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Professor Ralph Bergmann is a distinguished academic at the University of Trier, where he holds the position of Professor of Business Information Systems since 2004. He also serves as the research group leader for Experience-Based Learning Systems (EBLS) at the German Research Center for Artificial Intelligence (DFKI) Trier branch since 2020. His academic career spans multiple institutions with appointments at the University of Hildesheim (2001-2004) and the University of Kaiserslautern (1997-2001). Dr. Bergmann's research focuses on the integration of data-oriented AI methods with semantic technologies to create hybrid AI systems. His work centers on experience-based learning systems that combine machine learning, case-based reasoning, ontologies, and knowledge graphs for modeling explicit knowledge. This research enables the redesign, adaptation, and flexible execution of processes with applications across diverse domains including Industry 4.0, construction, crisis management, service, medicine, cooking, and political argumentation. His recent publications demonstrate a strong trend toward integrating traditional case-based reasoning with modern AI techniques like large language models and knowledge graphs. The research shows increasing application in healthcare (particularly oncology and emergency response), IoT-enabled process monitoring, and argumentation analysis. His work bridges theoretical AI foundations with practical implementations across multiple industries. Professor Bergmann has led approximately 35 research projects funded by prestigious organizations including the European Union, German Research Foundation (DFG), Federal Ministry of Education and Research (BMBF), and the State of Rhineland-Palatinate. With an h-index of 40, he has authored more than 200 scientific publications including four books and 13 conference proceedings. He actively contributes to the academic community through service on editorial boards including Engineering Applications of AI and the International Journal of Intelligent Information Technologies. His leadership extends to program committees for major conferences in artificial intelligence and case-based reasoning. Professor Bergmann heads the Artificial Intelligence and Intelligent Information Systems research group at the University of Trier and leads the Experience-Based Learning Systems research group at DFKI. His teams work on multiple projects including AI-AIM (AI-based anonymization in medicine), KIAFlex (interactive AI assistance for predictive and flexible control in discharge and transition management), DZW (Digital Twin Water Management), myRPA (experience-based Robotic Process Automation), and SPELL (Semantic Platform for Intelligent Decision and Operational Support in Control Centers).

• Artificial Intelligence
• Case-Based Reasoning
• Experience-Based Learning Systems
• +12 more

Emre Sertöz is an assistant professor with tenure in the Algebra, Geometry, and Number Theory group at the Mathematical Institute of Leiden University. Prior to his current position, he held postdoctoral positions at Leibniz University Hannover, Max Planck Institute for Mathematics in Bonn, and Max Planck Institute for Mathematics in the Sciences in Leipzig. He completed his PhD in Mathematics at Humboldt University of Berlin with a short stay at University of Amsterdam. His educational background includes: PhD in Mathematics, Humboldt University of Berlin (with a short stay at University of Amsterdam) Dr. Sertöz's research focuses on the intersection of algebraic geometry, number theory, and computational mathematics. His work primarily explores the arithmetic properties of periods of algebraic varieties, computational methods in algebraic geometry, and moduli spaces. He has developed computational tools like PeriodSuite for calculating periods and Picard ranks of surfaces. His research bridges theoretical mathematics with practical computational approaches, making significant contributions to understanding the transcendental aspects of algebraic varieties. His scholarly contributions have been published in prestigious journals including the Journal of the London Mathematical Society, Algebra & Number Theory, and SIAM Journal on Applied Algebra and Geometry. His work shows a strong trend toward integrating computational methods with classical algebraic geometry problems, particularly in the areas of period computation, Hodge theory, and moduli spaces. Dr. Sertöz has received recognition for his work through invitations to present at notable venues such as Oberwolfach, and his research has been supported through various academic positions at prestigious institutions. As an advisor, Dr. Sertöz has mentored several Master's students including Rafael Mohr, Samuele Pollaci, and Kamillo Ferry. His organizational contributions include co-organizing workshops and seminars on topics ranging from computational geometry to Hodge structures, demonstrating his commitment to advancing research in his field. His current and upcoming activities include organizing the Lorentz workshop "Recurrence, transcendence, and Diophantine approximation" in July 2025, and various reading seminars on advanced topics in algebraic geometry.

