Find Academic Supervisors

• Photogrammetry
• Geoinformation
• Remote Sensing
• +7 more

Prof. Christian Heipke is a distinguished academic serving as Dean of the Faculty of Civil Engineering and Geodetic Science at Leibniz University Hannover, Germany. He also holds the position of Executive Director at the Institute of Photogrammetry and GeoInformation (IPI), one of the leading research institutions in geospatial sciences within the faculty. His leadership extends across multiple committees including the Curriculum and Teaching Committee, Admissions and Examination Boards for Geodetic Science and Geoinformatics, and Navigation and Environmental Robotics. As a Professor at IPI, he maintains active research while overseeing significant academic and administrative responsibilities at the university. Professor Heipke's research spans multiple domains within geospatial sciences, with particular emphasis on: Advanced photogrammetric techniques and algorithms Remote sensing applications for environmental monitoring Computer vision approaches for geospatial data analysis Urban development monitoring using satellite imagery Machine learning applications in geoinformatics Disaster prediction and management systems His recent scholarly output reveals a strong focus on integrating cutting-edge computer vision and deep learning techniques with traditional photogrammetric methods. Analysis of his 15 most recent publications shows a clear trajectory toward more sophisticated AI-driven approaches for processing geospatial data, with particular attention to time-series analysis, uncertainty quantification, and multi-view systems. His work bridges theoretical advancements with practical applications in flood forecasting, deforestation monitoring, urban planning, and construction materials analysis. The geographic scope of his research has expanded significantly, with recent projects focusing on international case studies in the Philippines and tropical regions. Professor Heipke leads the Institute of Photogrammetry and GeoInformation, a major research hub that has celebrated 75 years of contributions to the field. His leadership extends to the Graduiertenkolleg 2159: "Integrity and Collaboration in Dynamic Sensor Networks," where he serves as a professor overseeing doctoral research. The institute maintains state-of-the-art facilities for processing satellite imagery, aerial photography, and developing novel algorithms for geospatial data analysis. Under his direction, the institute has strengthened its international collaborations and interdisciplinary research approaches, particularly in addressing Sustainable Development Goals through geospatial technologies.

Marianne PouplierView profile

Professor

• Speech production
• Coarticulation
• Phonetic universals
• +3 more

Prof. Marianne Pouplier is a Professor at the Institute of Phonetics and Speech Processing (IPS) at Ludwig Maximilian University of Munich. Her research focuses on speech production mechanisms, particularly coarticulation, phonetic universals, and language-specific variations. She investigates articulatory timing, speech errors, and cross-linguistic differences in consonant clusters using advanced methodologies like real-time MRI and electromagnetic articulography. Her work bridges theoretical models (e.g., Articulatory Phonology) with empirical data, emphasizing the interplay between phonological representation and motor execution. Key contributions include studies on nasal coarticulation, larynx dynamics, and the role of articulatory effort in speech production. Selected publications highlight her expertise in analyzing speech motor control through interdisciplinary approaches. She collaborates internationally, contributing to projects like the Bavarian Archive for Speech Signals (BAS). No awards or grants are explicitly listed, but her extensive publication record underscores her scholarly impact.

