Find Academic Supervisors

Andrea BarthView profile

Professor

• Stochastic Partial Differential Equations
• Uncertainty Quantification
• Monte Carlo Methods
• +4 more

Andrea Barth is a W3-Professor of Computational Methods for Uncertainty Quantification at the University of Stuttgart, leading the Research Group for Computational Methods for Uncertainty Quantification within the Excellence Cluster for Simulation Technology. She holds a Ph.D. from the University of Oslo (2009) and has held positions at ETH Zürich and the University of Stuttgart. Her work focuses on stochastic partial differential equations, numerical methods for uncertainty quantification, and applications in engineering and natural sciences. Education: Ph.D. in Mathematics, University of Oslo (2006–2009) Lecturer/Postdoc at ETH Zürich (2010–2013) Junior Professor at University of Stuttgart (2013–2017) Research Interests: Stochastic PDEs, uncertainty quantification, Monte Carlo methods, Bayesian inverse problems, and numerical analysis of random fields. Her work bridges stochastic analysis and numerical simulations, addressing challenges in modeling and simulating complex systems with uncertainties. Grants & Funding: Principal Investigator in projects like 'Data-Integrated Simulation Science' (ExC 2075) and 'Quantitative Methods for Visual Computing' (SFB/TRR 161). Her research also explores applications in porous media, carbon dioxide storage, and optical flow analysis. Supervision: Advised PhD students including Oliver König, Fabio Musco, and Robin Merkle. Current students focus on topics like deep learning for stochastic PDEs and continuous level Monte Carlo methods.

Federico BoniardiView profile

Researcher

• Robotics
• Mobile Robot Navigation
• Localization and Mapping
• +6 more

Federico Boniardi is a researcher at the Autonomous Intelligent Systems group in the Department of Computer Science at Albert-Ludwigs-Universität Freiburg, Faculty of Engineering. He holds advanced degrees in Mathematics and Artificial Intelligence and has been actively contributing to robotics research since 2014. Bachelor’s in Mathematics, University of Milano (2006–2010) Master’s in Mathematics, University of Milano (2010–2012) MSc in Artificial Intelligence, University of Edinburgh (2013–2014) PhD Research Assistant, University of Freiburg (2014–2019) His research centers on mobile robot navigation, focusing on robust localization and mapping in indoor environments using LiDAR and architectural or hand-drawn floor plans. He explores long-term autonomy and human-robot interfaces, particularly in sketch-based map interpretation and shared control systems. His work integrates sensor calibration, topometric mapping, and deep learning for room layout extraction. The most recent publications reflect a strong trend in leveraging CAD and sketched maps for robot localization, combining probabilistic filtering, graph-based optimization, and semantic understanding. His contributions span key robotics venues such as IEEE/RSJ IROS, IEEE ICRA, and Robotics and Autonomous Systems. Scientific Contributions: Developed pose graph-based localization for long-term navigation Advanced LiDAR-based localization in architectural plans Explored Bayes filters for shared autonomy Enabled robot navigation using hand-drawn sketch interfaces Federico has served as a Teaching Assistant for Theoretical Computer Science and supervised student seminars on robot navigation. He has contributed to EU-funded research projects including RobDREAM and SQUIRREL, focusing on robot performance optimization and clutter management. He is based in Freiburg, Germany, and continues active research in autonomous intelligent systems.

Ralf KellnerView profile

Professor

• Economics
• Data Science
• Statistics
• +2 more

Professor Ralf Kellner holds the Chair of Financial Data Analytics at the University of Passau, Faculty of Economics. His work integrates economics, data science, and statistics, focusing on empirical and application-oriented research to explore how statistical learning and AI can uncover insights in data-driven decision-making processes that generate economic value. He also teaches courses such as Deep Learning and Text Analysis in Finance, Financial Data Analytics and Machine Learning, and Scientific Computing with Python. His research examines the intersection of financial markets, statistical learning, and artificial intelligence, with specific interests in modeling adverse financial developments, systemic risks, and analyzing text data via domain-specific language models. Publications highlight collaborations with researchers like D. Rösch and N. Gatzert. Recent publications include work on hybrid service agents, quantile neural networks, default resolution time analysis, Bayesian sovereign bond risk models, and international diversification studies. His methodological approaches span extreme value theory, quantile regression, and multivariate statistical techniques applied to financial and insurance contexts. Contact: ralf.kellner@uni-passau.de

