Torsten Hoefler is a Full Professor of Computer Science at ETH Zurich, Switzerland, with an adjunct appointment in Electrical Engineering. He previously held roles at the National Center for Supercomputing Applications (University of Illinois at Urbana-Champaign) and Indiana University. Full Professor of Computer Science, ETH Zurich (2020–present) Adjunct Professor of Electrical Engineering, ETH Zurich (2020–present) Member at Large, ACM SIGHPC Executive Committee (2013–present) Leadership roles in the MPI Forum and Blue Waters project His research focuses on performance-centric system design , with emphasis on scalable networking, parallel programming models, and performance modeling. Key contributions include the Slim Fly network topology, Data-Centric Python framework, and innovations in parallel graph computations and RDMA-based systems. Recent publications span topics like LLM training networks , quantization geometry , chiplet interconnects , and AI-driven climate modeling , reflecting his interdisciplinary approach combining HPC, AI, and hardware-software co-design. ACM Gordon Bell Prize (2019) ERC Consolidator Grant (2020) IEEE TCSC Award for Excellence (2019) SIAM SIAG/SC Junior Scientist Prize (2012) Latsis Prize of ETH Zurich (2015) He has received multiple best paper awards at top conferences (SC10, SC13, SC14, SC19, IPDPS'15, HPDC'15, OOPSLA'16) and contributed to MPI-3 standardization.
Cezary Kaliszyk is a Professor in Theoretical Computer Science at the University of Melbourne. His research focuses on automated reasoning, formal methods, and learning for reasoning, particularly in the context of interactive theorem proving and formalized mathematics. He leads the ERC project 'FormalWeb3' and has been involved in several other significant research initiatives. Theoretical Computer Science Automated Reasoning Formal Methods Interactive Theorem Proving Machine Learning for Theorem Proving Formalized Mathematics Proof Guidance Learning for Reasoning His research explores the integration of machine learning techniques with formal reasoning systems to enhance automation in theorem proving. This includes developing systems like CoqHammer and Tactician, advancing premise selection, proof guidance, and learning-based proof search strategies. His work bridges logical foundations with practical AI-driven tools for formal verification. The recent publications demonstrate a consistent focus on advancing automated and interactive theorem proving through learning techniques, formalization of mathematical concepts (like surreal numbers), and improving reasoning systems (e.g., Prover9, tableaux methods). There is a strong emphasis on practical system development, formalization projects, and learning-based enhancements to reasoning. ERC project 'FormalWeb3' - Principal Investigator Cost Action EuroProofNet - WG5 Leader until 2024 FWF project P26201 - developing HOL(y)Hammer Other projects: JSPS P10044, NWO MathWiki, SURF WebDed, ProofWeb He has advised several PhD students to completion, including Michael Färber, Thibault Gauthier, Yutaka Nagashima, Stanisław Purgał, and Liao Zhang, and is currently supervising Daniel Ranalter and Neil Vyas. He has not received any explicitly mentioned scientific awards in the provided text. His work involves leadership in collaborative systems such as ProofWeb and HOL Import, and participation in major formalization efforts including the Mizar library integration with Isabelle. He is actively involved in the development of tool ecosystems for formal mathematics and automated reasoning.
