Prof. Dr. Thomas Brinkhoff is Chair of the Institute Board and Chair of Geoinformatics at Oldenburg University of Applied Sciences. He leads the Institute for Applied Photogrammetry and Geoinformatics (IAPG) and contributes to institutions like the Association for the Promotion of Geoinformatics in Northern Germany (GiN e.V.) and the Oldenburg Research and Development Institute for Computer Science (OFFIS e.V.). Education: Diploma in Computer Science (Informatik), Universität Bremen (1990) Doctorate in Computer Science (Dr. rer. nat.), Ludwig Maximilian University of Munich (1994) Brinkhoff's research spans geodatabase systems, spatiotemporal data processing, geosensor analytics, and location-based services. His work addresses Volunteered Geographic Information (VGI), web-based geospatial visualization, and mobile data integration, with applications in traffic management and forensic science. Recent projects include ProSaDi (Digital Provenance and Collection Research) and contributions to the Laboratory for optical 3D metrology . He has served on program committees for ACM SIGSPATIAL (2002-2019), AGILE conferences (2010-2025), and editorial boards of journals like GeoInformatica and TGIS. Notable Lectures: 2024: Forensic applications of tachograph data 2023: Geoinformatics in homicide investigations 2022: Spatiotemporal analysis for sustainability projects 2015: Open geodata standards at FOSSGIS 2014: Mobile sensor data processing
Jeffrey F. Brock is the Dean of the School of Engineering & Applied Science and the William S. Massey Professor of Mathematics at Yale University. He holds the Zhao and Ji Chair in Mathematics. His research focuses on low-dimensional geometry and topology, particularly hyperbolic geometry and its applications to data analysis. He completed his undergraduate studies at Yale and earned his Ph.D. from UC Berkeley. He held positions at Stanford, the University of Chicago, and Brown University, where he chaired the Mathematics Department from 2013 to 2017 and founded Brown’s Data Science Initiative in 2016. He joined Yale in 2018, serving as inaugural Dean of Science in the Faculty of Arts and Sciences until assuming his current role in 2022. He is a Guggenheim Fellow and Fellow of the American Mathematical Society. His research spans hyperbolic 3-manifolds, Teichmüller dynamics, and geometric methods in data science. Notable contributions include work on Thurston’s geometrization program, classification of hyperbolic manifolds, and applications of geometric topology to complex datasets. He co-authored foundational papers on ending laminations, Weil-Petersson geometry, and renormalized volume. His recent work bridges pure mathematics with applied challenges, such as algorithmic detection of medical imaging patterns. Awarded the Guggenheim Fellowship (2008) and AMS Fellow (2017), Brock has also led interdisciplinary initiatives at Brown and Yale. His administrative roles include overseeing engineering, natural sciences, and data science programs. Beyond academia, he co-founded the Vijay Iyer Trio, showcasing his passion for music performance and creativity.
Angela Capel Cuevas is an Assistant Professor of Quantum Information Theory/Theoretical Quantum Computation at the University of Cambridge's Department of Applied Mathematics and Theoretical Physics (DAMTP). Previously, she held a Junior Professorship at Eberhard Karls Universität Tübingen (2021-2024) and was an MCQST Distinguished PostDoc at TU München (2020-2021). Her research focuses on the intersection of quantum information theory and quantum many-body systems, particularly studying thermalization dynamics via quantum functional inequalities. She is a recipient of the Simons Emmy Noether Fellowship and Forbes 30 Under 30 (Spain 2023). Education: PhD in Mathematics, Universidad Autónoma de Madrid/ICMAT (2015-2019) Master in Computational Engineering and Mathematics, URV/UOC (2016-2018) Bachelor in Mathematics, Universidad de Granada (2009-2014) Research: Angela's work applies analytic and geometric tools to quantum systems, emphasizing decay of correlations in Gibbs states and quantum dissipative evolutions. Key topics include entropy inequalities, modified logarithmic Sobolev inequalities, and thermalization rates in spin chains. Her recent work bridges quantum functional analysis with practical implications for quantum computing and thermalization phenomena. Grants & Roles: PI of CRC TRR 352 'Mathematics of Many-Body Quantum Systems' (7M€) Co-PI of QuantERA project TouQan (1.25M€) Organized workshops at BIRS and Tübingen (2023-2024) Awards: Simons Emmy Noether Fellowship (2023) Forbes 30 Under 30 (2023) Vicent Caselles RSME-FBBVA Award (2022) Labs/Teams: Leads a group studying quantum dissipative systems and thermalization at DAMTP. Collaborates with institutions globally on quantum functional inequalities and Gibbs state properties.
