Multi-Marginal Optimal Transport Professors and Supervisors in Germany

Andreas Rietbrockمشاهده پروفایل

استاد

Karlsruhe Institute of TechnologyGermany

Andreas Rietbrock is Professor and Director of the Geophysical Institute (GPI) at the Karlsruhe Institute of Technology (KIT) , Germany, where he also serves as Dean of Studies for Geophysics . He is a leading expert in earthquake seismology, seismic tomography, and subduction zone dynamics, with a strong focus on integrating advanced observational techniques and computational methods. Education: While specific degrees are not listed in the provided text, his extensive publication record and leadership roles indicate advanced academic training in geophysics and seismology. Research Interests: His work spans a wide range of topics including: Seismic imaging of subduction zones (e.g., Nazca, Lesser Antilles) Earthquake rupture dynamics and fault mechanics Volcanic seismology and magma transport Full waveform inversion and AI-enhanced seismic analysis Distributed Acoustic Sensing (DAS) applications Induced seismicity and reservoir monitoring Research Trends: His recent publications (2022–2025) emphasize the use of dense seismic arrays, AI-based data processing, and multi-method tomography to study complex tectonic environments. Key themes include high-resolution imaging of slab structures, fluid migration in subduction zones, and the integration of DAS and machine learning for seismic monitoring. Scientific Contributions: Andreas has led major international projects such as the ANTICS Large-N deployment in Albania and the VoiLA project in the Lesser Antilles. He has published extensively in top-tier journals like Nature , Geophysical Research Letters , and Journal of Geophysical Research , with over 200 peer-reviewed articles. Teaching and Supervision: He teaches courses such as "Introduction to Geophysics II", "Seismology", and "Current Topics in Seismology and Risk". While specific student names are not listed, his role as Dean and principal investigator on numerous projects indicates active supervision of graduate students and postdocs. Labs and Teams: He leads the seismology group at GPI, coordinating large-scale deployments of seismic instruments, including ocean-bottom seismometers and fiber-optic DAS systems. His team collaborates globally with institutions in Europe, South America, and Asia.

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Marco Cuturiمشاهده پروفایل

استاد

Weierstrass Institute for Applied Analysis and StochasticsGermany

Marco Cuturi is a Research Scientist at Apple ML Research in Paris and Professor of Statistics at CREST-ENSAE, Institut Polytechnique de Paris. His work bridges machine learning , optimal transport , and optimization , with applications in time-series analysis , kernels , and multiresolution methods . He has held academic roles at Kyoto University and Princeton University, and previously worked in the financial industry. Research Interests: Optimal transport theory and computational methods Kernel design for structured data and histograms Time-series alignment and soft-DTW Entropic regularization in optimization Applications to computer vision and genomics Teaching: Cuturi has taught courses on linear optimization at Princeton, geometric methods in machine learning at Kyoto, and scientific English. He has also organized machine learning summer schools in Kyoto, Les Houches, and other international venues. Recent Trends: His 2024-2025 publications focus on entropic optimal transport solvers, disentangled representation learning via Gromov-Monge gaps, and applications to text-to-image diffusion models. Collaborative work with institutions like Google Research, MIT, and University of Tokyo highlights his interdisciplinary impact.

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Lorenz Dörschelمشاهده پروفایل

استاد مدعو

RWTH Aachen UniversityGermany

Lorenz Dörschel is an Adjunct Professor (Lehrbeauftragter) at the Institute of Automatic Control at RWTH Aachen University. He holds the academic title PD Dr.-Ing. habil, signifying post-doctoral research qualifications. His position is part-time, focusing on advanced control theory and applications. His primary research interests include: Control of distributed parameter systems (e.g., fluid dynamics, thermal processes) Model predictive control for industrial and automotive systems Parameter space methods for robust controller design Model reduction techniques for complex nonlinear systems Dörschel's recent publications (2018-2024) demonstrate broad applications across biomedical engineering, renewable energy, automotive systems, and industrial automation. His work consistently integrates mathematical rigor with practical implementations, emphasizing advanced control methodologies like nonlinear MPC, Lyapunov-based design, and Bayesian optimization. A recurring theme is the development of computationally efficient control strategies for distributed parameter systems. No scientific awards, student advising relationships, or research grants are documented in the available information.

