Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data
Patrick Henkel is a Professor at the Technical University of Munich (TUM) affiliated with the TUM School of Engineering and Design and the Chair of Communication and Navigation. He holds a professorship in Satellite Geodesy under Prof. Hugentobler. His research focuses on advanced positioning technologies, including Global Navigation Satellite Systems (GNSS), autonomous systems, and sensor fusion. He develops algorithms for precise positioning in challenging environments such as urban areas, alpine regions, and indoor spaces. His work also extends to environmental applications, such as snow hydrology and climate monitoring using GNSS signals. Henkel’s contributions include innovations in real-time kinematic (RTK) positioning, UAV navigation, and multi-sensor integration for robotics and autonomous vehicles. His research is supported by collaborations with industry and academic partners, addressing both theoretical and applied challenges in geodesy and navigation. Henkel leads projects on GNSS signal processing, satellite-based environmental monitoring, and autonomous driving technologies. He has contributed to the Galileo HAS service and developed methodologies for snow water equivalent estimation using multi-frequency GNSS signals. His expertise spans hardware-software co-design for navigation systems and algorithm optimization for high-precision positioning in dynamic environments. He actively publishes in top-tier journals and conferences, with a focus on advancing the reliability and accuracy of navigation systems across various domains. His advising and grants include funding for projects on sensor fusion, UAV-based measurements, and satellite receiver development. He collaborates with teams at TUM’s Navigation Lab and the Professur für Satellitengeodäsie, contributing to both academic and industrial applications. His work on low-bandwidth RTK dissemination and laser-tracker verified UAV positioning highlights his commitment to bridging theoretical advancements with real-world implementation.
Ana Filipa Sequeira is a researcher affiliated with INESC TEC, Porto, Portugal, and the University of Porto. Her work spans biometrics, fairness in AI, and explainable artificial intelligence. She has contributed to advancements in face recognition, synthetic data applications, and bias mitigation through techniques like knowledge distillation and model compression. Institution: INESC TEC (Porto, Portugal) Research Themes: Face recognition, fairness, explainability, synthetic data, biometric security Her recent publications focus on addressing demographic biases in face recognition systems, developing privacy-preserving explainable methods, and evaluating synthetic data's impact. She collaborates extensively with researchers like Pedro C. Neto, Jaime S. Cardoso, and Naser Damer. Sequeira has participated in organizing and analyzing competitions such as FRCSyn, BIOSIG, and SYN-MAD, emphasizing robust evaluation frameworks. Her work intersects technical innovation with ethical considerations, advocating for responsible AI applications in biometrics.
Elena Anatolyevna Babushkina is a Professor at the Department of Construction and Economics of Siberian Federal University. She serves as director and scientific consultant of the Scientific and Educational Laboratory 'Dendroecology and Environmental Monitoring' . Her work spans dendrochronology, climate change impacts on tree growth, wood anatomy, and environmental monitoring in Siberian ecosystems. Doctor of Biological Sciences (2020) Corresponding Member of the Russian Academy of Sciences Extensive collaborations with international institutions like University of Arizona, University of Cambridge, and Swiss Federal Institute for Forest, Snow and Landscape Research Her research focuses on climatic reconstruction through tree rings , moisture-limited forest ecosystems , and environmental drivers of xylogenesis . Recent studies analyze earlywood/latewood dynamics, drought sensitivity, and cross-species growth patterns in Siberian larch, spruce, and Scots pine populations. Elena’s publications (100+ scientific, 10+ methodological) include 15 recent articles on tree-ring-based climate proxies , crop yield modeling , and seasonal growth regulation . Key journals include Forests , Dendrochronologia , and Scientific Reports . Notable scientific awards include the 2021 Honorary Worker of Education of the Russian Federation title and multiple Presidential and Ministerial Certificates of Appreciation . She leads national grants (RFBR, RSF) on climate-crop interactions and genetic adaptation to environmental stress .
