Find Academic Supervisors

• Algorithm Design
• Transportation Systems
• Route Planning
• +3 more

Sabine Storandt is a Lecturer at the Department of Computer Science, University of Freiburg, with a focus on algorithm design and transportation systems. She has contributed significantly to research in route planning, electric vehicle navigation, and public transit optimization. Her work emphasizes practical applications of theoretical algorithms in real-world scenarios. Her research interests include algorithms for vehicle navigation, route optimization, and facility location problems. She has received notable awards, including the Best Paper Award at VLDB 2014 and the INFOS Award for her PhD thesis on 'Algorithms for Vehicle Navigation.' In teaching, she has led courses such as Information Retrieval (as a tutor), Randomized Algorithms (lecture + tutorial), and Information Extraction (seminar). She has also collaborated on projects like DORC (Distributed Online Route Computation) and Enabling E-Mobility, addressing challenges in transportation and energy efficiency. Her recent publications highlight advancements in electric vehicle infrastructure, public transit planning, and efficient route algorithms, reflecting her expertise in bridging theoretical computer science with practical transportation solutions.

Hajo LeschkeView profile

Professor

• Theoretical Physics
• Quantum Mechanics
• Statistical Physics
• +3 more

Prof. Hajo Leschke is a Professor of Theoretical Physics at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), affiliated with the Department of Physics, specifically Theoretical Physics I. His research group focuses on quantum and statistical physics, with emphasis on disordered systems, entanglement, and mathematical foundations of quantum mechanics. Research interests span: Theoretical Quantum Systems : Quantum spin glasses, fermionic entanglement, and disorder-driven phase transitions. Statistical Mechanics : Scaling behavior of entropy, free energy in spin models, and thermodynamic limits. Mathematical Physics : Rigorous analysis of Schrödinger operators, spectral theory, and path integral formalisms. His publications (2000–2023) predominantly explore quantum disorder effects, entanglement entropy scaling in Fermi gases, and applications of operator theory to statistical physics. Trends include mathematical rigor in quantum models and interdisciplinary links to quantum information science. Prof. Leschke advises multiple students, including current researchers (e.g., Helmut Fink, Sebastian Rothlauf) and former doctoral/diploma candidates. No awards or grants are detailed in available texts. His group maintains active collaborations, notably with international institutions like the American Institute of Mathematics.

Raphael TwerenboldView profile

Professor

• Cardiology
• Oncology
• Neuroscience
• +4 more

Raphael Twerenbold is a Professor at the Clinic and Polyclinic for Cardiology, University Medical Center Hamburg-Eppendorf. His research spans Cardiology , Oncology , and Neuroscience , with a focus on cardiovascular biomarkers , myocardial infarction , and brain aging . He leads studies in population-based cohorts like the Hamburg City Health Study and collaborates on international trials (GMMG-CONCEPT, DEDICATE-DZHK6). University: University Medical Center Hamburg-Eppendorf School: Medical Faculty Department: Cardiology Academic Rank: Professor Research trends in his 15+ recent articles (2023–2025) include biomarker development for myocardial infarction, brain aging in vascular disease, and occupational risk factors for coronary heart disease. His work integrates epidemiological data with multimodal imaging and machine learning . Notable projects involve the GRACE scores for coronary angiography timing, transcatheter aortic valve trials, and neuroimmune interactions in post-COVID conditions. He contributes to Clinical Research Cardiology and JAMA literature.

Denis BelomestnyView profile

Professor

• Statistics of Stochastic Processes
• Financial Mathematics
• Econometrics
• +5 more

