Find Academic Supervisors

• Random Matrices
• Limit Theorems
• Mathematical Diffraction Theory
• +1 more

Prof. Dr. Holger Kösters is affiliated with the Institute of Mathematics at the University of Rostock , where he focuses on probability theory and its intersections with mathematical physics and data science. His research spans random matrix theory, spectral distributions, and diffraction phenomena in stochastic systems. University: University of Rostock School: Faculty of Mathematics and Natural Sciences Department: Institute of Mathematics Email: holger.koesters@uni-rostock.de Research Interests: Random matrices and their applications, limit theorems in high-dimensional statistics, mathematical diffraction theory, and probabilistic methods in data science. His work explores universal patterns in eigenvalue distributions, connections to number theory, and statistical mechanics. Publication Trends: Recent articles emphasize random matrix products, spectral asymptotics, and applications to diffraction theory. Areas include polynomial ensembles, free probability, and probabilistic models for point processes. Academic Role: As a professor, he contributes to research and teaching in probability and mathematical statistics, with co-authorships in journals like Annals of Probability and Communications in Mathematical Physics .

Claudia KlüppelbergView profile

Professor

• Applied Probability
• Mathematical Statistics
• Financial Risk Management
• +3 more

Claudia Klüppelberg is a Professor and Chair of Mathematical Statistics at the Center for Mathematical Sciences, Technische Universität München (TUM). Her academic journey includes positions at ETH Zurich, University of Mainz, and TUM since 1997. She holds a Carl von Linde Senior Fellowship at TUM-IAS, focusing on Risk Analysis and Stochastic Modeling. Her research bridges applied probability, statistics, and their applications in finance and insurance, emphasizing extreme value theory, risk processes, and stochastic networks. She has received prestigious awards such as the New Frontiers in Risk Management Award (2007) and Cross of Merit (2001). Her work addresses real-world challenges in financial risk management, including systemic risk in networks and operational risk modeling. Her recent publications explore causal analysis of extreme risks in networks, max-linear models, and Bayesian networks for extreme events. She contributes to academic leadership as an editorial board member and advisor, promoting interdisciplinary stochastic sciences.

• Stochastic Analysis
• Random Walks
• Percolation Theory
• +1 more

Sebastian Andres is Professor of Stochastics at TU Braunschweig's Institute of Mathematical Stochastics. His research focuses on random processes in disordered media, including homogenization of random walks, percolation theory, and heat kernel analysis. He holds a habilitation from University of Bonn and previously held positions at University of Cambridge and University of Manchester. Research Themes: Quantitative homogenization, invariance principles for degenerate random walks, and applications to mathematical physics. Teaching: Current courses include Probability Theory and Discrete Financial Mathematics; authored lecture notes on Martingale Theory for Finance.

Abdelhak M. ZoubirView profile

Professor

• Statistical Signal Processing
• Cooperative Sensor Communications
• Radar Imaging
• +5 more

Abdelhak M. Zoubir is a Professor of Signal Processing and Head of the Signal Processing Group at Technische Universität Darmstadt, Germany. He has held leadership roles including Head of the Department of Electrical Engineering and Information Technology (2012–2014 and 2020–2022), and President of the European Association for Signal Processing (EURASIP, 2017–2018). His research focuses on statistical signal processing with applications in radar imaging, biomedical engineering, and automotive systems. Zoubir has authored over 500 publications and is a Fellow of IEEE and EURASIP. He currently leads projects on radar communication integration, robust signal processing algorithms, and radiation-hardened sensor development. Education: Dipl.-Ing. (BSc/MSc) from Fachhochschule Niederrhein and Ruhr-Universität Bochum, followed by a Dr.-Ing. (PhD) in Electrical Engineering from Ruhr-Universität Bochum (1992). Research Interests: Bootstrap techniques, robust detection/estimation, cooperative sensor networks, radar for landmine detection, and automotive safety systems. He has pioneered methods in robust statistical signal processing, including low-rank matrix completion and sparsity-aware algorithms. Recognition: Recipient of the IEEE Meritorious Service Award (2018), IEEE Signal Processing Magazine Best Paper Award (2017), and the M. Barry Carlton Award (2014). He has been a keynote speaker at major conferences such as ICASSP and EUSIPCO, and served as Editor-in-Chief of the IEEE Signal Processing Magazine (2012–2014). Current Projects: Focus on automotive radar signal processing, radiation-hardened sensors (MALTA), and distributed learning robustness. His work bridges theoretical advancements with practical applications in defense, healthcare, and automotive industries.