Nate Foster is a Professor of Computer Science at Cornell University and currently serves as the Associate Dean for Research in the Ann S. Bowers College of Computing and Information Science. He is also a Visiting Researcher at Jane Street and served as a Visiting Professor at École Polytechnique Fédérale de Lausanne during the 2023-24 academic year. His research uses ideas from programming languages to solve problems in networking, databases, and security. BA in Computer Science, Williams College (2001) MPhil in History and Philosophy of Science, University of Cambridge (2008, all work completed in 2003) PhD in Computer and Information Science, University of Pennsylvania (2009) Foster's research focuses on developing languages and tools that make it easy for programmers to build secure and reliable systems. His current work centers on the design and implementation of languages and tools for programmable networks, particularly using the P4 language. His past work includes bidirectional languages (also known as 'lenses'), database query languages, data provenance, type systems, mechanized proof, and formal semantics. His research group at Cornell has made significant contributions to network verification, software-defined networking, and formal foundations for programmable data planes. Analysis of Foster's recent publications reveals a strong focus on network verification and programming language foundations for networking. His work consistently applies formal methods to practical networking problems, with a particular emphasis on the NetKAT and P4 languages. Over the past five years, his research has evolved toward more complex network verification techniques, including infinite state verification, active learning of network models, and dependently-typed approaches to network programming. His work bridges theoretical computer science with practical networking systems, making formal methods accessible to network engineers. ACM Fellow (2025) ACM SIGPLAN Robin Milner Award (2023) ACM SIGCOMM Rising Star Award (2018) NSF CAREER Award (2013) Alfred P. Sloan Fellowship (2012) Multiple distinguished paper awards across top conferences including POPL, PLDI, and SIGCOMM Foster has advised numerous PhD and Master's students who have gone on to prominent positions in both industry and academia, with many continuing work in programming languages and networking. He has led multiple significant research grants including an NSF CAREER Award and has been involved in the P4 Language Consortium, serving as Chair of the P4 Language Governing Board. His work has been supported by various organizations including NSF, DARPA, and industry partners like Intel and Jane Street. Foster is also active in the programming languages research community, serving on numerous program committees and as Vice Chair of DARPA's Information Science and Technology (ISAT) study group. Foster leads a vibrant research group at Cornell focused on programming languages for networks, with collaborators from academia and industry. His group has developed several influential tools and frameworks including NetKAT, Petr4, and KATch. They maintain strong connections with the P4 community and work closely with industry partners to ensure their research has practical impact on real-world networking systems.
Xiaowei Jia is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh. He holds a Ph.D. from the University of Minnesota (supervised by Prof. Vipin Kumar) and B.S./M.S. degrees from the University of Science and Technology of China (USTC) and SUNY Buffalo. His research focuses on integrating scientific theory with machine learning to address societal and environmental challenges, such as climate modeling, hydrology, and fairness in AI. Education: Ph.D., University of Minnesota (2020) M.S., State University of New York at Buffalo B.S., University of Science and Technology of China (USTC) Research Interests: Knowledge-Guided Machine Learning Spatiotemporal Data Mining Fairness in AI for Social Good Applications in Environmental Science and Healthcare Publications showcase his work on physics-integrated neural networks, spatiotemporal modeling (e.g., water temperature prediction), and fairness-aware algorithms. His work has been recognized with Best Paper awards at SIAM SDM (2022, 2023). Awards include the Best Applied Data Science Paper Award at SIAM SDM in 2022 and 2023. He teaches advanced machine learning courses, emphasizing theory integration with real-world applications.
