Find Academic Supervisors

Jonas Fischer is the head of the Explainable Machine Learning group at the Max Planck Institute for Informatics, Department of Computer Vision and Machine Learning. His research focuses on interpreting complex machine learning models, particularly in genomics and healthcare, aiming to enhance robustness and alignment with human decision-making. Prior to his role at MPI, he was a postdoctoral fellow at Harvard University's Department of Biostatistics, where he worked on interpretable models for gene regulatory systems in cancer. Education: PhD in Computer Science from Saarland University (2022), with a thesis titled More than the sum of its parts , exploring the intersection of pattern mining and deep learning. He has contributed to advancing methods in neural network pruning, federated learning, and low-dimensional embeddings (e.g., dtSNE, Mercat). His work bridges computational biology, data mining, and machine learning, with applications in DNA methylation analysis, graph-based differential networks, and biomedical informatics. Key research areas include: (1) Explainable AI and neural network interpretability, (2) Biomedical applications of machine learning (e.g., gene regulatory networks, cancer genomics), (3) Low-dimensional embeddings and visualization techniques, (4) Federated learning for privacy-preserving collaborative models, and (5) Pattern mining for error analysis in NLP and classification tasks. Publications span top venues like NeurIPS, ICLR, Bioinformatics, and Genome Biology. His group develops tools such as BONOBO for omics data integration and node2vec2rank for scalable graph analysis. He actively collaborates with biomedical researchers to address challenges in data-driven healthcare and precision medicine.

• Machine Learning
• Explainable AI
• Neural Networks
• +3 more

Jan von Delft is a Professor (chair) at Ludwig-Maximilians-University (LMU) Munich, working in the Faculty of Physics within the Chair of Theoretical Solid State Physics. His research group consists of postdocs, PhD students, and master's students working on various aspects of strongly correlated electron systems, with physical space located at Theresienstr. 37 (Room A420) in Munich. von Delft's research focuses on correlated electron and spin systems, with particular interest in dynamical and transport properties, quantum impurity models, Hund metals, unconventional superconductors, quantum magnets, and quantum criticality. His methodological expertise includes many-body field theory, parquet formalism (FRG), DMFT, and tensor networks (NRG, DMRG, PEPS, XTRG, etc.). His work bridges theoretical concepts with computational approaches to understand complex quantum phenomena in condensed matter systems. He has developed a distinctive emphasis on real-frequency calculations and numerical methods for studying quantum critical phenomena. Analysis of von Delft's recent publications reveals a strong focus on developing and applying advanced computational methods to study strongly correlated electron systems. His group has made significant contributions to numerical renormalization group techniques, tensor network methods, and the parquet formalism for calculating real-frequency correlation functions. His research shows increasing sophistication in handling quantum criticality, particularly in heavy-fermion systems, and exploring unconventional superconductivity mechanisms. Notably, his group has developed specialized computational libraries like KeldyshQFT to make these advanced methods more accessible to the broader physics community. von Delft actively mentors a substantial research group consisting of one postdoc (Markus Scheb), eleven PhD students (Anxiang Ge, Sasha Kovalska, Mathias Pelz, Marc Ritter, Nepomuk Ritz, Changkai Zhang, Markus Frankenbacher, Felipe Picoli, Simone Fodera, Ming Huang), and two master's students (Ester Pages, Gianluca Grosso). His detailed Style Guide for scientific communication demonstrates his commitment to high-quality research presentation. The group appears well-funded with ongoing research activities spanning theoretical development, computational implementation, and physical interpretation of complex quantum phenomena.