• Algebraic Geometry
• Number Theory
• Computational Algebraic Geometry
• +5 more

Corinna Coupette is an Assistant Professor of Computer Science at Aalto University's School of Science, where she leads the Telos Lab at the intersection of law, computer science, and complex systems. She serves as Program Director for the interdisciplinary Master’s Program in Information Networks and holds affiliations as a Guest Researcher at Max Planck Institute for Informatics, Research Affiliate at Max Planck Institute for Tax Law and Public Finance, and member of ELLIS . Ph.D. in Computer Science (Dr. rer. nat., Saarland University, 2023) Ph.D. in Law (Dr. iur., Bucerius Law School, 2018) Her research focuses on computational legal theory and legal network science , using computational methods to model legal systems as networks and graphs. Key themes include responsible AI regulation, algorithmic fairness, and hypergraph formalization of legal data. Recent work spans quantum computing limits, graph-learning evaluation frameworks, and latent-space topological analysis. She has received accolades such as the Digital Futures Postdoctoral Fellowship (2023), Caroline von Humboldt Prize (2022), and Otto Hahn Medal (2020). Her publications in venues like ICML, KDD, and ICLR emphasize scalable algorithms for legal and social systems. Scientific Awards Outstanding Reviewer (multiple conferences, 2022–2025) Digital Futures Fellowship (2023) Caroline von Humboldt Prize (2022) Otto Hahn Medal (2020) Bucerius Best Dissertation Award (2018) Corinna actively mentors and collaborates with students and researchers globally, emphasizing interdisciplinary approaches to data-driven legal analysis and complex systems modeling.

• Legal Network Science
• Computational Legal Theory
• Graph Algorithms
• +7 more

Prof. Dr. Gjergji Kasneci is a Professor of Responsible Data Science at the Technical University of Munich (TUM), leading the Chair of Responsible Data Science. He holds affiliations with the TUM School of Social Sciences and Technology and the TUM School of Computation, Information and Technology. His research focuses on ethical, legal, and societal aspects of AI, emphasizing transparency, fairness, and robustness in machine learning algorithms. Prof. Kasneci’s academic journey includes a PhD in Computer Science from the University of Marburg (2009), postdoctoral research at Microsoft Research Cambridge, and leadership roles at the Hasso Plattner Institute and SCHUFA Holding AG. He was an Honorary Professor at the University of Tübingen (2018–2023) and currently serves as Vice Dean and Information Officer at TUM. Key awards include the Seoul Test of Time Award (2018) and an Honorary Professorship from the University of Tübingen (2019). He leads initiatives like the AI in Finance Lab and contributes to AI policy through projects such as the EU-funded AI4POL initiative.

• Machine Learning
• Artificial Intelligence Ethics
• Algorithmic Fairness
• +4 more

Prof. Zeynep Akata is a distinguished professor of computer science at the Technical University of Munich (TUM) and director of the Institute for Explainable Machine Learning at Helmholtz Munich. Her research focuses on developing transparent AI systems that can interact reliably with humans, integrating methods from machine vision, natural language processing, and learning. She holds the Liesel Beckmann Distinguished Professorship and has held academic positions at institutions including the University of Tübingen, University of Amsterdam, and UC Berkeley. Education: PhD from INRIA Rhone Alpes (2014), followed by postdoctoral research at the Max Planck Institute for Informatics and UC Berkeley. Career highlights include ERC Starting Grant (2019) and the European Computer Vision Young Researcher Award (2022). Research interests span explainable AI, zero-shot learning, vision-language models, and mitigating biases in AI systems. Her work emphasizes ethical AI deployment and human-centered AI design. Awards: Alfried Krupp Promotion Prize (2023), German Pattern Recognition Award (2021), and Werner von Siemens Ring Young Researcher Award (2019). Her laboratory at TUM and Helmholtz Munich focuses on advancing interpretable machine learning frameworks, with projects addressing societal impacts and technical challenges in AI transparency.

