Philippe Cudré-Mauroux is a Full Professor in the Department of Computer Science at the University of Fribourg, affiliated with the Faculty of Science and Medicine. His research spans data management, big data systems, knowledge graphs, semantic web, and human-AI collaboration. His primary research interests include Data Management , Big Data Systems , Knowledge Graphs , Time Series Analytics , Database Systems , Human-AI Collaboration , and Machine Learning for Data Cleaning . His work integrates theoretical database research with practical applications in smart cities, social media, and healthcare analytics. The recent publications reflect a strong trend toward knowledge graph embeddings , large language models for data quality , time series benchmarking , and human-in-the-loop systems . His research combines symbolic and neural methods, emphasizing schema awareness, explainability, and real-world deployment. He actively supervises numerous PhD and Master’s students and collaborates widely across institutions. His group contributes to open-source tools and benchmarking frameworks for database and AI systems.
Alexandre Alahi is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Visual Intelligence for Transportation (VITA) laboratory. He is affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC), the Institute of Infrastructure (IIC), and also contributes to diversity initiatives at ENAC. His research focuses on integrating computer vision, machine learning, and robotics to develop socially-aware AI for transportation and autonomous systems. University: École Polytechnique Fédérale de Lausanne (EPFL) School: School of Architecture, Civil and Environmental Engineering Department: Institute of Infrastructure, IIC Research Lab: Visual Intelligence for Transportation (VITA) Alexandre's research interests center on computer vision, machine learning, robotics, and AI safety, particularly in human trajectory prediction, depth estimation, and socially-aware autonomous navigation. He investigates how AI can understand and predict human behavior in complex environments to improve safety in mobility systems. His work bridges theoretical advances with real-world applications in autonomous driving, urban planning, and healthcare. His recent publications span a wide array of topics including omnidirectional stereo matching, trajectory forecasting, cross-view localization, AI security, and depth estimation. These works demonstrate a strong trend toward building generalizable, robust, and socially-compliant AI systems, with increasing focus on uncertainty quantification, safety certification, and real-world deployment. The integration of multimodal data and the development of foundation models are recurring themes. Alexandre has received numerous scientific accolades, including: Top 100 Most Influential Scholar in Computer Vision (2022–2023) Editor’s Choice Award, Image and Vision Computing (2021) Honorable Mention, ICCV Workshop (2019) CVPR Open Source Award (2012) ICDSC Challenge Prize (2009) Top 20 Swiss Venture Leaders (2010) He has advised numerous PhD students whose theses cover diverse topics such as human motion prediction, person re-identification, trajectory forecasting, and AI security. His lab has secured significant recognition and funding, enabling impactful research with real-world applications. Alexandre has also co-founded startups like Visiosafe, demonstrating strong industry engagement and technology transfer. The VITA lab fosters interdisciplinary collaboration, working across computer vision, robotics, transportation engineering, and human-centered AI. The team develops datasets, benchmarks, and open-source tools to advance the field and promote reproducibility.
Tim Althoff is an Assistant Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he directs the Behavioral Data Science Group. His research combines data science, social network analysis, and natural language processing to develop computational methods for understanding human behavior, mental health, and public health through digital traces.
Dr. Marcel Binz is a research scientist and deputy head at the Institute for Human-Centered AI at Helmholtz Munich. His work bridges machine learning and cognitive science to develop foundation models of human cognition, aiming to unify theories of human behavior through computational modeling.