• Correlated electron systems
• Quantum impurity models
• Quantum criticality
• +7 more

Peter Benner is a Professor and Director at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, where he leads the Computational Methods in Systems and Control Theory group. He also holds an Honorarprofessor position for Mathematics at Otto-von-Guericke Universität Magdeburg since 2011. Benner has previously served as Managing Director of the Max Planck Institute during multiple periods (2013-2014, 2021-2022, and 2025-2026), demonstrating his leadership in the field. Benner's research focuses on Scientific Machine Learning, Numerical Linear and Multilinear Algebra, Model Order Reduction and Reduced-order Modeling, Numerical Methods in Systems and Control Theory, PDE Constrained Optimization, High-performance and Power-aware Computing, and Mathematical Software development. His work bridges theoretical mathematics with practical engineering applications, particularly addressing challenges in large-scale dynamical systems. Analysis of his recent publications reveals a strong emphasis on developing efficient computational methods for complex systems. Benner has pioneered approaches combining model order reduction with tensor methods to tackle high-dimensional problems in uncertainty quantification and PDE-constrained optimization. His work shows a consistent trend toward integrating data-driven techniques with traditional model-based approaches, particularly for nonlinear and parametric systems. Throughout his career, Benner has actively mentored students and collaborated with researchers worldwide, delivering numerous invited talks at prestigious institutions and conferences across Europe, North America, and Asia. His research has received significant funding, supporting the development of mathematical software and computational methods for industrial applications. Benner leads the Computational Methods in Systems and Control Theory department at the Max Planck Institute, which focuses on developing and implementing advanced numerical methods for large-scale dynamical systems. The group maintains strong connections with both theoretical mathematics and practical engineering applications, particularly in fluid dynamics, energy systems, and control theory.

• Scientific Machine Learning
• Numerical Linear Algebra
• Numerical Multilinear Algebra
• +7 more

Vladimir Spokoiny is a Professor at the Departments of Mathematics and Economics of the Humboldt University of Berlin and Head of the Research Group "Stochastic Algorithms and Nonparametric Statistics" at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany. His research spans multiple areas of statistics, machine learning, and financial mathematics, with significant contributions to nonparametric statistics, high-dimensional data analysis, and statistical methods in finance. Spokoiny received his M.Sc. in applied mathematics from the Moscow Institute of Railway Engineering in 1981 and his Ph.D. in mathematics from Lomonosov Moscow State University in 1988. He completed his Habilitation at Humboldt University in 1996. His academic career includes positions at the All-Union Institute of Railway Transport in Moscow, the Institute for Information Transmission Problems in Moscow, and the Institute for Applied Analysis and Statistics in Berlin before joining the Weierstrass Institute and Humboldt University where he has been a professor since 2002. Spokoiny's research focuses on adaptive nonparametric smoothing and hypothesis testing, high dimensional data analysis, statistical methods in finance, image analysis with applications to medicine, classification, and nonlinear time series. His work often addresses the challenges of nonstationarity in time series data and develops innovative methods for volatility estimation and risk management. He has made significant contributions to the development of adaptive weights smoothing procedures, which have applications in image processing, community detection, and manifold learning. His recent work has expanded into high-dimensional statistics, Bayesian inference, and optimization methods for machine learning, with publications demonstrating novel approaches to Gaussian approximation, Laplace methods, and statistical inference in non-Euclidean spaces. Spokoiny has supervised numerous PhD students including Oliver Reiss, Danilo Mercurio, Ying Chen, Elmar Diederichs, and Mstislav Elagin, whose research has focused on mathematical finance, time series analysis, and statistical methods. He serves as an Associate Editor for The Annals of Statistics (since 2004) and Statistics and Decisions (since 2002), and has previously served on the editorial board of the Journal of Statistical Planning and Inference. His professional activities include reviewing for major statistical journals including Annals of Statistics, Bernoulli, Econometrica, and Journal of American Statistical Association, as well as reviewing grant proposals for the National Science Foundation (USA), German Research Foundation, and Netherlands Organisation for Scientific Research. Spokoiny is a member of several professional societies including the International Statistical Institute, American Statistical Association, Institute of Mathematical Statistics, and Bernoulli Society. He is fluent in Russian (mother tongue), English, and German, and has good knowledge of French. His research group at WIAS focuses on developing novel statistical methodologies with applications across various scientific domains, particularly emphasizing adaptivity and robustness in complex data environments. The group's work has significant implications for financial risk management, medical imaging, and machine learning applications, with recent publications addressing fundamental questions in high-dimensional statistics and nonparametric inference.

