Yannick Rudolph, M.Sc., is a Research Associate at the Institute for Business Information Systems (IIS) within Leuphana University of Lüneburg. His work focuses on Machine Learning , Artificial Intelligence , and Data Science , with particular emphasis on multiagent systems, explainability, and network modeling. His research interests span Temporal and spatiotemporal modeling of complex systems Deep learning architectures (CNNs, VAEs, GNNs) Information propagation analysis in neural networks AI applications in sports analytics and digital transformation Recent publications highlight trends in masked autoencoders , event classification in soccer , and conditional dependency modeling , reflecting his expertise in integrating theoretical machine learning with real-world application domains. Contact: yannick.rudolph@leuphana.de | Office: C 4.318b, Universitätsallee 1, Lüneburg, Germany
Miriam Schulte is a Professor at the Institute for Parallel and Distributed Systems (IPVS) at the University of Stuttgart . As Dean of Studies SimTech , she leads academic programs in simulation technology. Her research focuses on high-performance computing , multi-physics simulations , and scientific software development , with significant contributions to coupling libraries like preCICE and biophysical frameworks like OpenDiHu . Key Research Areas: High-Performance Computing (HPC) Multi-physics and Fluid-Structure Interaction (FSI) Sparse Grids and Hierarchical Numerical Methods Machine Learning in Simulation Software Parallel and GPU-Accelerated Algorithms Advising: Guided student projects on quantum neural networks , GPU-optimized sparse grids , and SYCL-based HPC frameworks . Coordinated SimTech Research Modules and IPVS/SGS team initiatives. Software Leadership: Maintains preCICE (coupling library for multi-physics) Develops OpenDiHu (neuromuscular simulations) Advances PLSSVM (parallel SVM library) and SG++ (sparse grids) Her recent publications (2022–2025) emphasize machine learning integration with multi-physics simulations , including groundwater heat pumps , brain tumor modeling , and neuromuscular EMG prediction . She actively promotes open-source software sustainability and collaborative research infrastructure at the University of Stuttgart.
Conor Mayo-Wilson is an Associate Professor in the Department of Philosophy at the University of Washington , specializing in formal epistemology, logic, and philosophy of science. His research spans interdisciplinary methodologies bridging philosophy with statistics, game theory, and causal inference. Education: Ph.D., Philosophy, Carnegie Mellon University (2012) M.S., Logic and Computation, Carnegie Mellon University (2009) M.S., Mathematics, Carnegie Mellon University (2009) B.S., Mathematics, Stanford University (2006) B.A., Philosophy, Stanford University (2006) His research focuses on the intersection of epistemology , statistics , and causal inference , with notable contributions to robust Bayesianism, causal discovery algorithms, and the philosophical foundations of statistical methods. Recent articles address computational philosophy, measurement theory, and severe testing frameworks. Mayo-Wilson has taught courses including Introduction to Logic, Seminar in Epistemology, and Statistics and Philosophy of Voting. He is affiliated with the Center for Statistics in the Social Sciences at the University of Washington. Scientific Awards: Best Paper Award, 2014
Abel Rodriguez serves as Professor of Statistics and Chair of the Department of Statistics at the University of Washington, with affiliate memberships at the eScience Institute and Center for Statistics in the Social Sciences. His research encompasses: Bayesian statistical methodology development Nonparametric and machine learning techniques Spatio-temporal modeling for complex data Network analysis applications in social sciences Extreme value theory for engineering problems Methodological innovations target biological, social science, and engineering challenges through interdisciplinary collaboration at affiliated research centers. Scientific Awards: No awards or honors were documented in the provided materials. Advising and Grants: Student mentorship activities and grant funding details were not specified in the source text.
Fabian H. Sinz is a Professor at the University of Tuebingen, leading the Neuronal Intelligence Group . His research focuses on understanding how biological neuronal networks leverage model biases through architecture, nonlinearities, and dynamics to enhance robust inference and accelerate learning. He employs deep learning and system identification techniques on large-scale neurophysiological and anatomical data. His work spans theoretical and applied domains, including system identification , neuroscience , reinforcement learning , and medical AI . Recent studies explore bidirectional coding in visual cortical neurons, contrastive learning for neuroscience time-series, and foundational models predicting neural responses to novel stimuli. Key publications highlight advancements in functional connectomics , invariance manifold learning , and neural likelihood estimation . Collaborations with institutions like the University of Texas and Max Planck Institute underscore his interdisciplinary impact. Tools like LAMINR (Learning and Aligning Manifolds of Single-Neuron Invariances) demonstrate his contributions to open-source neuroscience research.