Haoyu Chen is a Tenure-track Assistant Professor at the Center for Machine Vision and Signal Analysis (CMVS), University of Oulu. He is also a co-founder of the AI startup Aitomore and an AI-based restaurant Aitofresh. His research focuses on Machine Learning, Human Behaviour Analysis, Emotion AI, and Adversarial Learning, with particular emphasis on Hybrid Intelligence and human-AI interaction. Dr. Chen received his Ph.D. from the University of Oulu, Finland, where he was advised by Academy Professor Guoying Zhao. During his PhD studies, he visited CEL, TU Delft, the Netherlands. Prior to that, he received his B.E. degree from China University of Geosciences, China, and Master degree from University of Oulu, Finland. Before joining as faculty, he conducted Postdoc research in CMVS, University of Oulu, with projects on Emotion AI (Academy Finland project) and trustworthy AI (Infotech project). His research spans multiple areas of artificial intelligence with a focus on understanding human behavior through AI systems. Dr. Chen's work particularly emphasizes emotion recognition, 3D pose transfer, micro-gesture analysis, and the development of hybrid intelligence systems where humans and AI mutually enhance each other's capabilities. His recent work has increasingly incorporated large language models for multimodal understanding of human emotions and behaviors, with significant publications in ICML, CVPR, and NeurIPS. Academy Research Fellow funding (622k euros), ranked 1st in AI panel ELLIS member IEEE Finland Jt. Chapter SP/CAS Best Paper Award Dr. Chen actively supervises multiple students including PhD candidates, Master's students, and research interns. His teaching includes courses on Affective Computing and Computer Graphics at the University of Oulu. He also organizes workshops and seminars, including the MiGA workshop series on Micro-gesture Analysis for Hidden Emotion Understanding and the HAECU tutorial on Human-AI mutual promotion for emotion and cognition understanding.
Craig Innes is a Research Fellow at the University of Edinburgh , affiliated with the Institute for Perception, Action and Behaviour (IPAB) . His research bridges symbolic logic and formal verification with black-box probabilistic models in cyber-physical systems , focusing on trustworthiness , risk assessment , and safety guarantees for autonomous vehicles and robotics . His work explores temporal logic specifications , adaptive experiment design , and probabilistic calibration to improve safety validation . Key trends in his publications include formal verification for machine learning systems , physics-informed simulation for soft robotics , and risk-driven perception system design . He also investigates unawareness in decision-making and scenario generation via large language models . Craig supervises PhD students in areas like hybrid AI for cyber-physical systems and autonomous robotics , with funding opportunities through EPSRC Doctoral Training Partnerships and Centre for Doctoral Training programs in Dependable Robotics and Machine Learning Systems . He emphasizes research proposal development and collaboration with industry in his supervision approach.
Jonathan Berant is an Associate Professor at the Blavatnik School of Computer Science, Tel-Aviv University, and a Research Scientist at the Allen Institute for Artificial Intelligence. Currently on leave of absence at Google (based in Seattle) until summer 2026, he has established himself as a leading researcher in Natural Language Processing with significant contributions to semantic parsing, question answering, and weak supervision techniques. His research focuses on Natural Language Understanding problems including Semantic Parsing, Question Answering, Paraphrasing, Reading Comprehension, and Textual Entailment, with particular interest in learning from weak supervision that is easy to obtain and grounded in the world, as well as tasks requiring multi-step inference or handling of language compositionality. His work bridges theoretical foundations with practical applications in language understanding systems. Berant's recent publications demonstrate strong trends in language model alignment, robustness, and reasoning capabilities, with significant contributions to understanding how language models process information and how to make them more reliable. His work spans from fundamental NLP tasks to addressing critical challenges in modern large language models. Senior area chair outstanding paper award (NAACL 2025) Outstanding paper award (ICLR 2024) Spotlight talk (NeurIPS 2023) Spotlight talk (3% of submissions) (NeurIPS 2022) Oral presentation (NeurIPS 2021) Berant has advised numerous PhD and Master's students who have gone on to positions at leading AI companies and research institutions including Google, AI21, Allen Institute for AI, and academic programs. His teaching includes advanced NLP courses and research seminars at Tel-Aviv University. His educational background includes a Ph.D. from Tel-Aviv University (2006-12) with advisors Ido Dagan, Jacob Goldberger, and Eytan Ruppin, followed by postdoctoral work at Stanford University with Percy Liang and Chris Manning, and at Google Mountain View.