• Nonparametric Statistics
• High Dimensional Data Analysis
• Statistical Methods in Finance
• +7 more

Prof. Mathias Drton holds the Chair of Mathematical Statistics at the Technical University of Munich (TUM), within the Department of Mathematics and School of Computation, Information and Technology. His research focuses on graphical models, algebraic statistics, causal inference, and multivariate data analysis. He has authored numerous publications in top-tier journals and conferences, including work on conditional independence, sparse factor analysis, and causal discovery in linear models. Drton has supervised a large number of theses, mentoring students in areas like high-dimensional statistics, graphical models, and causal inference. He is actively involved in teaching advanced courses such as 'Graphical Models in Statistics' and 'Fundamentals of Mathematical Statistics.' His academic contributions span theoretical developments in statistical methodology and computational tools, including R packages like SEMID and symRC . Drton collaborates internationally, contributing to projects like the TUM-ICL Mathematical Sciences Hub and Exzellenzcluster MCQST. His work bridges algebraic methods with statistical challenges, addressing identifiability in latent variable models and robust graphical modeling under non-Gaussian assumptions. Recent research emphasizes causal structure learning under partial homoscedasticity, distribution-free independence tests, and multi-domain causal representation learning. Drton’s lab actively explores applications in genomics, epidemiology, and machine learning, leveraging both theoretical rigor and practical computational methods.

• Graphical models
• Algebraic statistics
• Causal inference
• +3 more

Joachim Weickert is a Professor of Mathematics and Computer Science at Saarland University where he heads the Mathematical Image Analysis Group since 2001. He received his diploma and Ph.D. in mathematics from the University of Kaiserslautern (1991, 1996), and a habilitation degree in computer science from the University of Mannheim (2001). Prior to his current position, he worked as a research assistant at the University of Kaiserslautern, as a post-doctoral researcher at the universities of Utrecht and Copenhagen, and as an assistant professor at the University of Mannheim. His research focuses on image processing, computer vision, and scientific computing, with special emphasis on techniques based on partial differential equations, variational principles, wavelets, morphological and nonlocal methods, as well as neuroexplicit approaches. He has developed mathematical models and efficient numerical algorithms for image restoration, enhancement, segmentation, compression, optic flow computation, stereo reconstruction, shape from shading, and signal processing methods for tensor fields. These ideas have been successfully applied in industry, biomedical image analysis, and other fields. Analysis of his recent publications reveals a strong trend toward combining traditional PDE-based methods with modern deep learning approaches, particularly in the areas of image inpainting and compression. His work increasingly explores the connections between numerical algorithms for partial differential equations and neural network architectures, demonstrating how mathematical foundations can inform cutting-edge AI techniques while maintaining strong theoretical guarantees. Gottfried Wilhelm Leibniz Prize (2010), considered the most important research award in Germany ERC Advanced Grant (2017) for "Inpainting-based Compression of Visual Data" Elected member of Academia Europaea - The Academy of Europe Jan Koenderink Prize for Fundamental Contributions in Computer Vision (2014) Multiple DAGM Prizes and Best Paper Awards throughout his career AAIA Fellow (2021) and Highly Ranked Scholar (2024) distinctions Professor Weickert has supervised over 250 bachelor's and master's theses and initiated the Master Programme in Visual Computing at Saarland University, the first of its kind in Germany taught in English. He has established numerous interdisciplinary collaborations with colleagues from medicine, bioinformatics, pharmacy, physics, mechatronics, and mechanical engineering. As Principal Investigator for Visual Computing within the Multimodal Computing and Interaction Cluster of Excellence, and former dean of the Faculty of Mathematics and Computer Science (2008-2010), he has played a significant leadership role in advancing visual computing research and education. He heads the Mathematical Image Analysis Group, which has been at the forefront of developing mathematical methods for image analysis. The group maintains strong connections with both theoretical mathematics and practical applications, bridging the gap between fundamental research and real-world implementation across various domains including medical imaging, industrial inspection, and multimedia processing.