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Fangneng Zhanمشاهده پروفایل

پژوهشگر ارشد

Max Planck Institute for InformaticsGermany

Currently a Research Fellow at Harvard University & MIT , Fangneng Zhan specializes in Neural Rendering and Generative AI . His research focuses on developing evolutive rendering frameworks, 3D-aware generative models, and multimodal synthesis techniques. Previously, he was a postdoctoral researcher at the Max Planck Institute for Informatics under Prof. Christian Theobalt. He earned his Ph.D. in Computer Science & Engineering from Nanyang Technological University, Singapore and a Bachelor's in Communication Engineering from the University of Electronic Science and Technology of China . His work spans 3D reconstruction, robotics applications , and lighting estimation , with significant contributions to SIGGRAPH , NeurIPS , and CVPR conferences. Recent research highlights include evolutive gauge transformations for neural fields, generalizable 3D style transfer via Gaussian splatting, and multimodal synthesis frameworks leveraging pre-trained models like CLIP and Stable Diffusion. He has co-authored Top50 Popular Paper in TPAMI 2023 and organized workshops at CVPR 2024 on generative models. Scientific Awards: Top50 Popular Paper, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023 Collaborative Network: Mentored students at institutions like Harvard, NTU, and ETH Zurich. His projects include datasets for lighting estimation and real-time scene text detection systems.

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Martim Brandãoمشاهده پروفایل

استادیار

Saarland UniversityGermany

Martim Brandão is a Lecturer (Assistant Professor) in Robotics and Autonomous Systems at King’s College London, where he leads the Responsible Robotics and AI (RRAI) Lab and serves as Co-Director of the UKRI Centre for Doctoral Training in Safe and Trusted AI. His research focuses on ethical, explainable, and safe AI and robotics, with applications in human-robot interaction, motion planning, fairness, and societal impact. His research interests include: Explainable AI and Motion Planning Fairness and Bias in AI Systems Human-Robot Interaction and Social Robotics Adversarial Robustness in Robotics Value Alignment and Ethical AI Inclusive and Participatory Robotics Design His recent publications (2023–2025) reflect a strong trend toward socially responsible robotics, focusing on fairness in navigation, explainability of planning failures, worker-centered agricultural robotics, environmental justice in drone delivery, and the dangers of bias in drowsiness detection and LLM-driven robots. His work emphasizes user understanding, societal impact, and ethical safeguards in autonomous systems. He has advised and collaborated with numerous students and researchers across diverse topics in robotics and AI. He is actively involved in shaping responsible robotics through: Leadership in the RRAI Lab Co-directing a national CDT in Safe and Trusted AI Developing fairness-aware algorithms Advocating for inclusive and ethical design practices His lab and research group focus on: Responsible Robotics and AI Explainability in Multi-Agent Planning Fairness in Coverage and Navigation Human-Centered Evaluation of AI Systems

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Martin Huesmannمشاهده پروفایل