Stefano Noventa is a Research Fellow at the Methods Center, Department of Social Sciences, Faculty of Economics and Social Sciences, University of Tübingen. He has held multiple postdoctoral positions at the University of Tübingen and previously at the University of Verona and the University of Padova. Education: Ph.D. in Cognitive Psychology, University of Padova (2011) M.Sc. in Physics, University of Padova (2006) Studies in Physics, University of Padova (1999–2006) International Visiting Graduate Student, University of Toronto (2009, 2010) Dr. Noventa's research lies at the intersection of mathematical psychology, psychometrics, and psychophysics, with a focus on developing and unifying quantitative models of human cognition and assessment. His work integrates Item Response Theory (IRT) and Knowledge Space Theory (KST) to create more robust frameworks for educational and psychological measurement. He investigates latent variable models, probabilistic knowledge structures, and the identifiability of complex psychometric models, often applying these to domains such as education, organizational psychology, and entrepreneurship. His recent publications (2020–2024) demonstrate a strong trend toward theoretical integration, particularly in bridging cognitive diagnosis models with traditional psychometric frameworks. The articles emphasize mathematical rigor, model generalization, and empirical validation, with applications in both cognitive science and applied psychology. Topics include the unification of assessment models, parameter estimation under local dependence, and the modeling of intuitive physical reasoning. Scientific Awards: No awards or honors listed in the provided text. Dr. Noventa has not been explicitly mentioned as an advisor to students, but he has served as a corresponding author and collaborator on multiple research projects, indicating a leadership role in research teams. He has been involved in a DFG-funded project (GLI NON-NORM) since 2019, suggesting active grant participation. His work is highly collaborative, involving researchers from Germany, Italy, Austria, and Canada. Labs and Research Groups: Methods Center, University of Tübingen Hector Institute of Education Science and Psychology, University of Tübingen Center of Assessment, University of Verona Department of General Psychology, University of Padova
Matthew K. Tam is an Associate Professor at the School of Mathematics and Statistics, The University of Melbourne, specializing in Operations Research. He is also an investigator at the Melbourne Centre for Data Science and an associate investigator in the ARC Training Centre OPTIMA. PhD in Mathematics (2016) from University of Newcastle under Jonathan Borwein Postdoctoral research at University of Göttingen with RTG-2088 and Alexander von Humboldt Foundation Junior Professor at University of Göttingen (2017-2020) His research focuses on continuous optimization, monotone operator theory, and variational analysis, with applications in wavelet construction and inverse problems. Key trends include distributed algorithms, resolvent splitting, and convergence analysis for feasibility problems. Discovery Early Career Researcher Award (DECRA) Alexander von Humboldt Fellowship He collaborates with institutions like ANZIAM, Springer, and IEEE, with publications spanning mathematical optimization, harmonic analysis, and computational mathematics. His work emphasizes algorithmic design for complex data systems and real-world applications in imaging and industrial modeling.
Prof. Jalal Etesami is an Assistant Professor in the Department of Computer Science at Technical University of Munich (TUM), leading the Decision Sciences & Systems group. He holds a Ph.D. in Industrial and Systems Engineering from the University of Illinois at Urbana-Champaign and was a Postdoctoral Fellow at EPFL in Switzerland. His research focuses on machine learning, causal inference, multi-agent systems, and game theory, with applications to systemic risk modeling and market design. He teaches advanced courses such as Causal Inference in Time Series , Algorithmic Game Theory , and Optimization, Learning, and Market Design . Notable contributions include work on causal structure learning, stochastic optimization, and non-Gaussian causal models. Recent research explores causal effect identification under confounding, neural networks for market analysis, and optimal experiment design. Prof. Etesami’s work appears in top venues like NeurIPS, AAAI, and IEEE journals. He actively contributes to the academic community, organizing seminars and workshops on topics ranging from causal reasoning to computational social choice.