Julien PoisatView profile

Lecturer

• Probability Theory
• Statistical Mechanics
• Disordered Systems
• +4 more

Julien Poisat is a Lecturer (equivalent to Assistant Professor) at CEREMADE, Paris-Dauphine University, where he has been affiliated since 2014. Previously, he was a postdoctoral researcher at Leiden University (2012-2014) and completed his Ph.D. at Lyon 1 University (2008-2012). His research focuses on probability theory and statistical mechanics, with specific interests in disordered systems, polymers, random walks, and large deviations. He investigates phenomena such as localization, pinning, and phase transitions in various models including copolymers, charged polymers, and random environments. His recent publications primarily explore rigorous analyses of stochastic systems, with recurring themes including large deviations for random walks, critical behavior of polymer models, and asymptotic properties of disordered systems. Research often involves mathematical techniques from renewal theory, potential theory, and weak convergence methods. He leads the ANR LOCAL grant (2022-2027) focused on localization phenomena in polymers and random walks. Currently advises two doctoral students: Nicolas Bouchot (2021-2024) and Elric Angot (2022-2025). Active in academic community, recently co-organized the 2023 Workshop on Random Walks, Localization and Reinforcement in Paris.

• Probability Theory
• Geometric Random Graphs
• Percolation Theory
• +3 more

Arne Grauer is a researcher in probability theory at the Department of Mathematics, University of Cologne. His work focuses on geometric random graphs, percolation, and stochastic processes in random environments. He completed his PhD in 2022 under Prof. Peter Mörters, exploring ultrasmallness and chemical distance in scale-free geometric random graphs. Education: PhD in Mathematics (2022, University of Cologne), Master’s in Mathematics (2017, University of Münster), Bachelor’s in Mathematics (2015, University of Münster). Research interests include understanding network structures and dynamics, particularly in scale-free and spatially embedded networks. His studies analyze ultrasmall graph distances, infection spread via contact processes, and preferential attachment models. Publications focus on mathematical physics and network science, with contributions to Communications in Mathematical Physics and Journal of Statistical Physics . He co-organized workshops on random geometric graphs and spatial networks.

• Asymptotic statistics
• Branching processes
• Limit theorems
• +4 more

Prof. Dr. Matthias Meiners is a faculty member at Justus-Liebig-University Giessen, holding the Chair of Stochastics within the Mathematical Institute. His research focuses on Asymptotic statistics Branching processes Limit theorems Random walks in random environments Regenerative processes Renewal theory Statistical mechanics . Recent publications analyze complex stochastic phenomena across branching processes, random walks, and renewal equations, with 2025 preprints on CMJ explosions and Markov renewal expansions. 2024 works examine active Brownian particle dynamics and supercritical branching fluctuations. Earlier studies address kinetic equations, martingale convergence, and percolation model speeds. Contact: Email: Matthias.Meiners@math.uni-giessen.de Phone: 0641 99-32100 Office: Room 216, Arndtstr. 2, 35392 Gießen

• Database Systems
• Information Retrieval
• Probabilistic Databases
• +4 more

Martin Theobald is a Professor in the Department of Computer Science at the University of Luxembourg's Faculty of Science, Technology and Communications. Previously affiliated with University of Ulm, Germany, his research spans database systems, information retrieval, and knowledge extraction with over 120 publications since 2002. His work bridges theoretical database foundations with practical applications in large-scale data processing. His research focuses on: Probabilistic and uncertain database systems Stream processing frameworks (notably the AIR architecture) Knowledge extraction from heterogeneous data sources Integration of machine learning with database systems Efficient query processing for structured and semi-structured data Recent publications demonstrate an evolving research trajectory toward real-time data stream processing with machine learning integration. His work on the AIR (Asynchronous Iterative Routing) framework and its extensions (TensAIR, OPTWIN) addresses critical challenges in concept drift detection, neural network training on streaming data, and efficient resource utilization. These contributions sit at the intersection of database systems, distributed computing, and machine learning, with applications in knowledge graph construction and question answering systems. Martin Theobald has mentored numerous researchers including Mauro Dalle Lucca Tosi, Alessandro Temperoni, and Vinu E. Venugopal, who have become active contributors to the database community. His collaborative network spans institutions across Europe, with frequent partnerships with researchers from University of Ulm, Max Planck Institute, and other European universities. His laboratory work focuses on developing scalable systems for processing evolving data streams, with particular emphasis on creating lightweight architectures that maintain high performance while minimizing resource consumption. Current projects involve integrating knowledge graphs with real-time analytics and developing adaptive systems that can handle concept drift in streaming environments.