Marcel KolloviehView profile

Researcher

• Generative models
• Graphs
• Time series
• +4 more

Marcel Kollovieh is a researcher at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science . He is part of the Data Analytics and Machine Learning (DAML) Lab under the mentorship of Prof. Dr. Stephan Günnemann. Education B.Sc. and M.Sc. in Informatics from TUM Research Interests : Marcel focuses on generative models (including variational autoencoders, diffusion models, and score-based models), graphs , and time series , with additional work on hierarchical structures , robustness , and Bayesian learning . His publications span conferences like ICML , ICLR , and NeurIPS , alongside journals such as TMLR . Themes include token merging , flow matching , and adversarial robustness in temporal data, alongside probabilistic clustering and diffusion models . Lab and Team : Marcel works within the DAML Lab at TUM.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +3 more

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

• Computer Architecture
• Hardware Design
• Signal Processing
• +7 more

Prof. Dr.-Ing. Holger Blume serves as Vice President for Research and Transfer at Leibniz University Hannover while maintaining his academic position as Professor in the Architectures and Systems Section within the Faculty of Electrical Engineering and Computer Science. He holds multiple leadership positions including Chairperson of the Research Commission and Central Ethics Committee, Executive Board member of eNIFE (Leibniz Research Initiative for Neurosciences), and membership in both the Laboratory of Nano and Quantum Engineering and L3S Research Centre. His research interests span computer architecture, hardware design, signal processing, AI accelerators, hearing aid technology, and biomedical engineering. His work bridges theoretical computer science with practical applications in automotive systems, medical devices, and quantum engineering. Professor Blume's research demonstrates strong interdisciplinary connections between electrical engineering, computer science, and biomedical applications, with particular emphasis on hardware-oriented solutions for real-world problems. Analysis of his recent publications (2023-2025) reveals a strong focus on hardware acceleration for AI and signal processing applications, particularly in automotive radar/LiDAR systems and hearing aid technology. His work shows consistent innovation in RISC-V processor design, specialized hardware for mathematical functions, and biomedical applications of engineering principles. The research demonstrates a clear trajectory toward energy-efficient, specialized computing architectures for specific application domains. As Vice President for Research and Transfer, Professor Blume oversees significant research initiatives at Leibniz University Hannover, which hosts multiple Clusters of Excellence including PhoenixD (Photonics, Optics, and Engineering), QuantumFrontiers, and Hearing4all. The university participates in numerous collaborative research centers and junior research groups funded by DFG, BMBF, and EU programs. Professor Blume is actively involved in multiple research facilities including the Laboratory of Nano and Quantum Engineering and the L3S Research Centre. His work connects with Leibniz University's research focuses on optical technologies, quantum optics and gravitational physics, and biomedical research and technology. His leadership positions indicate strong involvement in shaping the research strategy and ethical framework of the university's scientific endeavors.

Jun MorimotoView profile

Professor

• Reinforcement Learning
• Humanoid Robotics
• Exoskeleton Robotics
• +3 more

Jun Morimoto is a Professor and Head of the Department of Brain Robot Interface at ATR Computational Neuroscience Laboratories. He holds a Ph.D. in Information Science from the Nara Institute of Science and Technology (NAIST) and has held positions at Carnegie Mellon University and the Japan Science and Technology Agency (JST). His research focuses on reinforcement learning, humanoid robotics, exoskeleton systems, and brain-robot interfaces. He has led teams at RIKEN and contributed to projects such as the ICORP initiative. His work integrates neuroscience principles with robotics, emphasizing applications in assistive technologies and neurorehabilitation. Key contributions include developing exoskeleton control strategies, brain-computer interfaces, and adaptive humanoid robot systems. He has authored over 100 peer-reviewed papers, with recent work addressing EEG-based motor intent decoding and multi-site neuroimaging databases. His academic service includes organizing international conferences (e.g., Humanoid Robots, ICRA) and serving on program committees. He has also delivered invited talks on topics such as brain-controlled exoskeletons and stochastic optimal control in robotics.

Rolf FindeisenView profile

Professor

• Model Predictive Control (MPC)
• Cyber-Physical Systems
• Battery Systems Optimization
• +5 more

Prof. Rolf Findeisen is a Professor in the Department of Control and Cyber-Physical Systems (CCPS) at Technische Universität Darmstadt. His work focuses on advancing control theory and its applications in cyber-physical systems, autonomous systems, and energy storage systems. Key areas include model predictive control (MPC), battery management systems, machine learning integration into control frameworks, and optimization of crystallization processes. He leads research on safety-critical systems, data-driven control methods, and interdisciplinary applications in robotics and biotechnology. His research spans theoretical advancements in MPC stability, stochastic control, and Gaussian process modeling, alongside practical implementations in autonomous vehicles, lithium-ion battery systems, and bioprocess optimization. Notable contributions include frameworks like HILO-MPC for integrating machine learning with control systems, and methodologies for safe exploration in autonomous navigation. Prof. Findeisen's publications emphasize energy-efficient trajectory planning, fault detection in battery systems, and real-time optimization of manufacturing processes. His work bridges academic theory with industrial applications, addressing challenges in scalability, safety, and computational efficiency. His lab collaborates on national projects like IN-Fly-Tec and INFLIGHT, focusing on innovative flight control systems and sensor technologies. Current research trends include hybrid intelligent optimization, cybergenetic control of microbial systems, and safe reinforcement learning for control systems.