Jens Kober is an Associate Professor in the Cognitive Robotics department at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science, and a key member of the TU Delft Robotics Institute. His research bridges machine learning and robotics for real-world applications. His research focuses on Motor Skill Learning, Reinforcement Learning, and Interactive Learning for robotics, with applications spanning robot manipulation, locomotion, surgical assistance, and wind turbine control. Key areas include: Developing robust learning algorithms for compliant robots Creating interactive learning frameworks for human-robot collaboration Applying deep learning to robot control under uncertainty Transferring machine learning techniques to medical robotics His recent publications reveal strong trends in interactive imitation learning and reinforcement learning for dynamic tasks , particularly in robot manipulation (e.g., pick-and-place ambiguity resolution) and locomotion (explosive jumping with soft quadrupeds). The work increasingly integrates vision systems and focuses on real-world applicability in retail, dentistry, and renewable energy. Scientific recognition includes: Robotics: Science and Systems Early Career Award (2022) IEEE-RAS Early Academic Career Award (2018) Georges Giralt PhD Award for best European Robotics thesis (2013) Kober actively supervises PhD students and leads major collaborative projects including the NWO-funded DL-foRCe (Deep Learning for Robust Robot Control), Robot-Assisted Tooth Removal, and the industry-backed AI for Retail Lab with Ahold Delhaize. His work connects academic research with industrial applications through grants from NWO, TKI Wind op Zee, and industry partnerships. He directs the Cognitive Robotics group within the TU Delft Robotics Institute, collaborating closely with the Delft Center for Systems and Control and the ELLIS Unit Delft. His team develops foundational learning algorithms while deploying them in applied settings like the AIR Lab Delft test site for retail robotics.
Chris Watkins is Professor of Machine Learning at the Department of Computer Science at Royal Holloway, University of London . His research spans reinforcement learning , evolutionary algorithms , kernel methods in machine learning , epidemiological modeling , and financial mathematics . Key research contributions include: Invention of Q-learning in the 1980s Formal equivalence between evolutionary processes and Bayesian inference Pioneering work on string kernels for non-vectorial data His recent publications focus on evolutionary models satisfying detailed balance (2023), metastability in genetic systems (2022), and error-correcting codes in evolutionary contexts . He has also contributed to understanding fitness fluctuations and genetic architecture . Scientific recognition includes the ECML Innovative Contribution Award (2006) for work on string kernels and grammatical inference. His 1996-1999 research on portfolio optimization anticipated critical issues in financial risk estimation that resurfaced during the 2008 crisis.
Mackenzie Mathis is a Tenure Track Assistant Professor and Bertarelli Foundation Chair of Integrative Neuroscience at the Brain Mind Institute (BMI) of EPFL. She leads the UPMWMATHIS Lab, focusing on understanding neural circuits underlying adaptive behavior and developing AI tools like DeepLabCut and CEBRA. Her work bridges machine learning and neuroscience, with expertise in systems neuroscience, animal behavior, and computer vision. Education: PhD in Neuroscience, Harvard University (2017) Research Interests: Her lab explores motor learning, sensorimotor control, and neural dynamics. Key tools include markerless pose estimation (DeepLabCut), latent embedding analysis (CEBRA), and generative AI for behavioral studies. Projects span from mouse behavior assays to ethical AI applications in conservation. Publications: Recent work includes advancements in robust ML systems, pre-trained pose models (SuperAnimal), and neural-latent dynamics analysis. Her lab’s tools are widely adopted in neuroscience and robotics. Awards: Swiss Science Latsis Prize (2024) Eric Kandel Young Neuroscientist Prize (2023) FENS EJN Young Investigator Prize (2022) Vallee Scholar & ELLIS Scholar Advising & Grants: Current PhD students include Célia Benquet, Hossein Mirzaei, and others. Grants include SNSF Starting Grant (1.5M CHF) and CZI funding for open-source tools. Collaborates with robotics and conservation initiatives. Labs & Teams: The UPMWMATHIS Lab at EPFL Biotech Campus integrates computational and experimental neuroscience. Tools like AmadeusGPT and CEBRA are developed here, supported by interdisciplinary teams.
Dr. Martin Schüle is a Researcher at the Zurich University of Applied Sciences (ZHAW), specifically within the Institute of Computational Life Sciences at the ZHAW School of Life Sciences and Facility Management. He leads the 'Head Research AI & Computational Environment Focus' initiative and specializes in AI applications across environmental sciences, financial markets, and natural language processing. His work bridges theoretical computer science with real-world challenges, including optimizing agricultural practices and analyzing non-financial corporate communication. Research interests span AI ethics, neural networks, and nonlinear dynamics, with a focus on interdisciplinary projects like predicting investor behavior and improving fertilizer efficiency. He has contributed to over 15 peer-reviewed articles and conference papers, often as a principal investigator or co-author in collaborative international teams. Projects include leading efforts on optimal fertilizer application, greenwashing detection in corporate ESG reporting, and real-time agricultural decision systems. Active in networks such as the Deutsche Physikalische Gesellschaft and collaborates with institutions like Paris 1 Sorbonne. His articles explore foundational AI topics (e.g., large language model semantics) and applied domains (e.g., bond market sentiment analysis). He has presented at major venues like the International Symposium on Nonlinear Theory and its Applications (NOLTA), addressing synchronization in cellular automata and hybrid deep learning for environmental forecasting. While no scientific awards are explicitly listed, his extensive project portfolio highlights impactful contributions to computational science and policy-relevant research in sustainability and finance.