Yun Fu is a tenured Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a joint appointment in the Khoury College of Computer Science. He has established himself as a leading researcher in Artificial Intelligence, with over 500 publications in top-tier venues including IEEE/ACM transactions and major AI conferences. His work spans both theoretical foundations and practical applications, with significant impact in computer vision and machine learning. Professor Fu earned his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. His academic career progressed from Assistant Professor at SUNY Buffalo to his current position as tenured Professor at Northeastern University, where he has held appointments since 2012. His educational background includes a Beckman Graduate Fellowship at UIUC (2007-2008). His research focuses on advancing Artificial Intelligence with particular emphasis on Computer Vision, Pattern Recognition, and Machine Learning. His seminal work includes the "Residual Dense Network for Image Super-Resolution" presented at CVPR 2018, which was ranked among the Top 10 Most Influential CVPR papers. His research interests span image processing, anomaly detection, multimodal learning, and trajectory prediction, with applications ranging from healthcare to consumer technology. Analysis of his recent publications reveals a strong trend toward developing efficient and robust AI systems that bridge computer vision with language understanding. His work increasingly focuses on multimodal learning, trajectory prediction for multi-agent systems, anomaly detection in complex environments, and model validation techniques for black-box systems, while maintaining practical applications in real-world scenarios. Professor Fu's extensive recognition includes: Fellow of IEEE (2018), OSA (2019), SPIE (2018), IAPR (2016), AAIA (2021), and AAAI (2025) Member of Academia Europaea (2022) and European Academy of Sciences and Arts (2023) Fellow of National Academy of Inventors (2023) Multiple Young Investigator Awards from NAE, ONR, ARO, IEEE, ACM, and INNS 12 Best Paper Awards from major conferences Industrial Research Awards from Google, Amazon, Samsung, JPMorgan, and others Professor Fu has successfully mentored numerous Ph.D. students who now hold prominent positions in academia and industry at institutions including Amazon, Microsoft, Meta, Adobe, and major universities. His entrepreneurial ventures include founding Giaran (acquired by Shiseido in 2017) and co-founding TVision Insights, demonstrating his commitment to translating research into real-world impact. He has secured significant research funding from both government agencies and industry partners. As the PI and Founding Director of the SmiLe Lab at Northeastern University, Professor Fu leads a dynamic research group focused on advancing the state-of-the-art in AI and Computer Vision. The lab fosters interdisciplinary collaboration across computer science, electrical engineering, and applied mathematics, with ongoing projects in efficient deep learning, multimodal understanding, and practical AI applications.
Marko Tkalčič is a Full Professor at the Faculty of Mathematics, Natural Sciences and Information Technologies (FAMNIT) at the University of Primorska in Koper, Slovenia. He is affiliated with the Department of Information Sciences and Technologies and leads research in psychologically-informed recommender systems through the HICUP lab. His academic journey includes prior roles as Associate Professor and Assistant Professor at the Free University of Bozen-Bolzano, and postdoctoral work at Johannes Kepler University and the University of Ljubljana. Research interests include computational psychology, user modeling, personality and emotion modeling, affective computing, and bias mitigation in AI. His work integrates machine learning, data mining, and user studies to enhance personalization systems by incorporating cognitive and emotional models. He focuses on music and film domains, with applications in social media, automotive interfaces, and multimedia. The recent publications demonstrate a strong trend toward human-centric AI , exploring eudaimonic and hedonic user experiences, cognitive load in driver assistance, music relistening behavior, privacy in group recommendations, and emotion-based video and music prediction. His research increasingly blends cognitive science theories (e.g., ACT-R) with practical recommender system design. University of Primorska Golden Plaque Award (2024) Stanford List of Top Scientists Cited Worldwide Best Reviewer Award at ISMIR 2020 Italian National Habilitation for Full Professor (2020) Italian National Habilitation for Associate Professor (2017) He actively supervises PhD students, including Elham Motamedi, and has secured teaching and research roles within his team. He is a member of the editorial board for UMUAI and Frontiers in Psychology, and has co-edited books and special issues on group recommender systems and human-centered AI. He leads the HICUP lab, fostering interdisciplinary research at the intersection of psychology and computer science.
Antonio Rodríguez-Sánchez is an Associate Professor in the Intelligent and Interactive Systems group at the Department of Computer Science, Universität Innsbruck (since 2019). He holds a PhD from York University (2010) and has held academic roles in Austria, Canada, and Spain. His research focuses on Explainable AI, computational neuroscience, deep learning, computer vision, robotics, and medical imaging. Education: PhD in Computer Science, York University (Canada), 2010 M.Sc. in Computer Science, Universidade da Coruña (Spain), 1998 B.Sc. in Computer Science, Universidad de Córdoba (Spain), 1996 3-year Bachelor in Biology, Autonomous University of Madrid (Spain), 1998–2001 Research Interests: His work bridges AI and neuroscience, emphasizing explainable systems, medical imaging analysis, robotics, and deep learning applications. Recent trends in publications highlight advancements in healthcare AI (REM sleep disorder prediction), robotic recycling, and computer vision for environmental monitoring. Teaching & Advising: Teaches courses like Deep Learning, Computer Vision, and Algorithms. Supervises PhD/MSc students (e.g., Safoura Rezapour-Lakani, Sebastian Stabinger). Active in EU-funded projects like PaCMan (FP7-ICT) and IntellAct. Labs & Projects: Leads research in the Intelligent and Interactive Systems group, focusing on interdisciplinary AI applications. Projects include automated avalanche detection and robotic recycling systems.