Denis Belomestny is a Professor of Applied Stochastics at the Department of Mathematics, University of Duisburg-Essen. His academic journey includes a PhD from Lomonosov Moscow State University (2002), postdoctoral work at the University of Bonn, and research positions at WIAS Berlin and Humboldt University Berlin. He currently leads research at the intersection of stochastic processes and financial mathematics. PhD in Mathematics, Lomonosov Moscow State University (2002) W3 Professorship in Applied Stochastics, University of Duisburg-Essen (2011–present) His research focuses on statistics of stochastic processes , optimal stopping/control , and Monte Carlo methods , with applications in financial mathematics and machine learning. Key collaborations include work with John Schoenmakers on multilevel approximation algorithms and Alexey Naumov on variance reduction techniques. Recent publications (2025–2023) explore deep neural networks for SDEs , generative adversarial networks , and nonparametric estimation in complex stochastic models. His work spans stochastic differential equations , financial derivatives pricing , and machine learning-driven statistical inference . He supervises doctoral students, including Sascha Nolte (research: robust optimal stopping without reference models). Current projects involve McKean-Vlasov SDEs , gamma-driven processes , and reinforced optimal control .

Eckehard OlbrichView profile

Researcher

• Computational Social Science
• Text and network analysis for social media data
• Information theoretic approach to complex systems
• +7 more

Eckehard Olbrich is a Group Leader and Researcher at the Max Planck Institute for Mathematics in the Sciences (MiS) in Leipzig, Germany. His work bridges mathematics, information theory, and social science with a focus on complex systems analysis. He has coordinated major European research projects including SoMe4Dem (Social Media for Democracy) and ODYCCEUS (Opinion dynamics and cultural Conflict in European Spaces). His educational background includes a PhD in theoretical solid-state physics from the Technical University Dresden (1995), followed by postdoctoral work at the Max Planck Institute for the Physics of Complex Systems in Dresden and research at the University of Zürich. Since 2004, he has been affiliated with the Max Planck Institute for Mathematics in the Sciences. Olbrich's research spans computational social science, information theory, and complex systems. He applies information-theoretic approaches to analyze social media data, complex networks, and human sleep EEG patterns. His work on information decomposition, multi-level systems, and time series analysis has produced significant contributions to understanding complex phenomena across disciplines. He has developed methods for analyzing polarization, opinion dynamics, and network structures in social systems. His publication record shows a strong trend toward interdisciplinary research combining information theory with social and biological systems. Recent work focuses on computational social science applications, particularly analyzing polarization and issue alignment on social media platforms, while maintaining connections to fundamental information theory and complex systems research. Olbrich has collaborated extensively with researchers including Sven Banisch (Karlsruhe Institute for Technology), Peter Achermann (University of Zürich), David Wolpert (Santa Fe Institute), and Jürgen Jost at MiS. His research has been supported by major funding programs including Horizon Europe, Horizon 2020, and the DFG. He has taught courses on Complex Systems Methods and Data Analysis and Modeling at the University of Potsdam, and has contributed to the development of TISEAN, free software for nonlinear time series analysis. His current research continues to explore the intersection of information theory, network science, and computational social science with applications to understanding democratic processes in the digital age.

• Computational Algebra
• Commutative Algebra
• Applied Algebraic Geometry
• +3 more

Daniele Taufer is a postdoctoral researcher affiliated with the NUMA research unit at KU Leuven (Belgium) since 2022, supported by the FWO (Flemish Fund for Scientific Research) under project 12ZZC23N. Previously, he worked at CISPA (Germany) from 2020 to 2022 as a postdoc on elliptic curve cryptography within the ERC-669891 project, supervised by Antoine Joux. Education: Ph.D. in Mathematics (2016–2020), University of Trento (IT), cum laude, thesis: "Elliptic Loops" (supervised by Massimiliano Sala) Master in Mathematics (2014–2016), University of Duisburg-Essen (DE) and University of Leiden (NL) via the ALGANT double-degree program, thesis: "Algebraic aspects of the Number Field Sieve" (supervised by Hendrik W. Lenstra) Bachelor in Mathematics (2011–2014), University of Padova (IT), thesis: "Gröbner bases and applications" (supervised by Alberto Tonolo) Research interests span computational and commutative algebra, applied algebraic geometry, and cryptographic applications. Key areas include symmetric tensor decomposition (Waring, tangential, Chow, cactus ranks), effective decomposition algorithms (apolarity, Hankel operators), and algebro-geometrical properties of apolar schemes. His work bridges theoretical algebra and practical cryptography, particularly focusing on elliptic curves and their applications in blockchain and isogeny-based systems. Recent scientific contributions examine decompositions of symmetric tensors, elliptic curve discrete logarithm problems (ECDLP), and group structures over discrete rings. His algorithmic developments leverage apolarity and Hankel operators for computational efficiency. Scientific accolades: Maître de conférences qualification (2025) in Mathematics and Applied Mathematics sections Member of the SIAM Activity Group on Algebraic Geometry Labs and teams include the NUMA group at KU Leuven and the ERC-669891 project at CISPA.

