Warut Suksompong is an Assistant Professor in the School of Computing at the National University of Singapore (NUS), where he holds the NUS Presidential Young Professorship. Previously, he was a postdoctoral researcher at the University of Oxford, hosted by Edith Elkind, and completed his PhD at Stanford University under Tim Roughgarden. He holds bachelor's and master's degrees from MIT. His research focuses on algorithmic game theory, computational social choice, mechanism design, and interdisciplinary problems at the intersection of computer science, economics, mathematics, and operations research. He is particularly known for contributions to fair division, including envy-freeness, proportionality, and maximin fairness in both divisible and indivisible resource allocation contexts. Key awards include the IJCAI 2021 Distinguished Paper Award for work on dividing land with geometric constraints and the WINE 2021 Best Student Paper Award for research on funding public projects via the Nash product rule. His work bridges theoretical foundations with practical applications, addressing challenges such as connectivity constraints, budget allocation, and social choice mechanisms. He has authored over 100 publications in top conferences and journals, including SIAM Journal on Discrete Mathematics, Artificial Intelligence, and Games and Economic Behavior. His research has been supported by grants from NUS and international collaborations.
Michael Muehlebach leads the independent Learning and Dynamical Systems research group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. His interdisciplinary work bridges machine learning, dynamical systems theory, and control engineering to develop algorithms for cyber-physical systems with theoretical guarantees and practical implementations. Dr. Muehlebach received his B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich in 2010 and 2013, specializing in robotics and control systems. He completed his Ph.D. at ETH's Institute for Dynamic Systems and Control under Prof. R. D'Andrea in 2018, followed by postdoctoral research with Prof. Michael I. Jordan at UC Berkeley. His research focuses on constrained optimization, reinforcement learning, and control theory with applications in robotics. He pioneered approaches that express constraints in terms of velocities rather than positions, enabling more efficient optimization algorithms. His work spans theoretical foundations to physical implementations, including the One-Wheel Cubli balancing robot and electromagnetic navigation systems. Recent publications reveal a strong trend toward physics-informed machine learning, particularly for robotics applications requiring real-time performance and safety guarantees. Dr. Muehlebach has received numerous prestigious awards: Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellowship (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) He actively mentors doctoral researchers including Hao Ma, Melis Ilayda Bal, and Onno Eberhard, with research supported by multiple grants. His group maintains strong collaborations with Bernhard Schölkopf's Empirical Inference group at the Max Planck Institute. The Learning and Dynamical Systems group develops innovative hardware and software platforms, including Floaty (a wind-harnessing flying robot), advanced electromagnetic navigation systems, and data-efficient learning methods for robotic table tennis. Their approach combines rigorous theoretical analysis with practical validation on physical systems, emphasizing the integration of known physical structure into machine learning algorithms to improve sample efficiency and ensure generalization.
Prof. Dr. Dirk Heckmann is a prominent academic and legal expert currently serving as a member of the Bavarian Institute for Digital Transformation (bidt) Board of Directors and holding the Chair of Digital Law and Security at the Technical University of Munich . As a constitutional lawyer and judge at the Bavarian Constitutional Court, he bridges legal expertise with digital policy through advisory roles in the Federal Government's Data Ethics Commission and the Chancellor's National IT Summit. In 2020, he established the TUM Center for Digital Public Services to advance legally sound, common-good-oriented digitalization. His research focuses on digital education , digital administration , and healthcare digitalization , with particular emphasis on legal certainty for digital systems . Recent projects like ReDiKo (Regulating Digital Communication Platforms) and AFFAIRE (AI Regulation for Examinations) highlight his commitment to evidence-based digital governance. He has authored the juris Practical Commentary on Internet Law since 2021, shaping Germany's digital legal discourse. Professor Heckmann actively engages in public debates about digital sovereignty and platform regulation , most notably at the 2025 Nuremberg Digital Festival where he will discuss open-source foundations for digital sovereignty. His work spans constitutional law , data ethics , and AI policy , with recent publications analyzing social media harassment, AI's impact on education, and digital violence legislation.