Thomas Gärtner is a Professor at the Institute of Logic and Computation within the Faculty of Informatics at Vienna University of Technology, leading the Machine Learning research group (E194-06). His work bridges theoretical machine learning with practical applications in chemistry, biology, and network analysis. His primary research focuses on graph neural networks (GNNs) and geometric deep learning, with significant contributions to GNN expressivity, graph transformations, and kernel methods for structured data. He explores fundamental questions about the limitations of message-passing architectures while developing practical enhancements like path-based extensions and expectation-complete representations. His chemical informatics work applies these techniques to binding affinity prediction, reaction classification, and solvent selection, demonstrating real-world impact in computational chemistry. Analysis of his 15 most recent publications (2023-2025) reveals three dominant research thrusts: theoretical GNN advancements (35% of articles), chemical informatics applications (40%), and novel learning frameworks (25%). The theoretical work increasingly addresses expressivity limitations through graph transformations and path-based approaches, while chemical applications show growing sophistication in molecular representation. Recent publications also indicate expanding interest in foundation models for graphs and robustness verification. He actively supervises master's students including Fabian Traxler (binding affinity prediction), Maximilian Plattner (SGD optimization), Fabian Jogl (graph transformations), and Thomas Schmied (reinforcement learning). His research is conducted through the Network Lab at TU Wien, where he serves as Principal Investigator for the Structured Data Learning with Generalized Similarities project.
Jürgen Cito is an Associate Professor in the Department of Software Engineering at the Faculty of Informatics, TU Wien, where he leads research in probabilistic programming, security, and configuration management. His work is supported by major grants from the Austrian Science Fund (FWF), European Commission, and Meta Platforms, Inc., with active projects spanning 2022-2027. His research focuses on the intersection of software engineering and machine learning, particularly in static analysis of probabilistic programs, AI-driven penetration testing, and infrastructure security. Key contributions include identifying secret exposure in configuration files, grammar inference for ad hoc parsers, and performance prediction from source code, often combining empirical studies with tool development. Analysis of his 15 most recent publications (2020-2024) reveals three dominant trends: (1) Security vulnerabilities in configuration management systems, especially secret leakage in dotfiles; (2) Application of large language models to offensive security testing; and (3) Machine learning techniques for performance prediction and AutoML optimization in software contexts. Cito has supervised 22 Master's students on cutting-edge topics including AI security, infrastructure as code, and program analysis. His current research portfolio includes: Types4Strings (FWF, 2024-2027): Type systems for string processing Cloud Open Source Research Mobility Network (EU, 2023-2026): Open-source cloud infrastructure Software Assistants for Probabilistic Programming (Meta, 2022-2026): AI tools for probabilistic code He is embedded in TU Wien's Institute of Software Technology and Interactive Systems (E194), collaborating on cross-institutional projects focused on software security and developer tooling, with particular emphasis on empirical validation of security practices and configuration management systems.
Eszter Iklodi is a PreDoc Researcher at the Databases and Artificial Intelligence department (E192-02) of Vienna University of Technology (TU Wien). She contributes to research projects focusing on artificial intelligence, natural language processing, and legal informatics, particularly in the development of explainable frameworks and annotated legal corpora. Her recent work includes the creation of the BRISE-plandok German legal corpus for building regulations and the POTATO framework for explainable information extraction, both published in high-impact venues like Language Resources and Evaluation and the ACM International Conference on Information & Knowledge Management (CIKM).