Jan von Delft is a Professor (chair) at Ludwig-Maximilians-University (LMU) Munich, working in the Faculty of Physics within the Chair of Theoretical Solid State Physics. His research group consists of postdocs, PhD students, and master's students working on various aspects of strongly correlated electron systems, with physical space located at Theresienstr. 37 (Room A420) in Munich. von Delft's research focuses on correlated electron and spin systems, with particular interest in dynamical and transport properties, quantum impurity models, Hund metals, unconventional superconductors, quantum magnets, and quantum criticality. His methodological expertise includes many-body field theory, parquet formalism (FRG), DMFT, and tensor networks (NRG, DMRG, PEPS, XTRG, etc.). His work bridges theoretical concepts with computational approaches to understand complex quantum phenomena in condensed matter systems. He has developed a distinctive emphasis on real-frequency calculations and numerical methods for studying quantum critical phenomena. Analysis of von Delft's recent publications reveals a strong focus on developing and applying advanced computational methods to study strongly correlated electron systems. His group has made significant contributions to numerical renormalization group techniques, tensor network methods, and the parquet formalism for calculating real-frequency correlation functions. His research shows increasing sophistication in handling quantum criticality, particularly in heavy-fermion systems, and exploring unconventional superconductivity mechanisms. Notably, his group has developed specialized computational libraries like KeldyshQFT to make these advanced methods more accessible to the broader physics community. von Delft actively mentors a substantial research group consisting of one postdoc (Markus Scheb), eleven PhD students (Anxiang Ge, Sasha Kovalska, Mathias Pelz, Marc Ritter, Nepomuk Ritz, Changkai Zhang, Markus Frankenbacher, Felipe Picoli, Simone Fodera, Ming Huang), and two master's students (Ester Pages, Gianluca Grosso). His detailed Style Guide for scientific communication demonstrates his commitment to high-quality research presentation. The group appears well-funded with ongoing research activities spanning theoretical development, computational implementation, and physical interpretation of complex quantum phenomena.
Prof. Dr. Dennis Säring is a faculty member at the University of Applied Sciences Wedel , specifically affiliated with the School of Engineering. His academic and research activities focus on Deep Learning , Medical Image Analysis , and applications of Artificial Intelligence in healthcare and biomedical imaging. He has led seminars on Deep Learning topics and supervised student projects in Autonomous Driving at Audi's AADC 2018 competition. Research Highlights : Cardiovascular imaging, forensic age estimation via MRI, neural network-based bone segmentation, and cerebrovascular aneurysm analysis. Technical Expertise : Cardiac MRI, 3D/4D image processing, parametric mapping, and spatiotemporal data fusion. His recent publications (2018-2023) emphasize 3D MR segmentation for age assessment, CMR strain analysis in athletes, and T1/T2 mapping for myocarditis. Key collaborations include institutions like the University Medical Center Hamburg-Eppendorf and Wedler Hochschulbund, with funding for autonomous vehicle research. While no explicit scientific awards are listed, his work spans clinical cardiology, forensic radiology, and AI-driven medical diagnostics.
Professor Christian Bizer is a leading figure in web-based systems and data integration at the University of Mannheim , where he chairs Information Systems V: Web-based Systems . His research focuses on integrating data from multiple sources using large language models and LLM-based agents, with applications in product data extraction and DBpedia knowledge graph construction. He co-founded the DBpedia project and initiated the WebDataCommons initiative. Current research areas: Entity matching, schema matching, table annotation, information extraction, data discovery Key projects: WebMall benchmark, WInte.r integration framework, Schema.org analysis His work applies to e-commerce data integration and knowledge graph construction, with empirical studies on schema.org adoption. He supervises PhD students including Alexander Brinkmann and Ralph Peeters. Scientific Awards: Best Paper at iiWAS 2024 SWSA Ten-Year Award at ISWC 2019 Yahoo FREP Award 2015 Semantic Web Challenge winners Teaching includes courses on web data integration, web mining, large language models, and data mining for master's programs. He leads the DWS PhD colloquium and team projects on LLM agents for data integration.