Prof. Dr. Benedikt Wirth is a Professor of Mathematics at the University of Münster, Germany, affiliated with the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science. He is an active researcher and educator specializing in optimization and calculus of variations, with significant contributions to mathematical imaging and shape analysis. His research interests include image processing, scientific computing, numerical analysis, optimization, shape spaces, geodesics in shape space, variational methods, elastic deformation, and optimal transport. Wirth has developed innovative mathematical frameworks for shape analysis, particularly focusing on Riemannian metrics for shape spaces and variational approaches to shape comparison and optimization. His recent publications (2023-2025) demonstrate continued leadership in mathematical optimization, with particular focus on PET reconstruction, dimension reduction techniques, manifold embeddings, and branched transport theory. His work bridges theoretical mathematics with practical applications in medical imaging and computer vision, showing particular strength in connecting geometric analysis with computational methods. CRC 1450 - A05: Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling CRC 1450 - A06: Improving intravital microscopy of inflammatory cell response by active motion compensation EXC 2044 - C1: Evolution and asymptotics EXC 2044 - C2: Multi-scale phenomena and macroscopic structures EXC 2044 - C3: Interacting particle systems and phase transitions EXC 2044 - C4: Geometry-based modelling, approximation, and reduction Prof. Wirth actively supervises numerous bachelor's and master's students, with over 40 theses completed under his guidance since 2015. His teaching portfolio includes courses on inverse problems, numerical methods for partial differential equations, shape spaces, optimization, and optimal transport. He has consistently maintained an active research program while contributing significantly to the education of the next generation of mathematicians.
Prof. Dr. Philipp Doebler is a faculty member at the Technical University of Dortmund in the Faculty of Statistics , holding the Chair of Statistical Methods in the Social Sciences . He serves as Dean of the Faculty of Statistics (2022–2024) and spokesperson for the interdisciplinary FAIR program since 2021. His Erdős number is 3, reflecting collaborations in mathematical research. Education: Mathematics (Diploma, 2001–2006), PhD in Set Theory (2010) Professional Roles: Research Associate (Münster), Principal Investigator (DFG-funded 2014), Visiting Professor (Mannheim/Ulm), Full Professor (Dortmund since 2016) His research focuses on quantitative psychological methods , combining psychometric models with machine learning and meta-analysis . Key areas include large-scale assessment , automated scoring , and agile intervention research , with applications in educational psychology and mental health. Recent publications (2024–2025) emphasize interpretable machine learning for psychometric tasks, robust automated scoring , and Bayesian modeling in educational assessments. His work bridges statistical theory with practical applications in divergent thinking analysis and AI trustworthiness . His working group includes M.Sc. Loreen Sabel and M.Sc. Stefan Inerle , with collaboration history at the University of Münster and visiting roles in Mannheim/Ulm. Office hours are held Wednesdays at 11am in Mathematics Room 746, with remote Zoom options available.
Manolis Zampetakis is an Assistant Professor of Computer Science at Yale University. Previously, he was a postdoc at UC Berkeley's EECS Department working with Michael Jordan, and earned his PhD from MIT's EECS Department under Constantinos Daskalakis. His research spans Theoretical Machine Learning, Statistics, Optimization, Computational Complexity, Game Theory, and Mechanism Design. He has received the ACM SIGEcom Doctoral Dissertation Award and a Google PhD Fellowship. Current affiliation: Yale University (Assistant Professor) Prior affiliations: UC Berkeley (Postdoc), MIT (PhD student), NTUA (Undergraduate) His research focuses on algorithmic game theory, robust statistics, and optimization challenges in machine learning. He explores computational complexity in multi-player games, truncated linear regression, and strategy-proof mechanisms. Recent work includes backdoor attacks in neural networks and jailbreaking black-box LLMs, with publications in top venues like NeurIPS, COLT, FOCS, and STOC. Notable scientific contributions have been recognized through awards and special issues. He co-organized workshops at FOCS 2018, WALE 2019, and WALE 2022. His students include Anay Mehrotra, Jane Lee, Katerina Mamali, Shuchen Li, and Nikolaos Koumpis, often co-advised with prominent researchers like Amin Karbasi and Tuomas Sandholm.