Elena Esposito is a Full Professor at the Department of Political and Social Sciences, University of Bologna, and at the Faculty of Sociology, University of Bielefeld. She holds the scientific-disciplinary sector GSPS-06/A (Sociology of cultural and communicative processes). Her work bridges systems theory, digital communication, and algorithmic futures, with a focus on artificial intelligence, prediction mechanisms, and media sociology. She has received prestigious fellowships from institutions like the ERC, Wissenschaftskolleg Berlin, and Columbia University. PhD in Sociology (1990, University of Bielefeld) Habilitation (2001, University of Bielefeld) National Scientific Qualification (2013, Italy) Degree in Philosophy (1987, University of Bologna, summa cum laude) Degree in Sociology (1983, University of Bologna, summa cum laude) Esposito's research spans algorithmic prediction, artificial communication, digital media, and systems theory. Her work examines how algorithms shape social intelligence in insurance, medicine, and policing, the paradoxes of contingency in financial markets, and the societal implications of virtuality. She connects Niklas Luhmann's theoretical frameworks to contemporary issues like AI ethics and pandemic-era social systems. Her recent publications emphasize AI's role in constructing unpredictable futures, the social management of algorithmic opacity, and the transformation of risk perception in digital societies. She has authored 13 books and over 100 peer-reviewed articles, exploring topics from media memory to financial forecasting. ERC Advanced Grant (2018): €2.08M for algorithmic prediction studies Volkswagen Foundation Grant (€1.5M) for U3B project Leverhulme Trust funding for international network on value performances Esposito serves on editorial and advisory boards for journals like Sociologica and organizations including the Weizenbaum Institute. She directs the FAIR project on AI implications and co-leads initiatives like DEMOPE (Democracy under Pressure) and CRC 1567 on Virtual Worlds.
Ramin Hasani is a researcher at TU Wien's Cyber-Physical Systems department. He holds a Dr.techn. (Doctor of Engineering) and specializes in machine learning applications for robotics, control systems, and biologically-inspired neural networks. His work focuses on developing interpretable neural architectures like Liquid Time-Constant Networks and Neural Circuit Policies, emphasizing safety and stability in autonomous systems. Hasani's research bridges neural network theory with practical robotics challenges, including autonomous racing, medical data analysis, and adversarial robustness. Key research areas include continuous-time neural networks, formal verification of neural ODEs, and bio-inspired control mechanisms derived from biological neural circuits (e.g., Caenorhabditis elegans). He collaborates extensively with institutions like MIT and ETH Zurich, contributing to projects in health-monitoring systems and end-to-end robot learning frameworks. His publications consistently address real-world challenges such as sepsis prediction via reinforcement learning and robust CNN architectures for image classification. Recent work highlights include developing stable recurrent networks through Gershgorin loss functions and advancing zero-shot transfer learning for autonomous systems. Hasani's interdisciplinary approach integrates principles from neuroscience, control theory, and machine learning to create auditable, high-performance AI solutions for cyber-physical environments.
Martin Riegler is a researcher at the Institute of Physics and Materials Science , part of the University of Natural Resources and Life Sciences, Vienna (BOKU). His work focuses on advanced material analysis and sustainable construction within the wood technology sector. Specializes in electrical resistivity measurements of wood Applies machine learning to wood machining acoustics Studies adhesive bondlines modified with carbon fillers Investigates moisture dynamics in wood Recent publications highlight his contributions to smart wood composites , non-invasive testing , and machine learning applications in wood processing. He has presented research at international conferences and collaborated with institutions like the Northern European Network for Wood Science and Engineering. Riegler's work intersects with Materials Science , Wood Technology , and Sustainable Engineering , particularly in optimizing particleboard production and wood moisture prediction . His research spans technical innovation and environmental stewardship in forestry applications.