• Image Processing
• Computer Vision
• Mathematical Modeling
• +4 more

Prof. Peter Müller-Buschbaum is a Full Professor and Head of the Chair of Functional Materials at the Physics Department of the Technical University of Munich (TUM). He has held this position since April 2018 and also served as Scientific Director of the Research Neutron Source Heinz Maier-Leibnitz (FRM-II) and the Heinz Maier-Leibnitz Center (MLZ) from 2018 to 2023. His leadership extends to multiple roles including Core Member of the Integrated Research Institute Munich Institute of Integrated Materials, Energy and Process Engineering (MEP) since 2021, and Head of the Renewable Energies Network (NRG) at MEP. Full Professor (W3), Head of the Chair of Functional Materials at TUM School of Natural Sciences (since 04/2018) Deputy Editor of "ACS Applied Materials & Interfaces" (since 01/2024) Supervising Professor "Electronics Laboratory" at TUM School of Natural Sciences (since 11/2023) Member of TUM Sustainability Board (since 05/2023) Core Member of MEP Institute (since 10/2021) Head of Renewable Energies Network at MEP (since 10/2021) Prof. Müller-Buschbaum's research spans energy materials for photovoltaics and battery technologies, smart responsive materials that adapt to environmental stimuli, and nanocomposite materials with tailored properties. His group employs advanced scattering techniques to characterize materials at the nanoscale, providing insights into structure-property relationships critical for developing next-generation energy technologies. His extensive publication record demonstrates particular expertise in perovskite solar cells, lithium-ion battery technologies, and polymer-based functional materials, with recent work focusing on improving device stability and efficiency while understanding fundamental degradation mechanisms. His publications reveal a strong emphasis on energy conversion and storage technologies, with particular attention to interfacial engineering in both photovoltaic and battery systems. The research shows sophisticated integration of materials synthesis, advanced characterization, and device engineering to address critical challenges in renewable energy technologies. His work bridges fundamental science with practical applications through collaborations with major international research facilities. Scientific Service and Recognition Member of the Council of the Cluster of Excellence "ORIGINS" (since 01/2019) Spokesperson of the Chemical Physics and Polymer Physics Association of DPG (03/2021-10/2022) Member of the European Spallation Source Scientific Advisory Panel (since 03/2011) German representative at the European Polymer Federation for polymer physics (since 03/2011) Chairman of the Keylab "TUM.solar" in the Bavarian research project "Solar Technologies Go Hybrid" (since 03/2012) Prof. Müller-Buschbaum actively contributes to academic community through editorial work, having served as Associate Editor (2012-2022), Executive Editor (2023), and currently Deputy Editor (2024-present) of "ACS Applied Materials & Interfaces". He maintains strong international collaborations with synchrotron and neutron facilities worldwide, reflecting his expertise in advanced materials characterization techniques essential for cutting-edge materials research.

• Materials Science
• Polymer Physics
• Energy Materials
• +6 more

Bruce Allen is the Director of the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Hannover, Germany, where he also heads the Observational Relativity and Cosmology department. He holds dual academic appointments as Honorary Professor of Physics at Leibniz Universität Hannover and Adjunct Professor of Physics at the University of Wisconsin-Milwaukee, USA. His career spans over three decades in gravitational physics research, with a leadership role in the LIGO Scientific Collaboration from 1997 to 2018. Dr. Allen's research focuses on gravitational wave detection and data analysis, early universe cosmology, de Sitter space, curved-space quantum field theory, cosmic strings, inflationary models of the early universe, and gravitational radiation emission by cosmic strings. His work extends to large-scale cluster computing and public distributed computing projects like Einstein@Home, which has led to significant discoveries in gravitational wave astronomy. His recent publications demonstrate expertise in pulsar timing arrays, Hellings-Downs correlation analysis, and optimization of computational methods for gravitational wave detection. Allen's scientific contributions have been recognized with numerous prestigious awards including the Richard A. Isaacson Award (2020), the Bruno Rossi Prize (2017), the Princess of Asturias Award (2017), and the Special Breakthrough Prize (2016), all shared with the LIGO team for groundbreaking gravitational wave discoveries. He is also an Elected Fellow of both the American Physical Society and the Institute of Physics, UK. As a research leader, Allen has secured approximately $10 million in research funding from the National Science Foundation (1987-2018) and has mentored numerous students and researchers in gravitational physics. His work on Einstein@Home has engaged the public in scientific discovery through distributed computing, leading to several important astrophysical findings including gamma-ray pulsar discoveries.