Xiao ZhangView profile

Assistant Professor

• Trustworthy Machine Learning
• Statistical Machine Learning
• Convex Optimization
• +4 more

Xiao Zhang is a tenure-track faculty member at the CISPA Helmholtz Center for Information Security and a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). He obtained his Ph.D. in Computer Science from the University of Virginia in 2022 under Prof. David Evans, following an M.S. in Statistics from the same institution and a B.S. in Mathematics from Tsinghua University. Research Focus Zhang's research spans foundational and applied aspects of trustworthy machine learning, with emphasis on: Security vulnerabilities in ML systems (adversarial attacks, data poisoning) Robustness guarantees for learning algorithms Optimization methods for deep learning Privacy-preserving techniques Multimodal and physical-world applications His recent work focuses on developing theoretically-grounded defenses against emerging threats to AI systems. Publication Trends Zhang's 15 most recent publications (2019-2025) demonstrate consistent focus on adversarial machine learning, with growing emphasis on: 1) Physical-world attacks/defenses, 2) Multimodal model security, and 3) Certified robustness guarantees. The work consistently appears at top-tier venues (NeurIPS, ICLR, CCS). Academic Activities He actively recruits students and collaborators for trustworthy ML research, with open positions for PhD candidates and research assistants. No awards or specific grant information is documented in the provided materials.

Ana DjurdjevacView profile

Assistant Professor

• Numerical Analysis
• Stochastic Processes
• Partial Differential Equations
• +3 more

Ana Djurdjevac is an Assistant Professor in the Department of Numerical Analysis and Stochastics at the Freie Universität Berlin , within the Department of Mathematics and Computer Science. Her research focuses on numerical analysis, stochastic processes, and partial differential equations, with particular emphasis on uncertainty quantification and mathematical modeling in evolving domains. She teaches advanced courses such as Numerical Methods for Stochastic Differential Equations and Stochastik I , reflecting her expertise in computational methods and probabilistic frameworks. Her work integrates theoretical analysis with practical numerical techniques, addressing challenges in domains such as fluid dynamics, quantum systems, and biological surface fluctuations. Recent contributions include studies on hybrid algorithms for particle systems, rough homogenization in stochastic dynamics, and synchronization mechanisms in conservation laws. Djurdjevac actively participates in academic events, including the 2025 SIAM Conference on Computational Science and Engineering, and collaborates on projects involving quasi-Monte Carlo methods for Bayesian inversion and domain decomposition techniques. Professional activities highlight her role in shaping emerging fields like stochastic PDEs on evolving domains and feedback loops in agent-based models. While no specific awards are listed, her prolific publication record and teaching roles underscore her contributions to computational science and applied mathematics.

Ehrhard BehrendsView profile

Professor

• Functional Analysis
• Probability Theory
• Stochastic Processes
• +4 more

Prof. Ehrhard Behrends is a Professor of Mathematics at the Department of Mathematics and Computer Science, Freie Universität Berlin. His research focuses on functional analysis, probability theory, and stochastic processes. He is also deeply involved in mathematical education and public outreach, having authored numerous popular mathematics books and articles. Key roles include leading the European Mathematical Society's Raising Public Awareness committee (2009–2015) and developing the mathematics portal mathematics-in-europe.eu . Behrends has organized major exhibitions like 'Mathematics for all the Senses' and contributed to initiatives such as the Year of Mathematics 2008. His work bridges advanced research with accessible communication, reflected in his books on Markov chains, analysis textbooks, and the Five Minutes of Mathematics column.