• Explainable Machine Learning
• Artificial Intelligence
• Machine Vision
• +3 more

Prof. Dr. Jingui Xie is an Associate Professor of Business Analytics at the TUM School of Management (TUM Campus Heilbronn) and a core member of the Munich Data Science Institute. His research focuses on improving healthcare systems through data analytics, queueing models, and robust optimization frameworks. Key areas include ICU data analytics, healthcare resource planning, and the impact of air pollution/COVID-19 on medical systems. Previously, he held roles at Brunel University London, Cambridge Judge Business School, and the National University of Singapore. He earned his PhD from Tsinghua University and has received awards such as the POMS-HK Student Paper Competition (2018) and the Tsinghua Outstanding Dissertation Award (2010). Education: PhD in Operations Research, Tsinghua University Postdoctoral Research at National University of Singapore (2010–2011) Research Interests: Healthcare Data Analytics for ICU decision-making Joint estimation and robust optimization frameworks Applications of machine learning in healthcare operations Awards: First Prize, POMS-HK Student Paper Competition (2018) Junior Teaching Award, USTC (2016) Key Projects: COVID-19 Data Analytics: Linking environmental factors to infection rates ICU Data Analytics: Optimizing ventilator extubation decisions Bed Shortage Analytics: Coherent metrics for hospital resource planning

• Business Analytics
• Healthcare Management
• Data-Driven Healthcare Optimization
• +3 more

Georg Loho is a Professor at Freie Universität Berlin (FU Berlin), acting head of the Discrete Geometry and Topological Combinatorics Group. Previously, he held an assistant professorship at the University of Twente (on leave since 2023). He specializes in discrete geometry, optimization, and algebraic combinatorics, with notable contributions to tropical geometry and machine learning. His research integrates geometric and combinatorial methods with applications in optimization and data science. Education: PhD in Mathematics (2017) from TU Berlin Diploma in Mathematics (2012) from Universität Würzburg Research Interests: Focuses on tropical geometry, discrete optimization, and their applications in machine learning. Explores geometric structures like oriented matroids, polytopes, and their connections to neural networks and algorithm design. Advocates for sustainability in research and education. Teaching: Leads courses on discrete geometry, mathematics & sustainability, and optimization. Active in educational innovation, including free open-source course materials and the MatchTheNet educational game on polytopes. Grants & Collaborations: Participated in the HIM Trimester Program (Bonn, 2021), substitute professorships (Kassel, 2020–2021), and multiple ERC-funded projects. Collaborates with institutions like the London School of Economics (LSE) and EPFL. Labs/Teams: Coordinates the Discrete Geometry and Topological Combinatorics research group at FU Berlin, fostering interdisciplinary projects in geometry, combinatorics, and optimization.

• Discrete Geometry
• Topological Combinatorics
• Optimization
• +4 more

Ana Djurdjevac is an Assistant Professor in the Department of Numerical Analysis and Stochastics at the Freie Universität Berlin , within the Department of Mathematics and Computer Science. Her research focuses on numerical analysis, stochastic processes, and partial differential equations, with particular emphasis on uncertainty quantification and mathematical modeling in evolving domains. She teaches advanced courses such as Numerical Methods for Stochastic Differential Equations and Stochastik I , reflecting her expertise in computational methods and probabilistic frameworks. Her work integrates theoretical analysis with practical numerical techniques, addressing challenges in domains such as fluid dynamics, quantum systems, and biological surface fluctuations. Recent contributions include studies on hybrid algorithms for particle systems, rough homogenization in stochastic dynamics, and synchronization mechanisms in conservation laws. Djurdjevac actively participates in academic events, including the 2025 SIAM Conference on Computational Science and Engineering, and collaborates on projects involving quasi-Monte Carlo methods for Bayesian inversion and domain decomposition techniques. Professional activities highlight her role in shaping emerging fields like stochastic PDEs on evolving domains and feedback loops in agent-based models. While no specific awards are listed, her prolific publication record and teaching roles underscore her contributions to computational science and applied mathematics.

• Numerical Analysis
• Stochastic Processes
• Partial Differential Equations
• +3 more

Prof. Dr. Willy Dörfler is a full professor at the Institute for Applied and Numerical Mathematics within the Faculty of Mathematics at Karlsruhe Institute of Technology (KIT). He leads the research group on Numerical Methods for Partial Differential Equations and teaches advanced courses including Adaptive Finite Element Methods and Modeling and Simulation of Li-Ion Batteries . His work focuses on numerical analysis, scientific computing, and multi-physics simulations. His research spans Partial differential equations with adaptive discretization Wave propagation and space-time methods Battery modeling with chemo-mechanical coupling Lattice Boltzmann techniques for fluid dynamics Applications in mechanics, optics, and electrochemistry as evidenced by his 15 most recent publications. These works emphasize finite element methods, discontinuous Galerkin approaches, and high-performance computing for complex systems. Contact: willy.doerfler@kit.edu | Office hours: Mondays 14:30–15:30 during lecture periods