Professor Rainer Böhme is a prominent academic in the field of IT security and privacy at the University of Münster's School of Business and Economics, Department of Information Systems. He also holds a professorship for Security and Privacy at the University of Innsbruck's Institute of Computer Science. His research spans multiple dimensions of digital security, with particular expertise in steganography, digital forensics, privacy-enhancing technologies, and the economic aspects of cybersecurity. Dr. Böhme earned his Doctoral Degree from TU Dresden's Faculty of Computer Science in 2008, following studies in Communications, Economics and Computer Science at Technische Universität Dresden and Université Paris XII. His academic journey includes positions as Assistant Professor at the University of Münster (2010-2015), Postdoctoral Fellow at the International Computer Science Institute in Berkeley (2009-2010), and Scientific Staff at TU Dresden's Chair of Privacy and Data Security. His research interests focus on steganography and steganalysis, digital forensics, privacy-enhancing technologies, security and usability, economic and behavioral aspects of information privacy, virtual currencies, and the economics of information security. His work bridges technical security mechanisms with economic and behavioral considerations, creating a unique interdisciplinary approach to cybersecurity challenges. Analysis of his recent publications reveals a strong trend toward the economics of security, particularly regarding blockchain technologies, cryptocurrency, and privacy valuation. His work increasingly examines the intersection of technical security mechanisms with market dynamics, regulatory frameworks, and user behavior. The research spans theoretical game-theoretic models, empirical studies of user behavior, and practical applications in digital forensics and privacy protection. Program Co-Chair, Financial Cryptography and Data Security (2015) Program Co-Chair, New Security Paradigms Workshop (NSPW) (2015) General Chair, 8th International Conference on IT Security Incident Management & IT Forensics (2014) Organizer and Program Co-Chair, 1st Workshop on Bitcoin Research (2014) Associate Editor, Electronic Commerce Research (since 2013) Associate Editor, EURASIP Journal on Information Security (since 2013) Professor Böhme has supervised multiple Master's theses in Information Systems, including work on static code analysis for smart contracts, remote maintenance protocol security, and application protection requirements in retail banking. His research is supported by significant grants from the German Federal Ministry of Research, Technology and Space, the DFG, and the EU FP7 program, with projects focusing on digital forensics, privacy-enhancing technologies, and cryptocurrency crime prevention. He leads the IT Security Research Group at the University of Münster, which investigates theoretical and applied aspects of information security with particular emphasis on the economic dimensions of security decisions.