Michael UlbrichView profile

Professor

• Nonlinear Optimization
• Optimal Control
• Numerical Analysis
• +3 more

Prof. Dr. Michael Ulbrich is a full professor and Chair of Mathematical Optimization at the Technical University of Munich (TUM), within the School of Computation, Information and Technology. He has held this position since 2006 and previously served as Dean of Studies (2007–2010) and Vice Dean of the Faculty of Mathematics (2012–2015). His research focuses on nonlinear optimization, optimal control, and numerical analysis, with applications in fluid dynamics, shape optimization, and PDE-constrained systems. He leads projects in the DFG SPP 1962 and IGDK 1754, and has received prestigious awards including the Howard Rosenbrock Prize (2015) and the Doctoral Award from the TUM Association of Friends (1996). Ulbrich is Editor-in-Chief of Optimization and Engineering and contributes to multiple journals. His work bridges theoretical foundations and practical applications, including CO2 sequestration, fluid-structure interaction, and distributed optimization algorithms. Education: PhD (1996), Habilitation (2002) in Mathematics at TUM. Research stays at Rice University (USA) under DFG funding. Research Areas: Semismooth Newton methods, PDE-constrained optimization, optimal control of Navier-Stokes equations, and distributed parameter systems. Awards: Rosenbrock Prize, Teaching Excellence Awards, and recognition for doctoral work. Leadership Roles: Department Head of Mathematics (2022–), Member of TUM Senate (2019–2022), and Co-Chair of GAMM 2018. Ulbrich has authored influential textbooks like Semismooth Newton Methods for Variational Inequalities and Nichtlineare Optimierung . His recent projects include OptiGeoS (2024–2026) and collaborations on nonsmooth optimization and stochastic algorithms. His academic contributions span over 100 publications, emphasizing both algorithmic innovation and rigorous mathematical analysis.

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

• Virtual Reality
• Human-Computer Interaction
• Augmented Reality
• +4 more

Marc Erich Latoschik is a Professor in the Department of Human-Computer Interaction at the University of Würzburg, Germany. He previously held roles at Bayreuth University, Bielefeld University, and FHTW Berlin. His research focuses on virtual and augmented reality, embodiment, human-computer interaction, and applications in health, education, and social systems. Education: Completed his PhD in 2001 at Bielefeld University with a thesis on multimodal interaction in virtual reality. Research Interests: Embodied interaction, virtual embodiment, presence and plausibility in VR/AR, avatar design, social virtual reality, health applications (e.g., VR therapy for body image issues), and XR security/privacy. Active in developing frameworks like Reality Stack I/O and MAIL for VR/AR research. Key Projects: ViTraS study on body image exercises in VR, avatars for mass use via smartphone reconstruction, and motion-based biometrics in XR. Collaborates with medical teams on cybersickness detection and emergency training simulations. Labs/Teams: Leads research groups on immersive technologies and social VR applications. Involved in interdisciplinary projects combining HCI, AI, and healthcare.

• Theoretical Chemistry
• Computational Chemistry
• Material Science
• +1 more

Mr. Tobias Spengler is a lecturer and Master's student at the Faculty of Chemistry, Universität Bielefeld. His work is associated with the Theoretical Chemistry department, which aligns with the university's strategic focus on the Material World —examining structures and processes at interfaces of chemistry, physics, and biology. As part of this interdisciplinary environment, Spengler contributes to teaching and research within theoretical and computational chemistry frameworks. Universität Bielefeld emphasizes transcending disciplinary boundaries, evident in initiatives like the Emerging Synergies in Mathematics (ESyMath) focus area, which integrates mathematics with applied sciences. While Spengler's direct involvement in these projects isn't specified, his affiliation suggests exposure to collaborative research environments. The university supports advanced research through centers like CRC 1283, TRR 358, and IMW, addressing uncertainty, geometry, and mathematical economics. These projects highlight the institution's commitment to cutting-edge interdisciplinary work, though no explicit connection to Spengler is stated in the provided text.