Dr. Leoni Breth is a Researcher at the University for Continuing Education Krems, affiliated with the Department of Integrated Sensor Systems and the Center for Modelling and Simulation. She holds a PhD in Technical Physics from the Vienna University of Technology, specializing in Condensed Matter Physics and micromagnetic sensor research. Her work integrates theoretical modeling, experimental validation, and AI-driven approaches to advance materials science. Education: PhD in Technical Physics, Vienna University of Technology (focus: magnetoresistive sensors and thermal fluctuations) Undergraduate Studies in Technical Physics at Vienna University of Technology Research Interests: Dr. Breth's research focuses on micromagnetic simulations, magnetoresistive sensors, and the application of machine learning to analyze First-Order-Reversal Curves (FORCs) in materials science. Her work bridges fundamental physics and industrial applications, particularly in optimizing magnetic materials for advanced technologies like permanent magnets and cemented carbides. Key areas include coercivity enhancement, domain nucleation dynamics, and AI-based predictive modeling. Projects & Grants: FFG-funded project (2020-2023): AI-driven FORC analysis in carbide production FWF-funded project (2023-2026): Combinatorial synthesis and micromagnetic graph networks for magnet design Key Contributions: Her publications span topics like FORC diagram interpretation, skyrmion modeling in bulk materials, and machine learning for mechanical property prediction. She has also contributed to international conferences, including presentations at IEEE Magnetics Society events and the Joint European Magnetics Symposia.
Eleonora Laurenza is a PostDoc Researcher at the Department of Databases and Artificial Intelligence, Vienna University of Technology (TU Wien). Her research focuses on the intersection of deductive logic-based and inductive methods in Artificial Intelligence, with a special emphasis on Knowledge Graphs, Data Engineering, and Reasoning Systems. She contributes to projects like KnowledgeGraph (2020–2028), DeConquer (2023–2027), and SustainGraph (2023–2025). Her work spans topics such as scalable reasoning in knowledge graphs, probabilistic reasoning frameworks, and applications in distributed ledger technology. Recent publications address FATEful smart contracts and model-independent knowledge graph design. She teaches courses like 'Knowledge Graphs' and 'Research and Career Planning for Doctoral Students.' Laurenza collaborates on interdisciplinary projects involving blockchain ethics, regulatory compliance systems, and uncertainty modeling in data systems. Her research bridges foundational theory with practical applications in AI-driven data management.
Thomas Neubauer is a PostDoc Researcher at the Department of Information Systems Engineering, Technische Universität Wien. His research focuses on Smart Farming, Explainable AI, and Digital Agriculture with applications in precision livestock farming and sustainable agricultural systems. He leads projects on Precision Livestock Farming (PLFDoc), Legume-cereal intercropping, and Agricultural Photovoltaics integration (PlusIQ). Key projects include: Austrian Competence Centre for Feed and Food Quality, Safety & Innovation (2025–2028) Austrian Science Fund (FWF)-funded work on legume-cereal intercropping (2023–2027) EU-funded ICT4DecisionMaking in Farming (2017–2021) His research spans Digital Twin development for agriculture, machine learning in crop rotation planning, and data security solutions for e-Health. He has supervised over a dozen PhD and Master’s students since 2007. Notable publications include work on reinforcement learning for crop optimization and digital twin frameworks in smart farming.
Kees van Berkel is an Assistant Professor in AI Ethics at the Institute of Logic and Computation, TU Wien. His research focuses on logical and formal approaches to normative reasoning, AI ethics, and deontic logics. He holds a PhD in Computer Science from TU Wien (2022) and has held postdoctoral positions at Ruhr Universität Bochum and the University of Amsterdam. He teaches courses on AI Ethics, logical argumentation, and normative reasoning at both bachelor and master levels. Van Berkel's work bridges philosophy, computer science, and artificial intelligence. His research explores formal methods for ethical AI, including deontic explanations, normative systems, and argumentation frameworks. He has published extensively on topics like STIT logics, inconsistency measures in deontic reasoning, and applications of formal logic to Indian philosophical texts. His academic roles include co-coordinator of the AI Ethics Special Interest Group at TU Wien's Center for Artificial Intelligence and Machine Learning (CAIML), and membership in the Cluster of Excellence 'Bilateral AI' (BILAI). He has organized workshops and tutorials on logical argumentation and serves on program committees for major conferences like AAAI and KR. Key projects include the LoDEx initiative (Logical Argumentation for Deontic Explanations) and involvement in interdisciplinary research on normative reasoning in AI systems. His recent work emphasizes ethical auditing of LLM-based chat services and formalizing dialogical approaches to deontic explanations.