• Numerical Analysis
• Partial Differential Equations
• Scientific Computing
• +3 more

Professor Mathias Trabs is a faculty member at the Karlsruhe Institute of Technology (KIT), where he has been serving as a Professor since 2021. He is affiliated with the Department of Mathematics, specifically within the Institute of Stochastics. Previously, he held positions as Heisenberg professor at Universität Hamburg (2021) and Assistant professor at Universität Hamburg (2016-2021). His research spans several key areas in modern statistics and probability theory. Professor Trabs specializes in Nonparametric and high-dimensional Statistics, Statistics for stochastic processes, Statistical inverse problems, Statistical Learning, and Stochastic (partial) differential equations. His work bridges theoretical statistics with practical applications in physics, machine learning, and high-energy experiments. Analysis of his recent publications reveals a strong focus on the intersection of statistical theory and machine learning, particularly in generative models, neural networks, and high-dimensional data analysis. His work also demonstrates significant contributions to the mathematical foundations of stochastic processes and their applications in physical sciences. Professor Trabs has supervised numerous doctoral students including Lea Kunkel, Thea Engler, Jan Rabe, Sebastian Bieringer, Maximilian F. Steffen, and Florian Hildebrandt. His research has been supported by various projects including the Data Science in Hamburg - Helmholtz Graduate School for the Structure of Matter (DASHH), DFG project TR 1349/3-1 on high-dimensional statistics, and the LD-SODA research project. He is actively involved in academic leadership, serving as Deputy speaker of the KIT Center MathSEE (Mathematics in Sciences, Engineering, and Economics), and as a member of the steering boards of both the DMV-Fachgruppe Stochastik (Probability and Statistics Group of the German Mathematical Society) and the KIT Graduate School Computational and Data Science (KCDS).

• Nonparametric Statistics
• High-dimensional Statistics
• Statistics for Stochastic Processes
• +3 more

Steffen Winter is a Lecturer (PD Dr.) at the Institute of Stochastics, Karlsruhe Institute of Technology (KIT). His research specializes in Fractal Geometry, Geometric Measure Theory, Stochastic Geometry, and Dynamical Systems. He leads the DFG-funded project Scaling of curvature measures and the modified Weyl-Berry conjecture and serves as Principal Investigator for project 12 ( Morphometric Roughness of Nanostructured Surfaces ) within the DFG Priority Programme 2265 (Random Geometric Systems). Winter's work explores the mathematical foundations of fractals, stochastic processes, and geometric measurements. Key themes include Minkowski content, curvature measures, self-similar sets, percolation models, and applications to materials science and geoscience. Recent publications emphasize fractal dimensionality, surface roughness quantification, and stochastic convergence in complex systems. He teaches advanced courses including Stochastic Geometry , Fractal Geometry , and Markov Chains , and mentors students through seminars and proseminars. No awards or research grants besides DFG projects are documented.

• Fractal Geometry
• Geometric Measure Theory
• Stochastic Geometry
• +1 more

Jacob Gardner is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania, where he leads a research group focused on probabilistic machine learning. His work bridges theoretical foundations and practical applications, with emphasis on Bayesian optimization and generative modeling for scientific challenges like drug discovery and materials design. Education & Background He earned his Ph.D. in Computer Science from Cornell University under Kilian Weinberger, followed by postdoctoral work in Cornell's Operations Research department. Prior to joining Penn, he was a Research Scientist at Uber AI Labs. Research Focus His lab develops methods for: Bayesian optimization for high-dimensional scientific design problems Scalable Gaussian process inference Black-box variational inference with convergence guarantees Adversarial robustness in machine learning systems Publication Trends Recent work (2022-2023) demonstrates strong focus on efficient Bayesian methods: 80% of publications center on optimization techniques (local/global Bayesian optimization) and variational inference, with applications to latent space modeling and hyperparameter tuning. Papers consistently appear at NeurIPS, ICML, and AISTATS. Research Team Advises five PhD students: Natalie Maus (Bayesian optimization) Kaiwen Wu (optimization theory) Kyurae Kim (variational inference) Haydn Jones (structured data) Yimeng Zeng (generative modeling) Software Contributions Co-founded GPyTorch, a PyTorch-based framework for GPU-accelerated Gaussian processes that replaces traditional Cholesky decomposition with linear conjugate gradient methods.