• Educational Psychology
• Quantitative Research Methods
• Educational Effectiveness
• +6 more

Benjamin Nagengast is Professor of Educational Psychology and Deputy Director of the Hector Institute for Empirical Educational Research at the University of Tübingen. His work focuses on educational psychology, quantitative research methods, and motivation theory, with a particular emphasis on academic self-concept, intervention programs, and causal analysis in educational settings. He previously served as co-director of the LEAD Graduate School & Research Network and head of the LEADing Research Center until June 2022. His research spans topics including: Educational effectiveness and policy evaluation Motivational dynamics and value-belief interactions Multilevel modeling of educational outcomes Personality-achievement correlations Longitudinal studies of developmental trajectories While not explicitly listing scientific awards, his publications highlight methodological innovations and large-scale empirical studies. He mentors students through collaborative research projects, though individual advisees aren't named. His work frequently appears in top journals like Psychological Science and Journal of Educational Psychology , with recent articles examining detracking effects, twin comparison processes, and the interplay between self-concept and achievement. He maintains active collaborations with institutions like the University of Oxford and University of Jena.

Saharon RossetView profile

Professor

• Statistics
• Data Science
• Machine Learning
• +3 more

Saharon Rosset is a Professor and Chair in Modern Statistics and Data Science at the Department of Statistics and Operations Research, School of Mathematical Sciences, Tel Aviv University, since 2007. His expertise spans statistical learning theory, machine learning, and computational biology, with a focus on integrating statistical methods into biological and data science applications. Research Interests: Statistical Learning, Computational Biology, Genomics, Data Science Teaching: Statistics of Big Data, Statistical Genetics, Bootstrap Methods, Data Science His recent publications explore diverse applications including ATP production mechanisms, SARS-CoV-2 evolution, gene-environment interactions, and music structure analysis. He employs methodologies from high-dimensional statistics, random effects modeling, and multiple hypothesis testing. Scientific Awards : Best paper award at KDD-2011 KDD Cup 2007 Winner Best application paper award at KDD-02 He has mentored numerous PhD and MSc students, including Oren Yuval, Giora Simchoni, and Rajesh Karmakar. His collaborative work appears in journals like PNAS , Biometrics , and Journal of Machine Learning Research .

Ghassan KarameView profile

Professor

• Blockchain Security
• Machine Learning Security
• Platform Security
• +3 more

Prof. Dr. Ghassan Karame is a Full Professor of Computer Science at the Ruhr-University Bochum , leading the Chair for Information Security. He is a Principal Investigator in the Cluster of Excellence CASA and Director of the Horst Goertz Institute for IT Security since October 2023. Additionally, he serves as a part-time Chief Scientific Advisor at NEC Laboratories Europe . His research focuses on blockchain security and privacy , platform/storage security , and machine learning security . He has contributed to improving cryptocurrency protocols, hardware-assisted secure systems, and decentralized trust mechanisms. Education : PhD in Computer Science from ETH Zurich (2011) His recent work explores federated learning security, side-channel attacks in TEEs, and practical blockchain scalability solutions. He has served on over 50 program committees and held editorial roles at IEEE TDSC and IEEE TIFS. Notable Awards : 2024 Outstanding Editorial Board Member Award (IEEE Signal Processing Society) 2020 Value Realization Award (NEC) 2019 IEEE TC Best Paper Award Degree among top 2% authors (Stanford-Elsevier) His teaching includes courses on systems security, blockchain security, and practical ML security, with thesis projects in blockchain, ML, and platform security. He leads the Information Security Chair , part of major research initiatives like CASA and HGI.

• Urban Climate Research
• Thermal Comfort
• Urban Air Quality
• +6 more

Prof. Dr. Uwe Schlink is a leading Professor at the Institute of Meteorology, University of Leipzig, and Senior Researcher at the Department of Urban & Environmental Sociology, Helmholtz Centre for Environmental Research - UFZ. His work focuses on urban climate research , thermal comfort , urban air quality , and statistical modelling with Bayesian inference. He leads the working group on urban climate and personal exposure, bridging environmental science with societal resilience. Affiliation: University of Leipzig (since 2009) and UFZ (since 2013) Research Themes: Urban heat islands, personal exposure to environmental stressors, statistical climate models, and health impacts of air pollution His research spans environmental health , urban climatology , and resilient city planning , with significant contributions to understanding thermodynamic interactions between urban structures and climate. He has pioneered methods for high-resolution land surface temperature analysis and green infrastructure performance in mitigating heat stress. Recent publications (2023-2025) highlight his work on PM2.5-bound PAH exposure , anthropogenic heat impacts in Beijing, and Asian plateau climate dynamics . Collaborative projects address urban heat stress , green roofs , and health-focused urban planning .

