin (2002) Professor, RPTU Kaiserslautern-Landau (since 2004) His research interests include combinatorial optimization, approximation and online algorithms, robust scheduling, algorithmic game theory, and graph-theoretic applications. He has led major interdisciplinary projects such as GRK 2982: MIMO (Mathematics of Interdisciplinary Multiobjective Optimization) and ONE PLAN , focusing on optimizing emergency medical services in Rhineland-Palatinate. His work often bridges theoretical computer science and real-world decision-making systems. His recent publications (2016–2022) emphasize robust optimization , pandemic response logistics , decision-support systems in healthcare , and online routing . These works span domains such as vaccine location strategies, ambulance dispatch, car-sharing relocation, and scheduling under uncertainty. The recurring themes are efficiency, resilience, and algorithmic fairness in dynamic environments. Notable scientific contributions include work on network design, flow problems with budget constraints, and game-theoretic models of routing. While no specific awards are listed, his leadership in DFG-funded research groups underscores his academic standing. He actively supervises bachelor's and master's theses and teaches courses such as Grundlagen der Mathematik I: Analysis . He has collaborated with researchers across Germany and internationally, particularly in algorithmic game theory and optimization under uncertainty. Dr. Krumke leads and contributes to research teams in: AG Optimierung (Optimization Research Group) at RPTU GRK 2982: MIMO – interdisciplinary multiobjective optimization ONE PLAN – optimization in emergency medical response

• Combinatorial Optimization
• Approximation Algorithms
• Online Algorithms
• +7 more

Dr. Jan Friedrich is a PostDoc researcher at the Institute for Geometry and Practical Mathematics (IGPM) at RWTH Aachen University, where he is part of the research group led by Prof. Dr. Michael Herty. He is also a Principal Investigator in the DFG-funded SPP 2410 CoScaRa project focusing on nonlocal conservation laws with space-dependent nonlocalities. Previously, he completed his PhD and served as a PostDoc at the University of Mannheim under Prof. Dr. Simone Göttlich. Research Interests: Nonlocal Conservation Laws and Balance Laws Mathematical Modeling of Vehicular Traffic Numerical Analysis of PDEs High-order Numerical Schemes (Finite Volume, CWENO, Godunov) PDE-constrained Optimization Lyapunov Stabilization of PDEs Particle-based Optimization Methods The recent publications show a consistent focus on the analysis, modeling, and numerical simulation of nonlocal PDEs, especially in traffic flow applications. His work bridges theoretical analysis (existence, stabilization) with computational methods (high-order schemes, splitting algorithms) and practical applications (junctions, multilane traffic, moving obstacles). The trend indicates strong interdisciplinary engagement with engineering and applied sciences. Scientific Awards: Paired Early Career Fellowship in Applied Research (PECFAR), Indo-German Science & Technology Centre (IGSTC, BMBF), 2025 Advising and Grants: Although no direct students are listed, Dr. Friedrich has co-supervised project staff such as Anika Beckers (RWTH Aachen) and Fabian Ziegler (University of Mannheim) in the CoScaRa project. He has secured competitive funding including the DFG SPP 2410 grant and the PECFAR fellowship, demonstrating strong grant acquisition ability. He has also contributed to academic advising through teaching assistant roles and mentoring in research projects. Labs and Teams: He is an active member of the research group of Prof. Dr. Michael Herty at IGPM, RWTH Aachen. He collaborates closely with Prof. Dr. Simone Göttlich (University of Mannheim) and is co-Principal Investigator in the joint DFG project. He has also been involved in international research networks with institutions in India (IIPE), France (INRIA), and the USA (ASU).

• Nonlocal Conservation Laws
• Mathematical Modeling of Vehicular Traffic
• Mathematical and Numerical Analysis of PDEs
• +7 more

Anna Korba is a researcher affiliated with ENSAE & CREST, Institut Polytechnique de Paris, France. She specializes in sampling methods through optimization of discrepancies, focusing on theoretical and applied aspects of optimal transport, Bayesian inference, and gradient flows. Research Interests: Machine Learning, Probability Theory, Variational Methods, Non-convex Optimization. Recent Work: Interpolating between MMD and χ² divergences, developing mollified interaction energy descent algorithms, and analyzing quantization errors in particle-based optimization. Collaborations: Joint work with researchers across institutions including UCL, CMU, and EPFL. Scientific Contributions: Her publications address challenges in sampling from non-log-concave distributions, fairness constraints in Bayesian neural networks, and geometric interpretations of gradient flows in Wasserstein spaces.

• Machine Learning
• Optimal Transport
• Probability Theory
• +7 more

Carlotta Langer is a researcher currently affiliated with the Psychology Department at Ludwig Maximilian University of Munich (LMU Munich), where she works within the interdisciplinary field of embodied cognition and information theory. She also maintains a guest researcher position at the Institute for Data Science Foundations at Hamburg University of Technology, contributing to the DFG priority program "The Active Self." Dr. Langer completed her undergraduate studies in Mathematics at Martin-Luther University Halle-Wittenberg before pursuing her doctoral research at Hamburg University of Technology, where she earned her PhD with a dissertation titled "Information Integration in Embodied Systems" (2024). Her research focuses on understanding how information flows through embodied systems, examining the intricate relationships between body, brain, and environment. Langer applies information-theoretic measures to quantify morphological computation and integrated information in embodied agents. Her work on complexity measures explores how systems exhibit properties that emerge from the interactions of their components, rather than from the components themselves. She also investigates information-geometric algorithms such as iterative scaling and the EM algorithm, analyzing their mathematical properties and applications to embodied cognition. Langer's recent publications (2017-2025) demonstrate a consistent trajectory exploring the theoretical foundations of embodied cognition through the lens of information theory. Her work bridges disciplines including cognitive science, computer science, and complex systems theory, with particular emphasis on how physical embodiment shapes cognitive processes and information integration. She frequently collaborates with researchers like N. Ay and contributes to advancing mathematical frameworks for understanding consciousness and agency in embodied systems. Dr. Langer has presented her research at multiple Models of Consciousness Conferences (2021-2024), demonstrating growing recognition of her work in theoretical cognitive science and consciousness studies. Her computational implementations are publicly available on GitHub, reflecting commitment to reproducible research. Her laboratory work involves developing computational models of embodied agents, particularly examining how sensorimotor loops and morphological properties contribute to information processing efficiency. The code repositories associated with her publications demonstrate rigorous methodological approaches combining theoretical analysis with practical implementation.

• Information Theory
• Embodied Cognition
• Complex Systems
• +3 more