استاد

University of MünsterGermany

Prof. Dr. Martin Huesmann is a full Professor at the Institute for Mathematical Stochastics, Department of Mathematics and Computer Science, University of Münster, and a key investigator in the Excellence Cluster Mathematics Münster. He leads an active research group focusing on stochastic analysis, optimal transport, random measures, and robust finance. Research Interests: Stochastic analysis Optimal transport (classical, martingale, causal) Random measures and point processes Skorokhod embedding and its geometric variants Matching and allocation problems Robust and model-independent finance Gradient flows and asymptotic analysis of random structures His recent publications reveal a strong trend in probabilistic approaches to optimal transport, particularly in martingale and multi-marginal settings, with deep connections to financial mathematics and geometric analysis of stochastic processes. His work often involves collaboration with leading researchers such as Mathias Beiglböck, Alex Cox, and Felix Otto. Scientific Contributions: Investigator in EXC 2044 - C1: Evolution and asymptotics, developing gradient flow descriptions for diffusion-reaction systems. Research on asymptotics of random polytopes and random trees. Development of transport distances for stationary random measures. He actively advises PhD and master’s students, including Bastian Müller, Jonas Jalowy, and Hanna Stange. He teaches advanced courses such as Optimal Transport , Stochastic Analysis , and Probability Theory , and supervises numerous master’s and bachelor’s theses. His research group includes current members Bastian Müller, Hanna Stange, and Jonas Jalowy, and former members Francesco Mattesini and Martin Brückerhoff, reflecting sustained research activity and mentorship.

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Andreas Brotzerمشاهده پروفایل

پژوهشگر

Ludwig Maximilian University of MunichGermany

Dr. Andreas Brotzer is affiliated with the Department of Earth and Environmental Sciences at Ludwig Maximilian University Munich, working at the Geophysical Observatory. He currently holds a research position and is conducting a research stay at the University of British Columbia, Canada. His primary research focuses on rotational ground motions, seismology, and geophysical instrumentation, particularly involving ring laser technology (ROMY project) and multi-component seismic observations. His work addresses challenges like atmospheric pressure interference, sediment depth mapping, and high-resolution earth sensing. Research interests include: 1) Advanced rotational motion measurement systems, 2) Integration of fiber optic and ring laser technologies for geophysical monitoring, 3) Seismic array analysis for crustal structure imaging, and 4) Development of low-noise models for ground motion analysis. He has contributed to projects like the Pinon Flats Observatory in Southern California and Greenland's continental margin studies. Recent work emphasizes vehicular source tracking, length-of-day variations observed via ring lasers, and noise characterization in rotational measurements. His posters highlight operational aspects of large ring lasers and seismic station implementations. Professional profiles are available on ResearchGate and LinkedIn.

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Gero Frieseckeمشاهده پروفایل

استاد

Technical University of MunichGermany

Electronic structure of atoms and molecules
Calculus of variations
Molecular dynamics
+۳ مورد دیگر

Prof. Gero Friesecke is a Professor of Mathematics at the Technical University of Munich (TUM), leading the Chair of Analysis within the Department of Mathematics. He is affiliated with the TUM School of Computation, Information and Technology. His research focuses on electronic structure theory, calculus of variations, molecular dynamics, multiscale methods, and optimal transport. He has held academic positions at institutions like the University of Oxford and the University of Warwick before joining TUM in 2004. Education includes a PhD from Heriot-Watt University (1993) and postdoctoral work at Carnegie Mellon University, Universität Freiburg, and ETH Zurich. Notable awards include the Whitehead Prize (1999) and the Oberwolfach Prize (1995). His work spans theoretical and applied mathematics, with contributions to quantum chemistry, materials science, and optimal transport algorithms. He co-edits journals like the Archive for Rational Mechanics and Analysis and serves on scientific advisory boards, including the Max Planck Institute for Mathematics in the Sciences. Recent research emphasizes optimal transport applications in data science and quantum chemistry, with publications on topics like Wasserstein barycenters and self-assembly processes. His leadership roles include Dean of the Faculty of Mathematics at TUM (2012-2015) and co-founding the GAMM activity group on molecular systems modeling.