Carlos CinelliView profile

Assistant Professor

• Causal Inference
• Statistics
• Machine Learning
• +4 more

Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington. He is also a Data Science Fellow at the eScience Institute and Affiliate Faculty at the Center for Statistics and the Social Sciences. He obtained his Ph.D. in Statistics from the University of California, Los Angeles, where he was advised by Chad Hazlett and Judea Pearl. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences. Key interests include: Causal inference challenges in social and health sciences Intersections of causality with machine learning and AI Sensitivity analysis for omitted variable bias Robust statistical methods for observational studies Instrumental variables and Mendelian randomization Generalizability of experimental findings His publications demonstrate a consistent focus on developing practical sensitivity analysis tools and advancing causal methodology, with recent work emphasizing applications in machine learning and econometrics. Honors include: Best Paper Award at SBE 2024 in Econometrics UCLA Dissertation Year Fellowship (2020) He actively advises PhD students and has received research funding from: NSF/MMS Royalty Research Fund He leads development of several open-source software packages for sensitivity analysis and maintains active collaborations with researchers at UCLA and other institutions.

Steffen WinterView profile

Lecturer

• Fractal Geometry
• Geometric Measure Theory
• Stochastic Geometry
• +1 more

Steffen Winter is a Lecturer (PD Dr.) at the Institute of Stochastics, Karlsruhe Institute of Technology (KIT). His research specializes in Fractal Geometry, Geometric Measure Theory, Stochastic Geometry, and Dynamical Systems. He leads the DFG-funded project Scaling of curvature measures and the modified Weyl-Berry conjecture and serves as Principal Investigator for project 12 ( Morphometric Roughness of Nanostructured Surfaces ) within the DFG Priority Programme 2265 (Random Geometric Systems). Winter's work explores the mathematical foundations of fractals, stochastic processes, and geometric measurements. Key themes include Minkowski content, curvature measures, self-similar sets, percolation models, and applications to materials science and geoscience. Recent publications emphasize fractal dimensionality, surface roughness quantification, and stochastic convergence in complex systems. He teaches advanced courses including Stochastic Geometry , Fractal Geometry , and Markov Chains , and mentors students through seminars and proseminars. No awards or research grants besides DFG projects are documented.

Markus MaucherView profile

Researcher

• Computational Biology
• Bioinformatics
• Machine Learning
• +4 more

Markus Maucher is a Subject Advisor in the Department of Computer Science at the University of Ulm . He has taught exercises for courses like Einführung in die Informatik I (EidI 1) and Einführung in die Informatik II (EidI 2) across multiple semesters from 2004 to 2022. His research focuses on Computational Biology , Boolean Networks , and Ant Colony Optimization , with applications in Machine Learning , Gene Expression Analysis , and Evolutionary Algorithms . His publications span journals such as Bioinformatics , IEEE/ACM Transactions on Computational Biology and Bioinformatics , and Computational Statistics , as well as conferences like the Genetic and Evolutionary Computation Conference . He holds a PhD in Computer Science, evidenced by his 2009 dissertation and subsequent book on non-perfect randomness in probabilistic algorithms . Maucher has collaborated with researchers including H.A. Kestler , U. Schöning , and C. Müssel . The keywords from his recent work include Computational Biology , Boolean Networks , and Algorithm Design . His sub-fields encompass Probabilistic Algorithms , Correlation Analysis , and Feature Selection . Contact details: markus.maucher@uni-ulm.de