Clélia de Mulatier is an Assistant Professor at the University of Amsterdam , affiliated with both the Institute for Theoretical Physics and the Informatics Institute . She leads research at the intersection of statistical physics, information theory, and computer science , focusing on theoretical and numerical methods for complex systems . Her work spans collaborations with experimentalists in neuroscience and biology , and she actively participates in educational programs across multiple Dutch universities. Research Labs : Computational Soft Matter Lab, Computational Science Lab Affiliations : Dutch Institute for Emergent Phenomena (DIEP), Netherlands Platform Complex Systems (NPCS) Her research develops minimally complex spin models for high-order data analysis , applying exact Bayesian model selection to uncover hidden variable communities in binary datasets. This work has produced open-source tools like MinCompSpin and MinCompSpin_Greedy for different system sizes. Publications demonstrate expertise in tensor networks for dimensional reduction , epidemic modeling , and branching random walks in confined environments . Teaching includes Python programming , complex systems theory , and statistical inference for physics students across multiple institutions. She serves as program committee member for International Conference on Computational Science and organizes academic discussions through initiatives like Behind the CV: story from a Physicist .
Professor Ute Schmid is a Full Professor of Cognitive Systems at the University of Bamberg, where she has been a faculty member since September 2004. She leads the Cognitive Systems Group within the Bamberg Center of AI (BaCAI), focusing on creating AI systems that generate human-like explanations and reasoning processes. Her research bridges cognitive science and artificial intelligence to develop methods for explanation generation, inductive programming, and interactive machine learning. Professor Schmid's work emphasizes practical applications of explainable AI across diverse domains including image classification, medical diagnosis, and educational technologies. Her research on contrastive explanations, near misses, and human-AI alignment has significantly advanced the field of XAI. She has also pioneered research on AI literacy, recognizing the growing importance of basic AI understanding for responsible tool usage by non-experts. Her publication record demonstrates exceptional productivity and impact, with numerous articles in top-tier venues including Nature Machine Intelligence, IEEE Transactions on Visualization and Computer Graphics, and the Journal of Web Semantics. Her 2025 paper 'Aligning generalization between humans and machines' represents a significant theoretical contribution to understanding human-machine cognitive alignment. Professor Schmid actively contributes to gender diversity research in computer science through studies examining why women pursue PhDs in the field. She has also made important contributions to computing education, investigating how students acquire programming skills and how AI tools like code generators are integrated into learning processes. As an educator and researcher, Professor Schmid maintains strong international collaborations, with co-authors spanning multiple countries and institutions. Her interdisciplinary approach is evident in her diverse publication venues and collaborative work that bridges computer science, cognitive science, education, and application domains.
Dr. Sebastian Brandstäter serves as Lecturer at the Institute for Mathematics and Computer-Based Simulation, Bundeswehr University Munich since 2022. His academic trajectory includes research associate positions at Hamburg University of Technology (2021) and Technical University of Munich (2016-2021). His research focuses on: Scientific Machine Learning for biomechanical systems Uncertainty Quantification and Bayesian Inference Global Sensitivity Analysis of complex models Multi-Physics & Multi-Scale Modeling of biological tissues Open-source scientific software development Dr. Brandstäter's work centers on gastrointestinal biomechanics, particularly computational modeling of gastric electromechanics and motility. He has pioneered applications of Gaussian-process metamodelling for sensitivity analysis in vascular and gastric systems, and develops open-source frameworks (QUEENS, 4C) that enable efficient multi-query analysis of large-scale models. He actively supervises student theses on patient-specific modeling and computational biomechanics, teaches advanced numerical methods courses, and contributes to the scientific community through conference organization, peer review, and international collaborations. His recent work demonstrates increasing emphasis on data-driven surrogate modeling and solver-independent computational frameworks for biomedical applications.