Pratul SrinivasanView profile

Researcher

• Computer Vision
• Computer Graphics
• Machine Learning
• +3 more

Pratul Srinivasan is a Researcher at Google DeepMind specializing in Neural Radiance Fields (NeRF) , 3D scene reconstruction , and view synthesis at the intersection of computer vision , graphics , and machine learning . He earned his PhD from the EECS Department at UC Berkeley in 2020 under Ren Ng and Ravi Ramamoorthi , with prior research at Duke University on medical computer vision under Sina Farsiu . Research Interests: Pratul focuses on 3D reconstruction using neural fields, light field synthesis , illumination modeling , and diffusion-based 3D generation . His work addresses challenges in photorealistic rendering , real-time view synthesis , and inverse rendering with applications in astronomy and medical imaging . Publication Trends: Recent articles emphasize real-time NeRF (e.g., Bolt3D), refractive material modeling , shadow-based illumination recovery , and cross-scale generative synthesis . Collaborations span institutions including MIT , NVIDIA , and ETH Zurich . Scientific Awards: 2025 SIGGRAPH Significant New Researcher Award 2021 ACM Doctoral Dissertation Award Honorable Mention 2020 David J. Sakrison Memorial Prize Best Paper Awards at ECCV 2020, ICCV 2021, and CVPR 2022 Advising & Collaborations: Advised by Ren Ng and Ravi Ramamoorthi during his PhD, Pratul collaborates with researchers like Jonathan T. Barron , Ben Mildenhall , and Katherine L. Bouman . His work integrates differentiable simulations , Fourier feature networks , and multiplane image extrapolation . Technical Contributions: Key innovations include anti-aliased NeRF , memory-efficient rendering , refractive relighting , and multi-view relighting techniques , with impacts on virtual reality , astronomical imaging , and 3D content creation .

Agnes DesolneuxView profile

Professor

• Image Analysis
• Statistical Methods
• Computer Vision
• +7 more

Agnes Desolneux is a CNRS Research Director at the Borelli Centre (formerly CMLA) and a Professor attached to the Mathematics Department at ENS Paris-Saclay. Education: PhD in Applied Mathematics (2000) from ENS Cachan Habilitation in Applied Mathematics (2010) from Université Paris Descartes Her research focuses on image analysis via statistical methods , particularly a contrario approaches, image restoration, texture synthesis, Determinantal Point Processes (DPP), optimal transport, Gaussian mixtures, geometry of random field excursions, shot-noise models, and mathematical modeling of visual perception through Gestalt theory. The articles extracted reflect her expertise in applied mathematics and computer vision , with recent works (2025-2020) on optimal transport algorithms, DPP applications, multiscale texture analysis, and stochastic modeling in medical imaging. Keywords span machine learning, probability theory, medical imaging, and computer vision . She has no listed scientific awards but has authored influential works including the book From Gestalt Theory to Image Analysis: A Probabilistic Approach (Springer, 2008) and Pattern Theory: the stochastic analysis of real-world signals (AK Peters, 2010).

• Computational Pathology
• Medical Image Analysis
• Deep Learning
• +4 more

Nasir M. Rajpoot is a Professor in the Department of Computer Science at the University of Warwick, UK. His research focuses on computational pathology, medical image analysis, and deep learning applications in histology. He leads interdisciplinary projects integrating artificial intelligence with healthcare, particularly in cancer diagnostics and pathology workflows. Rajpoot’s work emphasizes developing robust algorithms for histology image analysis, including nuclear segmentation, tumor classification, and domain generalization in computational pathology. His contributions include the TIAToolbox, an open-source framework for tissue image analytics, and the CoNIC Challenge to advance nuclear detection and counting in histology images. He collaborates with clinicians and biologists to translate AI models into clinical practice, addressing challenges like tumor heterogeneity and staining variability. Rajpoot’s research spans colorectal, lung, and oral cancers, with a focus on predicting clinical outcomes via histological features and genomic data integration. Notable projects include the development of Handcrafted Histological Transformer (H2T) for unsupervised representations of whole slide images and the SAFRON framework for histology image synthesis. His work addresses domain adaptation, robustness evaluation, and explainability in AI-driven pathology systems.