Tom Griffiths is a Professor of Psychology and Cognitive Science at Princeton University, directing the Computational Cognitive Science Lab and co-leading the Princeton Laboratory for Artificial Intelligence . His research spans computational models of human cognition, Bayesian statistics, and AI systems. Key research themes: mathematical foundations of human intelligence, resource-rational analysis of decision-making, cultural evolution, and AI-human alignment Awards: National Science Foundation, Sloan Foundation, American Psychological Association, Psychonomic Society Recent publications focus on large language models, cognitive resource optimization, and cross-disciplinary insights from psychology, computer science, and neuroscience. He is also co-author of the popular science book Algorithms to Live By .
Lena A. Jäger is an Associate Professor in Digital Linguistics at the School for Transdisciplinary Studies, University of Potsdam. Her research focuses on computational linguistics, eye tracking, and machine learning applications in language processing. She leads the Digital Linguistics group, supervising 11 PhD students and contributing to interdisciplinary projects like the MultiplEYE corpus initiative. Her work bridges cognitive science and AI, with key contributions to eye movement analysis, biometric identification, and LLM interpretability. Her research interests span eye tracking in reading, linguistic predictability modeling, and the alignment of language models with human cognition. Notable achievements include developing the pymovements toolkit for eye movement data processing and pioneering studies on detecting alcohol inebriation via gaze analysis. Awards include best paper honors at ETRA 2023 and 2024. Recent work explores generative models (ScanDL) for synthetic eye movement generation, cross-lingual reading behavior analysis, and fairness in biometric systems. She advises on AI ethics through the DSI initiative and collaborates internationally on projects like the MECO corpus.
Andreas Fischer is an Ordentlicher Professor at the Fribourg School of Engineering and Architecture (HES-SO), specializing in Pattern Recognition, Machine Learning, and Document Analysis. His research focuses on handwriting recognition, graph-based methods, and applications in cultural heritage preservation and medical imaging. Education: BSc in Computer Science from Fribourg School of Engineering and Architecture Research Interests: Graph Neural Networks for automata universality analysis Hybrid systems for Vietnamese stele keyword spotting Medical image analysis (colorectal cancer, tumor budding) Large language models for post-OCR correction Key Projects: TAINA Technology (handwriting validation for tax forms) Swisscom (Swiss German to High German translation) Hasler Foundation (Vietnamese stele graph-based analysis) Publications: Over 30 peer-reviewed articles in top journals/conferences (IEEE Access, Medical Image Analysis, Pattern Recognition, etc.), with focus on graph-based methods, handwriting recognition, and medical applications. Grants & Roles: Principal Applicant for multiple industry-funded projects (TAINA, Swisscom) Co-developer of DIVA-DAF deep learning framework
Marco Zaffalon is Professor and Scientific Director at IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale), affiliated with the Università della Svizzera italiana's Faculty of Informatics. He leads a 30-member research group on probabilistic machine learning and has published over 150 papers. Education: M.Sc. in Computer Science (Università degli Studi di Milano) Ph.D. in Applied Mathematics (Università degli Studi di Milano) Research spans probabilistic machine learning, causal AI, imprecise probabilities, and quantum computation. His work develops theoretical foundations for uncertainty reasoning and applies them to AI systems. Recent publications focus on causal inference with LLMs, counterfactual computation, and quantum decision models. Articles consistently explore intersections of probability theory, computational methods, and real-world applications like healthcare. Trends include advancing tractability in causal queries and bridging logical frameworks with machine learning. Administrative roles include co-founding Artificialy (as Chief Scientist) and directing IDSIA since 2019. He teaches courses in Causal AI, Uncertain Reasoning, and Probability.