Daria Katharina BendenView profile

Research Fellow

• Student motivation and achievement
• Educational and occupational choices in STEM
• Teachers' professional competencies
• +2 more

Daria Katharina Benden serves as a Postdoctoral Researcher at the University of Bonn's Bonn Center for Teacher Education (BZL) within the Empirical Educational Research and Educational Psychology research group led by Prof. Dr. Fani Lauermann. She also holds a Postdoctoral Fellowship at the College for Interdisciplinary Educational Research (CIDER). Her academic journey includes bachelor's and master's degrees in Mathematics and Physics Teacher Education from the University of Bonn, followed by a summa cum laude Ph.D. in Psychology from TU Dortmund University in 2022. Her research focuses on critical aspects of educational psychology in STEM contexts, particularly examining student motivation trajectories, educational and occupational choices in math-intensive fields, and teachers' professional competencies. She investigates how digital self-testing and self-reflection tools can support students in postsecondary education transitions, along with assessing digital competencies required by both students and instructors in modern educational environments. Her work reveals important gender differences in motivational variability and identifies early warning signs of academic disengagement through short-term motivational assessments. Benden's publication portfolio demonstrates strong methodological rigor through longitudinal analyses, random intercept cross-lagged modeling, and network analysis approaches applied to large datasets from math-intensive study programs. Her research consistently addresses the critical challenge of student retention in STEM fields through examination of expectancy-value dynamics, test engagement factors, and motivational alignment patterns. Notably, her work shows that greater motivational alignment between expectancy and value beliefs can paradoxically indicate disengagement in challenging academic contexts. International awards for young scholars from European Association for Research on Learning and Instruction (EARLI SIG-8) International awards for young scholars from American Educational Research Association (AERA MotSIG) As an active researcher, Benden regularly presents at major international conferences including AERA and EARLI, with upcoming presentations scheduled through 2025. Her collaborative work spans multiple institutions including TU Dortmund University and involves significant contributions to theoretical frameworks in educational psychology, particularly regarding situated expectancy-value theory applications in higher education STEM contexts.

• Probability Theory
• Branching Processes
• Products of Random Matrices
• +7 more

Prof. Dr. Sebastian Mentemeier is a permanent Professor (W2) in the Department of Mathematics 2 at the Institute for Mathematics, Mathematics Education and Computer Science Education, University of Hildesheim, Germany, a position he has held since October 2019. He also holds significant administrative roles as Vice Dean and Dean of Studies in Faculty 4 (Mathematics, Natural Sciences, Economics & Computer Science), and is a member of the Institute's Board and the Central Commission for Studies and Teaching. His research is centered on advanced probability theory, with a focus on branching processes, products of random matrices, and extreme value theory for time series. His research interests include: Non-Gaussian limit theorems Branching processes, particularly Multitype Branching Random Walks Products of random matrices Extreme value theory for time series Heavy-tailed random variables Conditional limit theorems His recent publications, spanning from 2012 to 2022, demonstrate a consistent and high-impact research trajectory in theoretical probability. The articles reveal a strong trend in analyzing the asymptotic behavior of complex stochastic systems, particularly those defined by recursive equations and random matrix products. His work frequently intersects with statistical mechanics and time series analysis, often investigating the tail behavior and limit laws of solutions to stochastic fixed-point equations, with a particular emphasis on heavy-tailed and multivariate settings. Prof. Mentemeier has been a Principal Investigator on two major DFG projects: 'Nonlinear stochastic fixed-point equations with applications in statistical mechanics' (2017-2023) and 'Products of Random Matrices, Noncommutative Branching Random Walks, and Multitype Branching Random Walks in Random Environments' (since 2021). He is an active member of the academic community, serving as a referee for journals such as Stochastic Processes and their Applications and Journal of Theoretical Probability , and has co-organized international conferences on recursive stochastic processes and branching models. He supervises PhD and Master's students, although specific names are not listed on his profile. He has led a research group within the Department of Mathematics 2 and is a key member of the Institute's academic board. His work is conducted within the broader context of the Faculty of Mathematics, Natural Sciences, Economics & Computer Science at the University of Hildesheim.

Giacomo FrancisciView profile

Researcher

• Branching processes
• Depth functions
• Empirical processes
• +3 more

Dr. Giacomo Francisci is an academic staff member at the Institute of Financial Mathematics, part of Ulm University's Faculty of Mathematics and Economics. His research focuses on advanced statistical methodologies including branching processes, depth functions, empirical processes, nonparametric statistics, and stochastic process analysis. He has presented at major international conferences such as the International Conference on Robust Statistics (Stresa, 2025) and German Probability and Statistics Days (Dresden, 2025). His work spans theoretical developments in statistical depth functions and their applications to clustering, as well as practical applications like predicting student performance and analyzing movement patterns in Alzheimer's patients. Key contributions include studies on branching processes in random environments and methodological advancements in statistical depth for abstract metric spaces. Francisci collaborates internationally, with presentations at institutions like George Mason University (USA) and the CMSstatistics conference series. He is affiliated with Scopus and Web of Science, and can be contacted at giacomo.francisci@uni-ulm.de. His office is located in Room 2.27 at the Institute of Financial Mathematics.