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +4 more

Dr. Lea Frermann is an active researcher in computational linguistics and natural language processing, with a focus on media framing analysis, fairness in AI, and Bayesian modeling of semantic categories. Her work bridges machine learning with social science applications, particularly in political discourse and climate communication contexts. PhD in Bayesian models of category acquisition (University of Edinburgh, 2017) Key collaborations with University of Melbourne, ACL, EMNLP, and CoNLL communities Research emphasizes: Media framing analysis across cultures Explainable AI for bias detection Bayesian approaches to language understanding Word association modeling for value systems Computational methods for political discourse analysis Applications in mental health support forums Recent publications focus on large language models' limitations in hierarchical reasoning, multimodal categorization, and systematic fairness evaluation frameworks. Collaborations show strong connections with researchers like Timothy Baldwin, Trevor Cohn, and Omri Abend.

Fabian SchmidtView profile

Professor

• Cosmology
• Dark Matter
• Dark Energy
• +3 more

Fabian Schmidt is a Professor (Forschungsgruppenleiter) at the Max Planck Institute for Astrophysics (MPA) in Garching, Germany. He specializes in cosmology, focusing on dark matter, dark energy, and the evolution of cosmic structures. His research explores the universe's composition, history, and the mechanisms behind primordial density fluctuations. Education: Diplom (equivalent to MSc) in cosmic gamma rays from Humboldt University Berlin, PhD (2005-2009) at the University of Chicago under advisors Scott Dodelson and Wayne Hu. Held fellowships at Caltech (Moore Fellow) and Princeton (NASA CXC Einstein Fellow) before joining MPA in 2013. Research interests include cosmological observables in general relativity, baryon acoustic oscillations (BAO) reconstruction, and field-level inference techniques. His group is funded by the Excellence Cluster Origins and MPA resources, with ongoing work on large-scale structure, gravitational lensing, and dark matter physics. Notable contributions span theoretical cosmology, computational methods for galaxy clustering analysis, and Bayesian forward modeling. His team advises PhD students and postdocs in areas like EFT-based BAO reconstruction, simulation-based inference, and dark matter dynamics. Outreach: Active in public talks (Lange Nacht der Wissenschaften) and collaborations with science journalists. Co-author of Modern Cosmology textbook (2nd ed. 2020, 3rd ed. 2025), addressing topics like inflation and quantum vacuum cosmology.

Jilles VreekenView profile

Professor

• Causal Inference
• Machine Learning
• Data Mining
• +5 more

Prof. Dr. Jilles Vreeken is tenured faculty at the CISPA Helmholtz Center for Information Security , where he leads the Exploratory Data Analysis group. He also serves as an Honorary Professor at Saarland University . Research focuses on causal inference, machine learning, and data mining Develops unsupervised methods for robust, interpretable models PI on grants like HAICU's Neuro-Explicit Models and Crushing Antimicrobial Resistance His recent work spans causal discovery in non-stationary time series ( SPACETIME ), federated binary matrix factorization, interpretable neural search patterns, and data modification rule mining from event logs. He applies information-theoretic approaches to address hidden confounding, selection bias, and multi-environment causal modeling. Key trends in his publications include: Integrating causal inference with machine learning via algorithmic Markov conditions Advancing federated learning for privacy-preserving causal discovery Creating interpretable pattern mining frameworks for graphs, sequences, and high-dimensional data Developing MDL-based methods for reliable dependency and rule discovery Scientific Recognition: 2018 - IEEE ICDM Tao Li Award 2018 - IEEE ICDM Best Paper 2015 - UdS-CS Busy Beaver Teaching Award 2011 - ACM SIGKDD Best Student Paper 2010 - ACM SIGKDD Doctoral Dissertation Runner-Up 2009 - ECML PKDD Best Student Paper As an educator, he has supervised 15+ PhD/MSc students and taught courses like Topics in Algorithmic Data Analysis and Information-Theoretic Machine Learning . His research group pioneers methods for trustworthy information processing and causal anomaly detection , with applications in materials science, epidemiology, and cybersecurity.