Prof. Marc Stamminger is a Professor of Visual Computing at FAU since 2002, leading the Chair of Computer Science 9 (Computer Graphics). His work focuses on algorithms for synthesizing and analyzing images through 3D modeling, LiDAR/Radar capture, and light simulation. He co-leads FAU Solar, applying 3D modeling for environmental lighting analysis under varying conditions. Stamminger has published over 250 papers, winning prestigious awards like the Siggraph Test-of-Time Award. He holds executive roles in Eurographics and is Vice Dean of FAU's Technical Faculty. Research interests span neural rendering , 3D reconstruction , radar imaging , and medical visualization . Recent work emphasizes radiance field rendering (e.g., VR-Splatting, INPC) and radar-based human motion tracking. His lab's FAU Solar project integrates large-scale 3D models with environmental lighting simulations. Publications trends highlight neural rendering optimizations , radar-MIMO systems , and agricultural digital twins . Key collaborations involve medical imaging (e.g., vocal fold reconstruction) and autonomous driving data generation. Awards: Siggraph Test-of-Time (2023?), 2× Siggraph Best-Of-Show Grants/Teams: FAU Solar Lab, Eurographics leadership, FAU Vice Dean Labs: Chair of Computer Science 9, FAU Solar Initiative
Jaan Masso is an Associate Professor at the University of Tartu, Estonia, where he has been affiliated with the Faculty of Economics and Business Administration since at least 2001. He currently serves as Chair of the department, Head of the Chair of Economic Modelling, and Programme Manager of the Master's programme in Quantitative Economics. His research focuses on applied econometrics, economic development, foreign direct investment, and labor economics, with particular emphasis on Estonia and the Baltic region as catching-up economies. He has published 146 scholarly works with over 23,000 reads and 1,745 citations, demonstrating significant scholarly impact in his field. Masso's research interests span multiple interconnected areas including foreign direct investment effects , productivity analysis , gender wage gap studies , innovation economics , and labor market dynamics . His work often employs rigorous econometric methods to analyze firm-level and regional economic data, particularly from Estonia and other Baltic states. His recent publications (2023-2025) show consistent scholarly productivity across multiple journals, with particular focus on foreign ownership effects, gender pay gaps in academia, FinTech sector development, and innovative methods for measuring firm innovativeness through web data analysis. His scientific contributions demonstrate expertise in analyzing economic transitions in small open economies, with methodological strengths in econometric analysis of firm-level data and innovative approaches to measuring economic phenomena.
Peter Benner is a Professor and Director at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, where he leads the Computational Methods in Systems and Control Theory group. He also holds an Honorarprofessor position for Mathematics at Otto-von-Guericke Universität Magdeburg since 2011. Benner has previously served as Managing Director of the Max Planck Institute during multiple periods (2013-2014, 2021-2022, and 2025-2026), demonstrating his leadership in the field. Benner's research focuses on Scientific Machine Learning, Numerical Linear and Multilinear Algebra, Model Order Reduction and Reduced-order Modeling, Numerical Methods in Systems and Control Theory, PDE Constrained Optimization, High-performance and Power-aware Computing, and Mathematical Software development. His work bridges theoretical mathematics with practical engineering applications, particularly addressing challenges in large-scale dynamical systems. Analysis of his recent publications reveals a strong emphasis on developing efficient computational methods for complex systems. Benner has pioneered approaches combining model order reduction with tensor methods to tackle high-dimensional problems in uncertainty quantification and PDE-constrained optimization. His work shows a consistent trend toward integrating data-driven techniques with traditional model-based approaches, particularly for nonlinear and parametric systems. Throughout his career, Benner has actively mentored students and collaborated with researchers worldwide, delivering numerous invited talks at prestigious institutions and conferences across Europe, North America, and Asia. His research has received significant funding, supporting the development of mathematical software and computational methods for industrial applications. Benner leads the Computational Methods in Systems and Control Theory department at the Max Planck Institute, which focuses on developing and implementing advanced numerical methods for large-scale dynamical systems. The group maintains strong connections with both theoretical mathematics and practical engineering applications, particularly in fluid dynamics, energy systems, and control theory.
Dr. Ulrike Kuhl is a Researcher at the University of Bielefeld, serving as Project Coordinator for the AI Academy OWL at the Research Institute for Cognition and Robotics and as Scientific Project Coordinator within the Faculty of Engineering's Machine Learning Group. Her office is located at CITEC 2-412, and she can be reached at +49 521 106-12125. Dr. Kuhl's research spans several interconnected domains at the forefront of human-centered AI development: Explainable Artificial Intelligence (XAI) frameworks and their psychological impact Cognitive learning enhanced through AI technologies Counterfactual explanation methodologies and user behavior Human-AI interaction design principles Applications of machine learning in environmental monitoring and sports analytics Analysis of Dr. Kuhl's publication trajectory reveals a consistent focus on bridging the gap between sophisticated AI systems and human understanding. Her work particularly examines how different explanation types affect user trust and decision-making, with recent publications exploring counterfactual explanations in contexts ranging from water distribution networks to educational technology. She has developed experimental frameworks like the 'Alien Zoo' methodology for systematically studying explanation usability. Dr. Kuhl actively contributes to the Center for Cognitive Interaction Technology (CITEC) at the University of Bielefeld, an interdisciplinary hub where computer scientists, engineers, and cognitive scientists collaborate on next-generation interactive technologies. Through her coordination of the AI Academy OWL initiative, she facilitates regional collaboration between academic researchers and industry partners to advance artificial intelligence applications in the Ostwestfalen-Lippe region.