• Gravitational wave detection
• Gravitational wave data analysis
• Early universe cosmology
• +6 more

Prof. Dr. Martin Burger is a leading scientist at DESY and a Full Professor in the Department of Mathematics at Universität Hamburg, where he leads the Computational Imaging Group. His research bridges applied mathematics, imaging sciences, and machine learning, with a focus on inverse problems, mathematical modeling, and partial differential equations. He has held professorial positions at Universität Münster and FAU Erlangen-Nürnberg prior to his current dual appointment. Full Professor, Universität Hamburg (2023–present) Leading Scientist, DESY, Hamburg (2023–present) Full Professor, FAU Erlangen-Nürnberg (2018–2023) Full Professor, Universität Münster (2006–2018) His research interests include inverse problems, variational regularization, optimal transport, kinetic models, and mathematical modeling in biology and social sciences. He has made significant contributions to imaging reconstruction, sparse neural networks, and the analysis of transformer architectures. His work often integrates theoretical analysis with computational methods, influencing both pure and applied mathematics. The most recent articles reflect a strong trend toward interdisciplinary applications, combining deep learning with PDE-based modeling, analyzing social and biological systems via kinetic and mean-field models, and advancing mathematical imaging through graph-based and optimal transport methods. His publications span high-impact venues in applied mathematics and computational science. Calderon Prize, Inverse Problems International Association (IPIA) ERC Consolidator Grant (2014) Invited speaker at ECM (2021), ICM (2022), and ICIAM (2023) Editor-in-Chief, European Journal of Applied Mathematics (since 2017) Prof. Burger has supervised numerous PhD students and postdoctoral researchers, many of whom appear as co-authors in his publications. His research is supported by major grants, including funding from the German Federal Ministry of Education and Research (BMBF). He is actively involved in collaborative projects across mathematics, physics, and engineering disciplines. He leads the Computational Imaging Group at DESY, fostering a collaborative environment for developing novel mathematical tools in imaging science. The group works on both theoretical foundations and practical implementations, contributing to advancements in tomography, machine learning, and data analysis.

• Inverse Problems
• Applied Mathematics
• Mathematical Modelling
• +15 more

Prof. Felix Krahmer is a TUM Tenure Track Assistant Professor of Optimization and Data Analysis at the Department of Mathematics, Technische Universität München (TUM), part of the School of Computation, Information and Technology. Previously, he held a Junior Professorship (W1) for Mathematical Data Analysis at the University of Göttingen (2012–2015). His research focuses on mathematical foundations of data science, including compressed sensing, signal quantization, uncertainty quantification, and optimization algorithms. He has led an Emmy Noether research group and contributed to the ZeMat interdisciplinary project. Education : PhD in Mathematics (2009), New York University, advisors: Percy Deift & Sinan Güntürk MSc in Mathematics (2007), New York University BSc in Mathematics (2004), Jacobs University Bremen Research Interests : Krahmer’s work bridges theoretical mathematics and applied data science. Key areas include compressed sensing algorithms, high-dimensional signal reconstruction, quantization theory for analog-to-digital conversion, and optimization methods for inverse problems. He also explores applications in imaging (e.g., MRI reconstruction) and stochastic processes. Key Contributions : His research on phase retrieval, sigma-delta quantization, and compressed sensing recovery has advanced signal processing techniques. Recent efforts focus on uncertainty quantification for high-dimensional inverse problems and the mathematical analysis of consensus-based optimization. Grants & Awards : Emmy Noether Research Group Grant (DFG) Funding from BMBF (ZeMat project) Teaching & Outreach : Krahmer has taught advanced courses on compressed sensing, functional analysis, and random matrix theory. He co-organized workshops on probabilistic techniques and clinical risk prediction, emphasizing interdisciplinary collaboration. Labs/Teams : Member of the TUM Data Science Research Group, focusing on optimization, imaging, and uncertainty quantification. Active in the Munich Center for Quantum Science and Technology (MCQST).