Thomas A. RunklerView profile

Adjunct Professor

• Machine Learning
• Data Analytics
• Pattern Recognition
• +3 more

Thomas A. Runkler is an Adjunct Professor at the Technical University of Munich (TUM), holding a Chair in the Foundations of Software Reliability and Theoretical Computer Science within the TUM School of Computation, Information and Technology. Since 1999, he has taught computer science at TUM while concurrently serving in various expert and managerial roles at Siemens AG, where he currently holds the position of Senior Principal Research Scientist. His academic journey includes a Master’s and PhD in electrical engineering from TU Darmstadt (1992/1995), followed by postdoctoral research at the University of West Florida (1996–1997). Runkler’s research spans machine learning , data analytics , fuzzy systems , and optimization . He has authored/co-authored over 200 publications, focusing on topics like clustering algorithms, neural networks, and decision-making frameworks. Professional contributions include leadership roles in organizations such as the IEEE CIS committees and the German Association for Computer Science’s Fuzzy Systems group. His teaching includes courses on Data Mining and Knowledge Discovery . Key publications include foundational works on fuzzy preference structures, Bayesian decomposition of dynamical systems, and interpretable reinforcement learning policies. Runkler’s work bridges academia and industry, emphasizing practical applications in automation, recommendation systems, and industrial procurement forecasting. His research often addresses challenges in uncertainty handling, algorithm design, and scalable data analysis.

Ralf SchlüterView profile

Lecturer

• Automatic Speech Recognition
• Machine Learning
• Pattern Recognition
• +4 more

Ralf Schlüter is a senior researcher and Lecturer (Privatdozent) at RWTH Aachen University, where he leads the Automatic Speech Recognition Group within the Department of Computer Science. His academic roles include serving as Academic Director and leading research initiatives at Lehrstuhl Informatik 6: Human Language Technology and Pattern Recognition. He holds a Dipl. in Physics (1995), a Dr. rer. nat. in Computer Science (2000), and a Habilitation in Computer Science (2019), all from RWTH Aachen University. His research focuses on advanced speech recognition technologies, including end-to-end architectures, acoustic modeling, discriminative training, and machine learning applications. He has led numerous national and international projects, such as EU-funded initiatives like BABEL, BOLT, and GALE. Schlüter has published extensively in top-tier conferences like Interspeech and ICASSP, contributing to advancements in neural network frameworks, feature extraction, and error analysis. He teaches courses on speech recognition, pattern recognition, and signal processing, and serves as a Subject Editor for Speech Communication . His work also involves developing the RWTH ASR toolkit, emphasizing scalable and efficient speech recognition systems. Schlüter’s contributions to speech technology have earned recognition, including the ISCA Best Student Paper Award (2019).

• Distributed Computing
• Federated Learning
• Cloud Computing
• +12 more

Salman Zubair Toor is a researcher at Uppsala University, Sweden, specializing in distributed computing, federated learning, and cloud/edge infrastructure optimization. His work spans resource scheduling, data streaming, and secure anomaly detection.

Thomas NaglerView profile

Professor

• Mathematical Statistics
• Machine Learning
• High-dimensional Dependence Models
• +3 more

Thomas Nagler is a Professor at the Department of Statistics, Faculty of Mathematics, Computer Science and Statistics at Ludwig Maximilian University of Munich (LMU Munich). He also serves as a principal investigator at the Munich Center for Machine Learning (MCML), where he leads research at the intersection of mathematical statistics and machine learning. Nagler received his academic training at Technical University of Munich (TU Munich), earning a BSc in Mathematics (2009-2012), followed by an MSc in Mathematical Finance (2012-2014), and ultimately a PhD in Mathematical Statistics (2014-2018). Prior to his current position at LMU Munich, he held assistant professor positions at TU Delft (2021-2022) and Leiden University (2019-2021). Professor Nagler's research focuses on developing novel statistical methods with theoretical guarantees and scalable algorithms. His work spans high-dimensional dependence modeling, particularly using vine copulas, statistical machine learning, time series and functional data analysis, and statistical computing. He emphasizes creating methods that can be practically implemented and applied to solve real-world problems across diverse domains. An analysis of Nagler's recent publications reveals a strong emphasis on vine copula methodology, uncertainty quantification in machine learning, and applications to climate science and epidemiology. His work bridges theoretical statistics with practical implementation, often resulting in open-source software tools that make advanced statistical methods accessible to practitioners. The interdisciplinary nature of his research is evident in collaborations spanning climate modeling, healthcare, and finance. While specific awards are not detailed in the available information, Nagler's research impact is evident through his significant contributions to statistical methodology and his active engagement with the research community through open-source software development. As a principal investigator at MCML and Professor at LMU Munich, Nagler leads a research group focused on advancing statistical methodology for complex data analysis. His GitHub profile indicates active collaboration with students and researchers, with several followers from LMU Munich and other institutions. His research program appears to be well-funded through the MCML and university resources, supporting both methodological development and application-focused projects. Nagler maintains strong ties with the computational statistics community through his leadership of the VineCopula and pyvinecopulib projects, which provide essential tools for dependence modeling. His work with the Munich Center for Machine Learning positions him at the forefront of interdisciplinary research combining statistical theory with practical machine learning applications.