Vladimir Spokoiny is a Professor at the Departments of Mathematics and Economics of the Humboldt University of Berlin and Head of the Research Group "Stochastic Algorithms and Nonparametric Statistics" at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany. His research spans multiple areas of statistics, machine learning, and financial mathematics, with significant contributions to nonparametric statistics, high-dimensional data analysis, and statistical methods in finance. Spokoiny received his M.Sc. in applied mathematics from the Moscow Institute of Railway Engineering in 1981 and his Ph.D. in mathematics from Lomonosov Moscow State University in 1988. He completed his Habilitation at Humboldt University in 1996. His academic career includes positions at the All-Union Institute of Railway Transport in Moscow, the Institute for Information Transmission Problems in Moscow, and the Institute for Applied Analysis and Statistics in Berlin before joining the Weierstrass Institute and Humboldt University where he has been a professor since 2002. Spokoiny's research focuses on adaptive nonparametric smoothing and hypothesis testing, high dimensional data analysis, statistical methods in finance, image analysis with applications to medicine, classification, and nonlinear time series. His work often addresses the challenges of nonstationarity in time series data and develops innovative methods for volatility estimation and risk management. He has made significant contributions to the development of adaptive weights smoothing procedures, which have applications in image processing, community detection, and manifold learning. His recent work has expanded into high-dimensional statistics, Bayesian inference, and optimization methods for machine learning, with publications demonstrating novel approaches to Gaussian approximation, Laplace methods, and statistical inference in non-Euclidean spaces. Spokoiny has supervised numerous PhD students including Oliver Reiss, Danilo Mercurio, Ying Chen, Elmar Diederichs, and Mstislav Elagin, whose research has focused on mathematical finance, time series analysis, and statistical methods. He serves as an Associate Editor for The Annals of Statistics (since 2004) and Statistics and Decisions (since 2002), and has previously served on the editorial board of the Journal of Statistical Planning and Inference. His professional activities include reviewing for major statistical journals including Annals of Statistics, Bernoulli, Econometrica, and Journal of American Statistical Association, as well as reviewing grant proposals for the National Science Foundation (USA), German Research Foundation, and Netherlands Organisation for Scientific Research. Spokoiny is a member of several professional societies including the International Statistical Institute, American Statistical Association, Institute of Mathematical Statistics, and Bernoulli Society. He is fluent in Russian (mother tongue), English, and German, and has good knowledge of French. His research group at WIAS focuses on developing novel statistical methodologies with applications across various scientific domains, particularly emphasizing adaptivity and robustness in complex data environments. The group's work has significant implications for financial risk management, medical imaging, and machine learning applications, with recent publications addressing fundamental questions in high-dimensional statistics and nonparametric inference.
Judy Kay is a Professor at the University of Sydney, Australia , renowned for her contributions to Artificial Intelligence in Education , User Modeling , and Ubiquitous Computing . Her work focuses on creating scrutable open learner models , enhancing collaborative learning analytics , and applying machine learning to education and health. Current research includes AI-driven misinformation detection and human-AI teaming for education. Recent projects involve virtual reality exergames and long-term physical activity tracking . Key publication trends span AI in education , data-driven learning design , and privacy-aware personalized systems . She actively collaborates with researchers in human-computer interaction and health informatics , emphasizing user control and ethical data use .