Elena Simperl is a Professor of Computer Science and Deputy Head of Department for Enterprise and Engagement at King's College London's Department of Informatics. She co-directs the King's Institute for Artificial Intelligence and serves as Director of Research for the Open Data Institute. As a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study, she leads the Trustworthy Knowledge Graphs focus group and contributes to advancing human-centric AI research across European institutions. Professor Simperl obtained her doctoral degree in Computer Science from the Free University of Berlin and her diploma from the Technical University of Munich. Prior to joining King's, she held academic positions in Germany, Austria, and at the University of Southampton, and was a Turing Fellow. Her career trajectory demonstrates consistent leadership in bridging academic research with practical applications in data ecosystems. Her research sits at the critical intersection of AI and social computing, focusing on human-centric approaches to building sociotechnical systems that integrate data, algorithms, and human capabilities. She investigates how to make knowledge engineering more accessible, how to leverage collective intelligence for data quality improvement, and how to design participatory AI systems that address societal challenges like misinformation. Her work spans knowledge graphs, semantic technologies, crowdsourcing, and open data, with particular emphasis on the social dimensions of data-intensive systems and the governance frameworks needed for trustworthy AI deployment. Analysis of her recent publications reveals a strong evolution toward integrating large language models with traditional knowledge engineering practices while maintaining human oversight. There's a clear trajectory from foundational work on knowledge representation toward increasingly applied research addressing real-world challenges in media ecosystems, citizen science, and data governance, with growing attention to policy implications of AI technologies. Fellow of the British Computer Society Fellow of the Royal Society of Arts Hans Fischer Senior Fellow at TUM-IAS (2023) Ranked among top 100 most influential scholars in knowledge engineering of the last decade Included in Women in AI 2000 ranking Professor Simperl has led 14 major European and national research projects totaling millions in funding, including MediaFutures (a Horizon 2020 program tackling online misinformation), QROWD, ODINE, Data Pitch, and ACTION. She currently co-chairs the Croissant working group in ML Commons developing data standards for AI, and serves as president of the Semantic Web Science Association. Her research has directly influenced the development of data ecosystems supporting startups and citizen science initiatives across Europe, demonstrating exceptional ability to translate theoretical advances into practical impact. As Director of Research at the Open Data Institute, she oversees initiatives connecting data entrepreneurs with artists and civic organizations. Her leadership in the MediaFutures project established a data-driven innovation hub that supported 51 startups/SMEs and 43 artists through three open calls, creating a sustainable model for arts-technology collaborations addressing media challenges. Her work with the ODINE project helped create a European ecosystem for data-driven startups, demonstrating her commitment to building practical applications of open data principles.
Prof. Dr. Ingo Scholtes is Chair of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). His research spans network science, graph machine learning, and computational social science, with applications in software engineering, ecology, biology, and physics. He received a Juniorfellowship from the German Informatics Society (2014) and an SNSF Professorship (CHF 1.5Mio, 2018). Current affiliations: JMU Würzburg (since 2021), University of Zurich (2018-2024), Bergische Universität Wuppertal (2019-2021) Research focus: Higher-order network modeling, temporal graph analysis, AI for collaborative systems, causality-aware machine learning His recent publications demonstrate strong trends in temporal network analysis , graph neural networks for time-series, and higher-order models across software engineering and social science domains. He co-chairs multiple international workshops on complex networks and serves as associate editor for EPJ Data Science and Advances in Complex Systems. Key scientific contributions: Foundational work on higher-order network models published in Nature Physics Methodological innovations in temporal network visualization (HOTVis) and path-based analysis (pathpy) As both educator and organizer, he leads the Computational Social Science Section at GI e.V., mentors across disciplines, and develops tools like git2net for collaboration analysis. His work bridges theoretical foundations with practical applications in network science.