Guo Li is affiliated with the Beijing Institute of Technology, School of Management and Economics. Their research spans computer vision, optimization algorithms, signal processing, and machine learning. Collaborations include work on image super-resolution, sensor networks, and energy systems. Publications are distributed across journals like Comput. Electron. Agric. , IEEE Trans. Circuits Syst. , and Entropy . Research interests focus on computational methods for image processing, algorithm design, and interdisciplinary applications in agriculture and energy. Recent work emphasizes lightweight neural network architectures, sparrow search algorithms, and thermodynamic modeling in materials science. Notable contributions include advancements in citrus fruit detection, fatigue life assessment of superalloys, and load forecasting techniques. Active in international conferences such as CVPR, ICC, and NSDI, with a strong publication record since 1998.
Dr. Francesca Delogu is a Scientific Associate at Saarland University's Department of Linguistics and Language Technology. Her research focuses on the cognitive and neural bases of online language understanding, particularly expectation-based mechanisms, event/script knowledge, pragmatic inferences, and reference processes. Specializes in ERP (Event-Related Potentials) and eye tracking methodologies Collaborates extensively with Prof. Matthew W. Crocker and Dr. Herbert Brouwer Her work examines how extra-linguistic knowledge influences semantic integration and lexical retrieval, with applications in computational linguistics and language technology. She has taught courses on experimental methods in psycholinguistic research since 2012. Key research trends include the functional dissociation of ERP components (N400/P600) in language comprehension, rational redundancy in referential expressions, and the role of discourse context in expectation generation. Her publications span journals like Language, Cognition and Neuroscience , Cognitive Science , and Brain Research .
Prof. Dr. Jürgen König is a Professor at the University of Duisburg-Essen , contributing to projects within the Collaborative Research Centre 1242. His research focuses on Charge Carrier Dynamics in Nanostructures (Project A02) and Unifying Theoretical Description of Relaxation in Electron Systems (Project B07). Contact details include email: koenig@thp.uni-due.de . Research interests span Condensed Matter Physics , Quantum Transport , Nanostructures , and Electron Dynamics . His work often employs theoretical modeling of quantum systems, with recent publications analyzing phase transitions in 2D Ising systems Floquet engineering in superconductors nonlocal thermoelectric correlations Lee-Yang zero analysis of transport . Articles from 2017-2024 highlight expertise in quantum dot systems , spin dynamics , surface physics , and full counting statistics .
Sani Nassif is a Research Fellow at the Technical University of Munich (TUM) under the Rudolf Diesel Industry Fellowship, hosted by Professor Ulf Schlichtmann. With 28 years of experience at Bell Labs and IBM Research, he has led teams in integrated circuit modeling, simulation, statistical analysis, and optimization. Research Interests: His work bridges integrated circuit technology with cross-disciplinary applications in medicine. Key areas include variability analysis in semiconductor manufacturing, low-power circuit design, and reliability engineering for nano-scale systems. He focuses on applying machine learning and statistical methods to solve challenges in energy-efficient computing and biomedical systems. Selected Publications: His research spans circuit variability trends, leakage current modeling, and reliability frameworks for nano-era systems. Work includes foundational studies on SRAM failure analysis and CMOS scaling limitations. Scientific Awards: He is recognized as an IEEE Fellow IBM Master Inventor (75 patents) Rudolf Diesel Industry Fellow
Max Fathi is a Professor of Mathematics at Université Paris Cité, affiliated with the Laboratoire Jacques-Louis Lions (LJLL) and Laboratoire de Probabilités, Statistique et Modélisation (LPSM). He concurrently holds a part-time teaching position at the Department of Mathematics and Applications (DMA) at École Normale Supérieure (ENS). Since 2023, he has been a member of the Institut Universitaire de France (IUF), a prestigious national research fellowship in France. He completed his PhD in 2013 at Université Pierre et Marie Curie under Cédric Villani, followed by a postdoctoral position at the University of California, Berkeley with Lawrence C. Evans and Fraydoun Rezakhanlou. Previously, he was a CNRS researcher at the Institut de Mathématiques de Toulouse before joining Université Paris Cité. His habilitation thesis (2019) focuses on optimal transport applications in analysis and probability. Fathi's research centers on optimal transport theory, particularly its applications to analysis, probability, and statistical physics. Key topics include interacting particle systems, functional inequalities (e.g., Poincaré, log-Sobolev), high-dimensional phenomena, Ricci curvature in discrete/continuous spaces, Stein's method, concentration of measure, and numerical methods for stochastic dynamics. His work is supported by the ANR project 'Conviviality.' He has delivered courses on functional analysis at ENS and participated in summer schools, including an MSRI course on functional inequalities and localization techniques. His teaching materials include lecture notes on optimal transport and stochastic processes. His contributions have been recognized through awards such as the IUF membership. Notable research collaborations include work with Thomas Courtade, Matthias Erbar, and Gabriel Stoltz on topics ranging from stability estimates of inequalities to hypocoercivity and numerical analysis of stochastic systems.