Claus Munk is a Professor at Copenhagen Business School's Department of Finance, where he researches dynamic asset allocation, life-cycle financial planning, and housing economics. His work employs stochastic modeling to analyze consumption-investment decisions under uncertainty. Research interests focus on: Optimal portfolio strategies across life stages Housing market interactions with financial decisions Retirement savings mechanisms and decumulation Stochastic interest rate and income risk modeling Welfare implications of financial regulations Publication analysis reveals consistent focus on life-cycle finance since 1998, with recent emphasis on retirement systems (2019), mortgage structures (2018), and ETF-based portfolios (2024). Methodologically, his work combines theoretical frameworks with empirical validation using household data. Professor Munk maintains research collaborations with Goethe University Frankfurt economists and co-advises projects through Copenhagen Business School. His personal academic website is available at: http://sites.google.com/view/clausmunk/home
Prof. Michael Habeck is a faculty member at the University of Göttingen, specializing in structural biology and computational methods. His research focuses on cryo-EM data analysis, molecular modeling, and integrating machine learning for biomedical applications. Advisor to 10+ PhD theses (2016–2023) Expertise in cryo-EM structure determination and validation Develops probabilistic models for dynamic proteins and change-point detection algorithms His work bridges structural biology with computational innovation, enabling high-resolution analysis of macromolecular complexes and advancing diagnostic tools via machine learning. Recent projects include CRISPR-edited cardiomyocyte models and super-resolution microscopy techniques. Key research areas span: Structural Biology: Cryo-EM, protein conformational changes Computational Methods: Bayesian modeling, tomography algorithms Machine Learning: Cardiac disease prediction, genomic data analysis
Matteo Camilli is an Associate Professor in the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano, Italy, where he leads research in software engineering and verification. His academic journey includes positions as Assistant Professor at Free University of Bozen-Bolzano and postdoctoral research at the University of Milan and University of Bergamo. His educational background includes a PhD in Computer Science (2015), MSc in Computer Science (2012), and BSc in Computer Science (2009), all from the University of Milan. His doctoral research focused on combining advanced abstraction techniques and big data approaches to address state explosion problems in formal verification. Camilli's research primarily centers on software verification, testing, and methods to improve dependability of autonomous, cyber-physical, service-based, and ML-enabled critical systems. His work spans formal methods, model-based testing, uncertainty quantification, and design-time/runtime verification with applications to complex distributed systems. His recent publications reflect a growing focus on explainable self-adaptation, quality assurance for LLM-based systems, and managing uncertainty in adaptive systems. His publication record includes papers in top journals (TOSEM, TAAS, JSS, EMSE) and conferences (ICSE, ISSRE, ICST, ICSA). He serves on program committees for prestigious conferences including ICSE, ICSA, ICST, and ECSA, and is on the steering committee for the International Workshop on Formal Approaches for Advanced Computing Systems (FAACS). Camilli actively contributes to the academic community through conference organization, including serving as Program Committee Member for numerous conferences and as Program Co-Chair for the Software Architecture track at ACM SAC. He also serves as guest editor for special issues on automated testing and dependable AI systems. His teaching portfolio at Politecnico di Milano includes Software Engineering 2, Software Engineering for Automation, and Distributed Software Development. Previously at Free University of Bozen-Bolzano, he taught Systems Engineering and Verification and Reliability for Dependable Systems.
Professor Christoph Hanck serves as Chair of Econometrics at the University of Duisburg-Essen's Faculty of Business Administration and Economics since 2012, where he teaches a comprehensive range of statistics and econometrics courses at undergraduate and graduate levels. His academic journey includes positions as Associate and Assistant Professor at the University of Groningen (2009-2012), postdoctoral work at Maastricht University and TU Dortmund, and doctoral studies in econometrics at TU Dortmund. Professor Hanck's research focuses on nonstationary panel data analysis, macroeconometrics, and multiple testing procedures, with recent expansion into educational technology and machine learning applications. His publication record shows consistent output in top econometrics journals with over 40 publications spanning more than 15 years. His most recent work (2023-2025) demonstrates a dual research trajectory: advancing econometric methodology while innovating in digital teaching methods for statistics education. This includes publications on nonlinear cointegration testing, Bayesian econometrics, and educational data mining for assessment integrity and student performance prediction. Professor Hanck collaborates extensively with researchers including Massing, Klenke, Arnold, and Demetrescu across multiple institutions, indicating a strong research network in both methodological econometrics and educational technology applications. His teaching portfolio encompasses core statistics and econometrics courses at all academic levels, with increasing integration of digital assessment methods and computational approaches using R programming.