• Compressed Sensing
• Signal Processing
• Optimization
• +4 more

Prof. Dr.-Ing. Stephan Staudacher is the Director of the Institute of Aircraft Propulsion at the University of Stuttgart. His work focuses on aircraft propulsion systems, gas turbine performance, and turbomachinery design. He holds a professorship in the Faculty of Mechanical Engineering and Aerospace, leading research in advanced engine technologies, erosion effects, and fault detection algorithms. Research interests include engine reliability, computational fluid dynamics (CFD), and experimental validation of propulsion systems. His publications emphasize topics like neural network applications for fault detection, ice crystal icing simulations, and particle transport in additive manufacturing processes. Recent studies highlight the optimization of composite-cycle engines and assessment of mission severity caused by erosion. Key contributions include advancements in engine condition monitoring, transient performance analysis, and the development of Stuttgart University’s Altitude Test Facility (ATF). His work bridges theoretical models with industrial applications, addressing challenges in both civil and military aviation propulsion systems.

• Aircraft Propulsion
• Gas Turbine Engineering
• Thermodynamics
• +3 more

David W. Hogg is Professor of Physics and Data Science in the Center for Cosmology and Particle Physics in the Department of Physics at New York University. He serves as Senior Research Scientist in the Astronomical Data Group in the Center for Computational Astrophysics of the Flatiron Institute and maintains an affiliation with the Max-Planck-Institut für Astronomie in Heidelberg. His primary research focuses on observational cosmology, particularly approaches that use galaxies to infer physical properties of the Universe. He also conducts significant research on stellar kinematics in the Milky Way and the measurement and discovery of exoplanets. Across all domains, Hogg develops engineering systems and statistical methodologies that enable large-scale astrophysical projects for both his research group and the broader community. Recent work demonstrates expertise in robust statistical methods, particularly dimensionality reduction techniques like Robust-HMF. His research bridges theoretical statistics with practical applications in major astronomical surveys including Gaia, SDSS-V, and SPHEREx. He frequently explores connections between Bayesian and frequentist approaches to astronomical data analysis, with recent work on nuisance parameter integration, anomaly detection, and robust matrix factorization. Research supported by NYU, NASA, NSF, Moore Foundation, Sloan Foundation Additional support from Max Planck Society, Humboldt Foundation, ERC, Simons Foundation Hogg is actively involved in major astronomical projects including Astrometry.net, Gaia, and SDSS, with long-term comprehensive goals of analyzing all galaxies, stars, and astronomical images. His work emphasizes open science principles, reproducible research practices, and the development of publicly accessible tools for the astronomical community.

• Observational cosmology
• Galactic astronomy
• Exoplanet science
• +5 more

Yen-Chi Chen is an Associate Professor in the Department of Statistics at the University of Washington. He also holds positions as a Data Science Fellow at the UW eScience Institute and as a co-investigator and statistician at the National Alzheimer's Coordinating Center. His academic career spans multiple interdisciplinary fields including statistics, data science, and astrostatistics. Chen's educational background includes a Ph.D. from Carnegie Mellon University, where he received prestigious awards including the Umesh K. Gavasakar Thesis Award (2017) and the William S. Dietrich II Presidential Ph.D. Fellowship Award (2015). His research focuses on nonparametric statistics, topological data analysis, missing data methodologies, cluster analysis, manifold learning, and applications in large-scale structure analysis and astrostatistics. Chen has made significant contributions to the development of statistical methods for analyzing cosmic web structures, GPS data, and causal inference with continuous treatments. His work bridges theoretical statistics with practical applications in astronomy, neuroscience, and public health. Analysis of his recent publications reveals a strong emphasis on developing novel statistical frameworks for complex data structures, particularly focusing on density-based methods, manifold learning, and approaches that address challenges in missing data and causal inference without standard assumptions. ASA Noether Early Career Scholar Award, American Statistical Association (2022) CAREER Award, National Science Foundation (2022-2027) Umesh K. Gavasakar Thesis Award, Carnegie Mellon University (2017) William S. Dietrich II Presidential Ph.D. Fellowship Award, Carnegie Mellon University (2015) Chen has advised numerous graduate students across multiple publications, with a focus on developing new statistical methodologies. His research has been supported by major funding agencies including the National Science Foundation and the National Institutes of Health. He is actively involved in several research groups including the UW Geometric Data Analysis Group, the UW Center for Statistics and the Social Sciences, and the National Alzheimer's Coordinating Center.