• Probabilistic Machine Learning
• Generative Modeling
• Bayesian Optimization
• +1 more

Lisa Hartung is a Full Professor (W3) at Johannes Gutenberg University Mainz's Department of Mathematics since 2025, leading research in stochastic processes. She previously held Associate (2019-2025) and Assistant Professor (2019) positions at the same institution and served as a Courant Instructor at NYU's Courant Institute (2016-2018). Her educational background includes a PhD from Bonn University (2016) under Prof. Anton Bovier in Probability Theory. Key research interests span branching Brownian motion , Gaussian free fields , random energy models , and stochastic processes on evolving networks , with applications in statistical mechanics and mathematical physics. Her work frequently addresses extremal processes, phase transitions, and complex temperature phenomena. Analysis of her recent publications reveals dominant trends in log-correlated random fields, variable-speed branching processes, and hard-wall boundary conditions. Articles consistently explore asymptotic behavior, maximum processes, and structural properties of stochastic systems, with increasing interdisciplinary connections to computer science through data stream modeling. Scientific recognition includes: Förderpreis der Fachgruppe Stochastik (2018) Hartung secures substantial research funding as PI for multiple high-impact projects: the ANR-DFG grant Random Energy Models (2020), DFG's SPP 2265 Random Geometric Systems (2020), TRR146 Multiscale Simulation Methods (2020), and Carl-Zeiss project TOPML (2022). She also serves on the executive board of DMV Fachgruppe Stochastik (since 2023) and coordinates Jugend trainiert Mathematik for grades 8/9. Her research group participates in the DFG-funded network Stochastic Processes on Evolving Networks and collaborates internationally through workshops on interacting particle systems and stochastic population models.

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +3 more

Olga Mula is a researcher at Eindhoven University of Technology (TU Eindhoven) in the Netherlands specializing in optimal transport theory, Wasserstein spaces, and model reduction techniques for partial differential equations. Her work bridges theoretical mathematics with practical applications in state estimation and inverse problems. Her research interests focus on Optimal Transport , Wasserstein Spaces , Model Reduction , and Numerical Analysis of PDEs . She develops algorithms for state estimation in metric spaces, particularly focusing on the Wasserstein space of probability measures. Her work includes developing reduced models using barycentric approximation, analyzing convergence properties, and addressing challenges in sensor placement for optimal data acquisition. Her recent publications demonstrate significant contributions to understanding how to build efficient reduced models in Wasserstein spaces for both forward and inverse problems. She has developed theoretical frameworks for state estimation algorithms, analyzed their performance in terms of Kolmogorov widths, and created practical implementations using sparse Wasserstein barycenters. Her work spans pure mathematical theory to applications in image processing and PDE-constrained optimization. Her scientific contributions include: Development of piecewise-affine algorithms for state estimation Nonlinear model reduction on metric spaces for conservative PDEs Sparse approximation using Wasserstein barycenters Applications to shape reconstruction and line completion Theoretical analysis of approximation rates in Wasserstein spaces She collaborates extensively with leading researchers in the field including Cohen, Dahmen, Feydy, and Rai. Her work demonstrates both theoretical depth and practical relevance, connecting abstract mathematical concepts to real-world problems in data assimilation and inverse modeling.

• Optimal Transport
• Wasserstein Spaces
• Model Reduction
• +4 more

Elizabeth Qian is a tenure-track Assistant Professor at Georgia Tech with joint appointments in the Schools of Aerospace Engineering and Computational Science and Engineering. She holds a visiting Hans Fischer Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) since 2023. Her research develops computational methods for scientific machine learning, model reduction, and multifidelity approaches to accelerate engineering decision-making. Education : PhD (2021), SM (2017), and SB (2014) in Aerospace Engineering from MIT Her work focuses on enabling efficient simulations for complex systems through model reduction , scientific machine learning , and multifidelity methods . Recent publications address Bayesian inverse problems, neural operator learning trade-offs, and ensemble Kalman methods. Scientific awards include the 2025 NSF CAREER Award, 2024 AFOSR YIP Award, 2023 TUM-IAS Fellowship, 2022 SIAM Student Paper Prize, and 2014 Hertz/NSF/Fulbright fellowships. She mentors GT PhD researchers and contributes to diversity initiatives in engineering communities.

• Scientific machine learning
• Model reduction
• Multifidelity methods
• +5 more