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Jakob von Saldernمشاهده پروفایل

پژوهشگر

Technical University of Berlin (TU Berlin)Germany

Jakob von Saldern is a Scientific Employee at the Technical University of Berlin within the Faculty of Transportation and Mechanical Systems , specifically the Institute of Fluid Mechanics and Technical Acoustics . His research focuses on thermoacoustic hydrogen burner design and physics-informed neural network applications for turbulent flow modeling. PhD candidate since 2019 Specializes in climate-neutral combustion technologies His work combines fluid mechanics and machine learning through: Development of mean field consistent closure models Research on hydrogen burner dynamics with MAN Energy Solutions Innovations in data assimilation methods for turbulent flows Recent publications demonstrate expertise in: Thermoacoustic instability analysis Reinforcement learning-augmented turbulence models Acoustic communication in can-annular combustors

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Camilla Brizziمشاهده پروفایل

پژوهشگر

Technical University of MunichGermany

Camilla Brizzi is a Researcher at the Technical University of Munich (TUM), affiliated with the Chair of Global Analysis under Prof. Friesecke. She completed her PhD through a co-tutorship program between the University of Florence and the University of Paris Dauphine. Her research focuses on optimal transport theory, calculus of variations, and related areas such as Wasserstein barycenters, multi-marginal problems, and entropic methods. She has contributed to advancements in non-convex cost optimization and supremal variational problems. Her academic journey includes studies at the University of Florence, followed by doctoral research combining European institutions. She collaborates with leading figures in the field, including Gero Friesecke and Luigi De Pascale, and her work bridges theoretical analysis with computational methods. Her publications span high-impact journals like Nonlinear Analysis and Applied Mathematics and Optimization , addressing both foundational and applied aspects of optimal transport. Brizzi’s research interests also extend to the application of optimal transport in solving complex mathematical problems, such as those arising in physics and data science. She actively contributes to the academic community through seminars, conferences, and interdisciplinary projects, reflecting her commitment to advancing mathematical foundations and their real-world implications.

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Robert Beinertمشاهده پروفایل

مدرس ارشد

Technical University of Berlin (TU Berlin)Germany

Robert Beinert is a Senior Lecturer (Privatdozent) in Applied Mathematics at the Technische Universität Berlin, affiliated with the Department of Applied Mathematics within the Faculty of Mathematics and Natural Sciences. He holds a Dr. rer. nat. (Ph.D.) from Georg-August-Universität Göttingen (2015) and completed his Habilitation at Technische Universität Berlin in 2025. His research focuses on inverse problems, optimal transport, phase retrieval, and mathematical imaging, with applications in signal processing and data analysis. He has held academic positions including Research Associate roles at TU Berlin (2020–2025), Karl-Franzens-Universität Graz (2016–2020), and Georg-August-Universität Göttingen (2016). His work bridges theoretical foundations and practical applications, with contributions to regularization techniques, optimal transport theory, and algorithmic development. Key research interests include phase retrieval uniqueness analysis, Gromov-Wasserstein transport, and denoising methodologies for manifold-valued data. His recent publications emphasize advancements in optimal transport frameworks, regularization strategies, and applications in image processing and machine learning.

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Florian Beierمشاهده پروفایل

پژوهشگر

Technical University of Berlin (TU Berlin)Germany

Florian Beier is a Researcher at the Institute of Mathematics, Technical University of Berlin, specializing in Applied Mathematics. Since October 2021, he has served as a research assistant pursuing his Dr. rer. nat. His academic journey includes a Master of Science (2021) and Bachelor of Science (2018), both in Mathematics from TU Berlin, with thesis topics focusing on Optimal Transport and Percolation Theory. Research interests revolve around Optimal Transport theory, particularly multi-marginal and Gromov-Wasserstein formulations, with applications to mathematical imaging and computational geometry. His work bridges theoretical advancements and practical implementations, addressing challenges in transportation barycenters, entropic regularization, and algorithmic design for complex data analysis. Notable achievements include the Best Poster Award at the 2022 International Conference on Curves and Surfaces and recognition for academic excellence at TU Berlin's Dies Mathematicus. He actively contributes to conferences and workshops, presenting groundbreaking research on transport operators and barycenter computations. Current projects involve developing fixpoint iteration methods for Gromov-Wasserstein barycenters and exploring transport kernels for unpaired data analysis. His collaborations span institutions like the University of Tübingen and KTH Royal Institute of Technology, emphasizing interdisciplinary approaches to transport problems.

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