Sen. Prof. Dr.-Ing. Hermann Ney is a Professor at RWTH Aachen University's Chair of Computer Science 6 (Human Language Technology and Pattern Recognition). His primary affiliation is with the Department of Computer Science, where he leads research and teaching activities. Research Interests: Statistical classification and machine learning Automatic speech recognition Statistical machine translation Text/image/sign language recognition Image and object recognition Teaching: Pattern Recognition and Neural Networks Speech Recognition Digital Processing of Speech and Image Signals Language Modeling Statistical Natural Language Processing Advanced Topics in Statistical Modeling Full course details available here . Publications are accessible via the publication page and Google Scholar profile.
Peter K. Friz is an Einstein Professor in Mathematics at TU-Berlin, affiliated with the Institute of Mathematics, and associated with the Weierstrass Institute for Applied Analysis and Stochastics. His research focuses on stochastic analysis, rough path theory, and mathematical finance, with particular emphasis on volatility modeling and applications to quantitative finance. He has held prestigious grants, including ERC Starting and Consolidator Grants, and coordinates the DFG research unit 'Rough paths, stochastic partial differential equations, and related topics.' Friz's work bridges theoretical stochastic analysis and practical financial applications, emphasizing rough path theory and its implications for differential equations and stochastic processes. His collaborations include organizing international conferences and courses on rough paths, with invited lectures at institutions like Cambridge, Paris, and Bonn. Supported by DFG, the European Research Council, and the Einstein Foundation, his research explores geometric aspects of pathwise analysis and stochastic volatility dynamics. He has advised numerous PhD students and maintains active roles in academic administration, including coordinating Berlin Mathematical School programs and teaching advanced topics in stochastic calculus. His contributions to rough path theory and stochastic finance are recognized through his academic leadership and influential publications, including co-authoring the seminal book Multidimensional Stochastic Processes as Rough Paths .
Prof. Dr. Matthias Krauledat is a faculty member at Hochschule Rhein-Waal , specifically within the Faculty of Technology and Bionics . His academic career spans both theoretical research and industrial application, with a focus on Machine Learning and Brain-Computer Interfaces . After completing his PhD in Electrical Engineering/Computer Science at Technische Universität Berlin , he has contributed significantly to the advancement of EEG-based communication systems and neural signal processing methodologies. Born in Essen, Germany Studied Mathematics with a minor in Computer Science at University of Münster/Oxford Doctoral research at TU Berlin on Brain-Computer Interfaces Industrial experience at Henkel AG & DMT GmbH Research Interests focus on Machine Learning applications in Neuroscience and Biomedical Engineering , specifically Brain-Computer Interfaces , EEG Signal Processing , and Adaptive Classification Systems . His work explores how algorithms can be developed to enable self-learning computers to solve complex tasks involving neural data interpretation and prediction for previously unseen data in clinical and technological contexts. Publications demonstrate a consistent contribution to Neuroscience and Machine Learning fields, with particular emphasis on Brain-Computer Interface systems from 2004 through 2009. His research has focused on reducing training requirements, improving signal processing accuracy, and developing novel interaction paradigms like the Hex-o-Spell mental typewriter while addressing statistical challenges like covariate shift in neural data analysis. Professional Experience includes academic research at TU Berlin's Intelligent Data Analysis group, industrial software development roles at Henkel AG's Scientific Computing department, and TÜV Nord Group's Optical Metrology and Machine Diagnostics divisions. He maintains active research connections through collaborative publications with leading experts in the field.