• Nonparametric statistics
• Topological data analysis
• Missing data
• +7 more

Prof. Dr. Daniel Hug is a Professor at the Karlsruhe Institute of Technology (KIT) , affiliated with the Department of Mathematics and the Institute of Stochastics . His research focuses on Probability , Geometry , Convex geometric analysis , Stochastic geometry , and Educational mathematics . He contributes to the DFG Priority Program Random Geometric Systems and previously participated in the DFG research unit Geometry and Physics of Spatial Random Systems . Prof. Hug has co-authored two influential monographs: Poisson Hyperplane Tessellations (Springer Monographs in Mathematics) and Lectures on Convex Geometry (Springer Graduate Texts in Mathematics, 2020). His work spans theoretical advancements in convex and stochastic geometry , including integral geometry, random mosaics, and tensor valuations, alongside applied studies in digital microstructures and Minkowski tensors . His publications from 2025–2024 emphasize hyperbolic space , Boolean models , and Minkowski tensor estimators , reflecting ongoing collaborations with researchers like M. Klatt, C. Thäle, and R. Schneider. Prof. Hug leads courses such as Stochastic Geometry and seminars on Random graphs and tessellations , and is actively involved in working groups like AG Stochastische Geometrie and AG Stochastik .

• Probability
• Geometry
• (Convex) geometric analysis
• +3 more

Professor Dörthe Tetzlaff is a leading figure in ecohydrology, serving as Head of Department (A1) Ecohydrology at the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) and as Full Professor of Ecohydrology at Humboldt-Universität zu Berlin, Faculty of Mathematics and Natural Sciences, Geography Department. She also holds an Honorary Professorship at the University of Aberdeen, Scotland. Her work bridges theoretical hydrology with practical applications for water security and environmental management. Professor Tetzlaff's research focuses on ecohydrological processes, particularly using stable water isotopes to understand water cycling, catchment functioning, and landscape connectivity. Her work spans from pristine natural systems to complex urban environments, with particular emphasis on drought resilience, water security, and nature-based solutions. She has pioneered tracer-aided modeling approaches to quantify water storage, fluxes, and ages across the soil-plant-atmosphere continuum. Her publication record shows a clear trend toward increasingly sophisticated integration of multi-tracer approaches with ecohydrological modeling, with recent work focusing on urban systems, drought impacts, and nature-based solutions. The publications demonstrate expertise across hydrology, biogeochemistry, and ecological applications, with strong methodological development in isotope hydrology and modeling techniques. Polubarinova-Kochina Hydrologic Sciences Mid-Career Award of the American Geophysical Union AGU (2024) Water Resource Prize of the Rüdiger Kurt Bode Foundation (2024) Member of the Berlin-Brandenburg Academy of Sciences and Humanities (2023) Fellow of The European Academy of Sciences (2022) Fellow of the Geological Society of America GSA (2020) Professor Tetzlaff actively supervises numerous PhD students and postdoctoral researchers, with her group focusing on cutting-edge ecohydrological research. She leads multiple significant research projects including WETSCAPES2.0, ECCO, ISO-SCALE, and BiNatUr, which address critical questions about water security, climate change impacts, and nature-based solutions in urban environments. Her work is supported by substantial funding from various national and international sources. She leads the Ecohydrology research group at both IGB and Humboldt-Universität zu Berlin, which employs an integrative approach combining field measurements, isotope techniques, and advanced modeling to understand water dynamics across diverse landscapes. The group collaborates extensively with international partners across Europe, North America, and Asia, contributing to global understanding of hydrological processes.

• Ecohydrology
• Hydrology
• Landscape Ecology
• +6 more