Michael Mühlebach is a Research Group Leader at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, leading the independent Learning and Dynamical Systems group. His academic journey began at ETH Zurich where he earned his B.Sc. (2010) and M.Sc. (2013) in mechanical engineering, specializing in robotics, systems, and control. He completed his Ph.D. at ETH Zurich in 2018 under Prof. R. D'Andrea, followed by postdoctoral research at UC Berkeley with Prof. Michael I. Jordan. Dr. Mühlebach's research spans machine learning, dynamical systems, control theory, and optimization . His work bridges theoretical foundations with practical applications in robotics, developing methods that incorporate physical constraints and system dynamics into learning frameworks. His group focuses on online learning, physics-informed machine learning, and large-scale optimization for cyber-physical systems, with applications in electromagnetic navigation, robotic table tennis, and energy-efficient flight systems like the shape-changing robot Floaty . His publication record shows a strong focus on constrained optimization, with recent work exploring decision-dependent stochastic optimization, nonlinear feedback, and the theoretical foundations of reinforcement learning. His research integrates perspectives from control theory, dynamical systems, and optimization to develop algorithms with strong theoretical guarantees and practical performance. Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellow (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) Dr. Mühlebach actively mentors doctoral researchers and is seeking talented students for PhD and Master's projects. His research group has received funding from multiple prestigious fellowships and maintains collaborations across institutions including ETH Zurich, UC Berkeley, and various Max Planck research units. The group's work spans theoretical developments to practical implementations on robotic systems, demonstrating strong connections between mathematical theory and physical realization.
Björn Brandenburg is a researcher at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany. His work focuses on real-time systems, scheduling algorithms, and operating system design, with a particular emphasis on predictable resource allocation and performance guarantees in multiprocessor and cyber-physical environments. His research interests include real-time response-time analysis (e.g., PROSA ), locking protocols for multiprocessor systems, side-channel mitigation in cloud environments, and the verification of real-time scheduling policies. He has contributed to foundational studies on deadline failure probabilities, self-suspending tasks, and predictable real-time Linux implementations. Scientific awards include recognition for outstanding papers on TimerShield (2017) Offline Equivalence (2017) . His work intersects with practical systems like LITMUSRT and ROS 2, aiming to bridge theoretical guarantees with real-world applications in safety-critical and distributed real-time systems.
Nasir M. Rajpoot is a Professor in the Department of Computer Science at the University of Warwick, UK. His research focuses on computational pathology, medical image analysis, and deep learning applications in histology. He leads interdisciplinary projects integrating artificial intelligence with healthcare, particularly in cancer diagnostics and pathology workflows. Rajpoot’s work emphasizes developing robust algorithms for histology image analysis, including nuclear segmentation, tumor classification, and domain generalization in computational pathology. His contributions include the TIAToolbox, an open-source framework for tissue image analytics, and the CoNIC Challenge to advance nuclear detection and counting in histology images. He collaborates with clinicians and biologists to translate AI models into clinical practice, addressing challenges like tumor heterogeneity and staining variability. Rajpoot’s research spans colorectal, lung, and oral cancers, with a focus on predicting clinical outcomes via histological features and genomic data integration. Notable projects include the development of Handcrafted Histological Transformer (H2T) for unsupervised representations of whole slide images and the SAFRON framework for histology image synthesis. His work addresses domain adaptation, robustness evaluation, and explainability in AI-driven pathology systems.
Katy Börner is a Professor affiliated with Indiana University, Bloomington, USA. Her research focuses on data visualization, scientometrics, and the science of science, with contributions to tools like Network Workbench (NWB) and Sci2. She leads interdisciplinary projects such as the Human Reference Atlas and explores visualization literacy in education and public health. Her work spans virtual reality applications, biomedical knowledge networks, and AI-driven scientific discovery. Key research interests include mapping scientific collaboration networks, analyzing scholarly publications, and developing visualization frameworks for big data. She collaborates widely with institutions like NIH and VIVO, advancing open science and interdisciplinary research. Her projects often bridge computational methods with human-centric design, enhancing understanding of complex systems in health, technology, and social sciences. Publications highlight innovations in interactive visualization tools (e.g., opioid crisis research networks), data integration (Human Reference Atlas), and AI applications in biomedical research. She emphasizes translating data into actionable insights through visual analytics, impacting policy, education, and healthcare.