• Probability Theory
• Mathematical Finance
• Stochastic Analysis
• +4 more

Peter K. Friz is Einstein Professor in Mathematics at Technische Universität Berlin (TU-Berlin), where he leads research in the Probability Theory and Mathematical Finance group. He is also affiliated with the Weierstrass Institute for Applied Analysis and Stochastics (WIAS), a major research institute in Berlin focused on applied mathematics. Professor Friz's research spans several interconnected areas of mathematics and finance: Stochastic analysis and rough path theory Quantitative finance and volatility modeling Partial differential equations driven by rough paths Applications of rough path theory to financial mathematics His recent publications demonstrate a continued focus on advancing rough path theory and its applications. The research trends show increasing sophistication in handling nonlinear rough differential equations, connections to machine learning through signature methods, and deeper applications to financial modeling, particularly in volatility analysis. His work bridges pure mathematical theory with practical financial applications, maintaining relevance to industry problems while advancing theoretical foundations. Professor Friz has received significant recognition for his work: ERC Starting Grant "Rough path theory, differential equations and stochastic analysis" (2010-2016) ERC Consolidator Grant "Geometric aspects in pathwise stochastic analysis and related topics" (2016-2021) Einstein Professorship at TU-Berlin He actively mentors students and researchers, with a list of former PhD students available through his academic profile. Professor Friz coordinates the DFG research unit "Rough paths, stochastic partial differential equations and related topics" (2016-2019) and has secured substantial funding from the European Research Council, DFG, and other sources. His research group regularly hosts seminars and collaborates with institutions worldwide, including recent invited courses at Cambridge, Paris (IHP), and Bonn (HIM). Professor Friz maintains active research collaborations through his position at TU-Berlin and affiliation with WIAS, contributing to Berlin's status as a hub for mathematical finance and stochastic analysis in Europe.

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +3 more

Lisa Hartung is a Full Professor (W3) at Johannes Gutenberg University Mainz's Department of Mathematics since 2025, leading research in stochastic processes. She previously held Associate (2019-2025) and Assistant Professor (2019) positions at the same institution and served as a Courant Instructor at NYU's Courant Institute (2016-2018). Her educational background includes a PhD from Bonn University (2016) under Prof. Anton Bovier in Probability Theory. Key research interests span branching Brownian motion , Gaussian free fields , random energy models , and stochastic processes on evolving networks , with applications in statistical mechanics and mathematical physics. Her work frequently addresses extremal processes, phase transitions, and complex temperature phenomena. Analysis of her recent publications reveals dominant trends in log-correlated random fields, variable-speed branching processes, and hard-wall boundary conditions. Articles consistently explore asymptotic behavior, maximum processes, and structural properties of stochastic systems, with increasing interdisciplinary connections to computer science through data stream modeling. Scientific recognition includes: Förderpreis der Fachgruppe Stochastik (2018) Hartung secures substantial research funding as PI for multiple high-impact projects: the ANR-DFG grant Random Energy Models (2020), DFG's SPP 2265 Random Geometric Systems (2020), TRR146 Multiscale Simulation Methods (2020), and Carl-Zeiss project TOPML (2022). She also serves on the executive board of DMV Fachgruppe Stochastik (since 2023) and coordinates Jugend trainiert Mathematik for grades 8/9. Her research group participates in the DFG-funded network Stochastic Processes on Evolving Networks and collaborates internationally through workshops on interacting particle systems and stochastic population models.

• Latent Variable Models
• Dynamic Latent Variable Models
• Latent Class Methods
• +8 more

Augustin Kelava is a Professor at the Department of Quantitative Methods, Eberhard Karls University of Tübingen. He has held this position since 2018 and leads the Methods Center as Managing Director. Previously, he was Professor at the Hector Institute for Empirical Educational Research (2013-2018) and Junior Professor at Technical University of Darmstadt (2011-2013). PhD in Psychology (Goethe University Frankfurt, 2009) Diploma in Psychology (Goethe University Frankfurt, 2004) Kelava specializes in latent variable modeling, machine learning in social sciences, and educational research. His work spans dynamic latent class models, Bayesian regularization techniques, and prediction of human behavior using intensive longitudinal data. He contributes to psychometric theory (e.g., item response theory extensions) and applies these methods to diverse fields including sports science and emotion regulation. Editor of "Testtheorie und Fragebogenkonstruktion" (3rd ed., Springer, 2020) Key researcher in the Cluster of Excellence "Machine Learning in Science" Active in methodological conferences (FGME 2017, SEM 2019) Review activities for 20+ journals and foundations including Psychometrika, DFG, and SNSF His recent publications focus on integrating machine learning with psychometrics, addressing identifiability in complex models, and evaluating personality assessment validity for large language models. He collaborates with researchers across psychology, education, and computational fields.