• LiDAR-based Perception
• Autonomous Vehicles
• Agricultural Robotics
• +4 more

Jens Behley is a Lecturer (Privatdozent) and postdoctoral researcher at the Department of Photogrammetry, University of Bonn. He completed his habilitation in 2023 with a thesis on LiDAR-based spatio-temporal scene understanding for autonomous vehicles and earned his PhD in 2014 under Prof. Armin Cremers. His research focuses on LiDAR perception, agricultural robotics, and 3D scene understanding. Behley is an Associate Editor at IEEE Robotics and Automation Letters (RA-L) and has authored influential datasets like SemanticKITTI and BonnBeetClouds3D. Education: PhD in Computer Science, University of Bonn, 2014 Habilitation in Photogrammetry, University of Bonn, 2023 Research Interests: LiDAR-based perception in urban and agricultural environments, machine learning for robotics, semantic mapping, SLAM algorithms, and 3D reconstruction. His work bridges computer vision and robotics, with applications in autonomous vehicles and precision agriculture. Awards: Best Agri-Robotics Paper Award (IROS 2024) Outstanding Reviewer Awards (ECCV, CVPR, ICRA) Faculty Award for Geodesy (2021) Advisees & Grants: Behley collaborates extensively with the PRBonn lab and researchers like Cyrill Stachniss, focusing on projects funded by EU Horizon and industry partners. His team develops open-source tools for LiDAR processing (e.g., KISS-ICP, VDBFusion). Labs/Teams: Part of the Photogrammetry and Robotics Institute (IGG) at the University of Bonn, contributing to the PRBonn research group.

Hendrik WeberView profile

Professor

• Stochastic Analysis
• Stochastic Partial Differential Equations (SPDEs)
• Regularity Structures
• +4 more

Prof. Dr. Hendrik Weber is a Professor of Mathematics at the University of Münster, leading the Workgroup for Stochastic Analysis. He holds the Bridging the Gaps Professorship and is affiliated with the Faculty of Mathematics and Computer Science. His expertise lies in stochastic analysis, particularly stochastic partial differential equations (SPDEs) and their applications in mathematical physics and statistical mechanics. Weber's research focuses on regularity structures, singular SPDEs, and the interplay between stochastic processes and nonlinear dynamics. Education and Career: Weber earned his PhD from the University of Bonn (2010) and held positions at the University of Warwick (2010–2018) and the University of Bath (2018–2022) before joining Münster in 2022. He has been recognized with awards including the ERC Consolidator Grant (2022), Philip Leverhulme Prize (2017), and Rollo Davidson Prize (2016). Research Interests: Weber's work addresses theoretical challenges in SPDEs, including invariant measures, phase transitions, and scaling limits. His projects span topics like singularities in PDEs, field theory randomness, and deep learning surrogate methods. Recent studies include the dynamic Φ⁴ model, stochastic quantization in non-commutative spaces, and a priori bounds for quasilinear SPDEs. Publications: Over 60 peer-reviewed articles, including high-impact contributions to Annals of Probability , Communications in Mathematical Physics , and Archive for Rational Mechanics and Analysis . His work emphasizes rigorous mathematical analysis of stochastic systems and their physical implications. Awards and Grants: ERC Consolidator Grant (2022), Royal Society Fellowship (2016), and multiple collaborative projects funded by the EPSRC and DFG. His research also bridges theoretical developments with applications in machine learning and feature engineering using regularity structures. Labs/Teams: Leads a dynamic research group comprising PhD students (e.g., Sophie Mildenberger) and postdoctoral researchers. Collaborations with global institutions like the University of Warwick and the University of Bath drive interdisciplinary advancements in stochastic analysis.

Gerold AlsmeyerView profile

Professor

• Theory of stochastic processes
• Stochastic fixed-point equations
• Iterated function systems
• +7 more

Prof. Dr. Gerold Alsmeyer is a faculty member at the Institute of Mathematical Stochastics, Department of Mathematics and Computer Science, University of Münster. He is an active researcher with a focus on stochastic processes, particularly stochastic fixed-point equations and iterated function systems. His work is supported by his role as an Investigator in Mathematics Münster in the project EXC 2044 - C1: Evolution and asymptotics. His primary research interests include the theory of stochastic processes, branching processes, Markov random walks, renewal theory, and the asymptotic analysis of random structures such as random trees and polytopes. He has made significant contributions to the understanding of fluctuation theory, perpetuities, and the smoothing transform. His recent publications (2017–2023) reveal a sustained focus on theoretical probability, with recurring themes in random difference equations, iterated function systems, and limit theorems for stochastic processes. The work spans pure mathematical theory and applications in mathematical biology and combinatorics, indicating a broad yet deep research profile. Prof. Alsmeyer has supervised numerous doctoral and master’s students, including Viet Hung Hoang, Christopher Eick, Philipp Godland, and Fabian Buckmann, whose dissertations cover topics in branching processes, random walks, and stochastic fixed-point equations. He has no listed scientific awards in the provided texts. He teaches courses in probability theory, mathematical statistics, branching processes, and stochastic recursion equations, demonstrating a strong commitment to academic mentoring and education.