Kord Eickmeyer is a Lecturer at Technische Universität Darmstadt in the Department of Mathematics, specializing in the mathematical logic group. He holds a PhD in mathematics from Humboldt University Berlin and has held postdoctoral positions at TU Darmstadt (2011–2017) and the National Institute of Informatics in Tokyo (2011–2013). His research focuses on finite model theory, graph structure theory, and computational complexity, particularly in descriptive and parameterized complexity, as well as randomization and derandomization techniques. Research interests include exploring the boundaries of computational complexity through logical frameworks, analyzing graph structures for efficient algorithm design, and investigating the role of randomness in computation. His work bridges theoretical computer science and mathematical logic, with applications in algorithm design and formal methods. Publications span topics from model-checking on ordered structures to gap-planar graphs and randomized logics. Collaborations include prominent institutions like the National Institute of Informatics and Humboldt University Berlin. No scientific awards are explicitly listed, but his extensive academic contributions reflect a strong research trajectory. Advising and grants are not detailed in the provided text, though his academic career includes supervision roles during his PhD and postdoctoral phases. His involvement with the mathematical logic group at TU Darmstadt highlights collaborative research efforts in foundational areas of computer science and mathematics.
Krishna Gummadi is a Scientific Director and Professor at the Max Planck Institute for Software Systems (MPI-SWS) in Germany, where he leads the Networked Systems Research Group. He also holds a professorship at the University of Saarland, demonstrating his dual commitment to research and academic instruction in computer science. His educational background includes: Ph.D. in Computer Science and Engineering from the University of Washington (2005) B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Madras (2000) Gummadi's research spans networked and distributed computer systems with a current focus on social computing systems. His work addresses critical challenges in algorithmic fairness, privacy in social media, trustworthiness of online identities, and information dissemination in social networks. He approaches these problems through interdisciplinary methods combining user-centric studies, data-centric analysis, and systems-centric design to create practical solutions that enhance fairness, transparency, and user control in online platforms. His methodology integrates large-scale observational studies, computational modeling, and system implementation to tackle complex human-computer interaction challenges at societal scale. His recent publications reveal a strong emphasis on fairness in algorithmic decision making, with significant contributions to quantifying and addressing discrimination in machine learning systems. His work bridges computer science, social science, and ethics, creating frameworks for fair classification, understanding media bias, and developing privacy-preserving techniques that maintain functionality while protecting user data. The research demonstrates a progression from technical system design to addressing societal implications of computing systems. Among his notable scientific achievements: ERC Advanced Grant in 2017 for 'Foundations for Fair Social Computing' Test of Time Awards at ACM SIGCOMM and AAAI ICWSM Casper Bowden Privacy Enhancing Technologies (PET) and CNIL-INRIA Privacy Runners-Up Awards IW3C2 WWW Best Paper Honorable Mention Multiple Best Paper awards across prestigious conferences Gummadi has advised numerous PhD students and postdoctoral researchers who have gone on to prominent positions in academia and industry. His ERC Advanced Grant has supported extensive research into fair social computing, while his leadership in major conferences (including serving as General Chair for ICWSM 2016 and Program Chair for WWW 2015) has shaped research directions in the field. His teaching portfolio includes courses on Distributed Systems, Human-Centered Machine Learning, and Social Media Analysis. He leads the Networked Systems Research Group at MPI-SWS, which has developed several publicly available systems including tools for fair classification, privacy risk assessment, trust evaluation in social media, and information diet management. The group's work bridges theoretical advances with practical implementations that address real-world challenges in social computing, with numerous software releases and datasets made available to the research community.
Floris van Doorn is a Professor at the Mathematical Institute of the University of Bonn where he leads the Formalized Mathematics group. His research focuses on making it viable to formalize research mathematics in proof assistants that can check the correctness of such proofs. He primarily works with the Lean Theorem Prover and is a maintainer of its mathematical library (mathlib). University of Bonn: Professor (2023-present) University of Paris-Saclay: Postdoc with Patrick Massot (2021-2023) University of Pittsburgh: Postdoc with Tom Hales (2018-2021) Carnegie Mellon University: PhD under Jeremy Avigad and Steve Awodey (2013-2018) Van Doorn's research interests center on formalized mathematics, tools and automation for formalization, and homotopy type theory. He has made significant contributions to several major formalization projects including the Carleson project (proving Carleson's theorem), the sphere eversion project (formalizing Gromov's h-principle), the Flypitch project (formalizing the independence of the continuum hypothesis), and the Spectral sequences project. His work demonstrates that proof assistants can handle complex areas of mathematics beyond algebra, including differential topology and analysis. His recent publications show a consistent focus on advancing formalized mathematics, with his most recent work formalizing the Gagliardo-Nirenberg-Sobolev inequality and continuing the Carleson project. His publications span theoretical foundations of type theory, practical applications of formalization, and educational resources for learning proof assistants. Skolem award (2025) for the paper 'The Lean Theorem Prover (System Description)' Van Doorn actively mentors students and collaborators, with Maria, Michael, and Arend recently joining his formalization group in Bonn. He has taught various courses on formalized mathematics and proof assistants at the University of Bonn, University of Pittsburgh, and Carnegie Mellon University. His educational efforts include developing learning resources such as the Natural Number Game and the online book 'Mathematics in Lean.' He also maintains an active presence in the Lean community through the Formalized Mathematics group and collaborative projects like the Carleson project, which invites participation from those familiar with Lean.