Lars LinsenView profile

Professor

• Interactive Visual Analysis
• Medical Visualization
• Scientific Visualization
• +4 more

Prof. Dr.-Ing. Lars Linsen is a full Professor of Computer Science at the Westfälische Wilhelms-Universität (WWU) Münster, leading the VISualization & graphIX (VISIX) group. His primary affiliation is the Institute of Computer Science within the Faculty of Mathematics and Computer Science. He holds adjunct professorships at Jacobs University, Bremen, and has held previous academic roles including Full Professor at Jacobs University (2012–2017) and Associate/Assistant Professor roles in Germany and the U.S. His research focuses on interactive visual analysis, medical visualization, and scientific visualization, with applications in life sciences and engineering. Education: PhD (Dr.-Ing.) in Computer Science from Universität Karlsruhe (2001), M.Sc. (Diplom) in Computer Science (1997), B.Sc. (Vordiplom) in Computer Science (1994). Awards: IEEE Visualization Design Contest Winner (2008, 2022, 2018), Preis des Fördervereins des Forschungszentrum Informatik (2002). Research Highlights: Develops visualization tools for medical imaging (e.g., mass spectrometry imaging, MRI data analysis) and physical simulations (e.g., wildfire spread analysis, asteroid impact modeling). Active in EU-funded projects like Pig-Pro-QuO (surface coatings) and cells-in-motion initiatives. Supervised over 20 PhD/MS advisees, including notable graduates in medical visualization and simulation ensemble analysis. Publications: Over 100 peer-reviewed articles in top venues like IEEE Transactions on Visualization and Computer Graphics, Computers & Graphics, and EuroVis. Key works include SciVis contest-winning wildfire analysis frameworks and medical visualization tools for stenosis detection. Teaching: Offers courses on visualization, computer graphics, and computational science. Actively involved in thesis supervision and curriculum development at both WWU Münster and Jacobs University. Grants & Collaborations: Principal investigator on DFG-funded projects (e.g., hemodynamics simulations, ensemble visualization) and industry collaborations (e.g., Tascon GmbH for coating quality analysis). Member of the Cells-in-Motion Interfaculty Centre and CDH board at WWU.

Mathias DrtonView profile

Professor

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

Markus BachmayrView profile

Professor

• Numerical Analysis
• Partial Differential Equations
• High-dimensional Problems
• +4 more

Prof. Dr. Markus Bachmayr is a full professor at the Institute for Geometry and Practical Mathematics, RWTH Aachen University, holding the chair for Applied Mathematics. His research focuses on nonlinear approximation, high-dimensional partial differential equations (PDEs), uncertainty quantification, and numerical methods in quantum chemistry. He leads the ERC Consolidator Grant project Computational Complexity of Highly Nonlinear Approximations (COCOA) and contributes to CRC 1481 Sparsity and Singular Structures, and RTG 2326 Energy, Entropy, and Dissipative Dynamics. His recent work emphasizes adaptive low-rank and sparse approximation techniques for parametric and stochastic PDEs, including applications in radiative transfer and poroviscoelastic flow modeling. He serves as Editor-in-Chief of Foundations of Computational Mathematics and Associate Editor for multiple journals. Scientific Awards: John Todd Award 2013 Borchers Plakette 2014 Erwin Wenzl Preis 2007 He has taught courses such as Numerische Analysis I/II, Numerische Mathematik für Elektrotechniker, and seminars on numerical methods and approximation theory.