Dr. Hilde Kuehne is a Professor at the University of Tuebingen and a key researcher at the Tuebingen AI Center. She holds affiliations with MIT-IBM Watson AI Lab and Goethe University Frankfurt, with a focus on computer vision, multimodal learning, and explainable AI. Her work bridges vision-language models, audio-visual alignment, and self-supervised methods. Co-organizer of the New Frontiers in Associative Memories workshop @ ICLR 2025 Member of the Scientific Advisory Board of the Carl-Zeiss-Foundation Contributor to CVPR 2025's UTD dataset for unbiased video benchmarks Her research addresses critical challenges in: Explainability for Vision Transformers (LeGrad) Fine-grained audio-visual alignment (CAV-MAE Sync) Zero-shot visual recognition automation (Meta-Prompting) Spatio-temporal grounding without annotations Recent collaborative work spans 15+ publications across CVPR, NeurIPS, ICCV, and ICLR, with emphasis on multimodal foundation models, dataset bias mitigation, and differentiable logic networks. She actively contributes to workshop organization and peer review as evidenced by her involvement in CVPR 2025 and ICLR 2025 program committees.
Chantal Pellegrini is a Lecturer and PhD student at the Chair of Computer Aided Medical Procedures (Prof. Navab) at Technical University of Munich (TUM). Her research focuses on Deep Learning applications in medical imaging, including explainable AI for radiology report generation and Vision-Language Models for clinical decision support. She actively contributes to the DHM, NARVIS Lab, and RobUSt research groups. Teaching responsibilities include courses such as 'Computer Aided Medical Procedures', 'Medical Augmented Reality', and 'Surgical Robotics'. She supervises student projects in medical AI and healthcare innovation, with recent projects involving multimodal report generation and graph pretraining for medical applications. Education: BSc/MSc Computer Science (TUM), current PhD student since 2022 Labs: DHM (German Heart Center), NARVIS Lab, RobUSt Robotics & Ultrasound Research Keywords: Medical Image Understanding, Radiology Reports, LLMs in Healthcare Her publications span surgical OR dataset development, reinforcement learning for clinical decisions, and explainable X-ray diagnosis systems. She mentors MA/BA students in medical AI and project management for healthcare applications.
Prof. Dr. Sebastian Büttner is an Associate Professor at the Institute of Sociology within the Friedrich-Alexander University Erlangen-Nuremberg (FAU) and since September 2024, he serves as the scientific project manager of the Transfer Unit Science Communication at the Berlin-Brandenburg Academy of Sciences and Humanities (BBAW) . His academic career includes acting professorships at the University of Kassel, Freie Universität Berlin, and University of Duisburg-Essen. Research Interests : Sociological theory and cultural sociology Sociology of knowledge and science Political sociology of expertise and policy European integration and transnationalization Regionalization processes in Europe Academic Training : 2024: Außerplanmäßiger Professor at FAU 2017: Habilitation in Sociology at FAU 2010: Doctorate (Dr. rer. pol.) at BIGSSS 2004: Diploma in Sociology at University of Bamberg Collaborations and Projects : He is an associate editor of Culture, Practice & Europeanization , co-organizer of the German-French working group 'Governing European Mobilities', and member of the interdisciplinary group 'Conflict Management in Europe'. He led DFG-funded projects on EU professionalism and co-edited works on Europeanization and controversial expertise.
