Michael Potter is Professor of Logic at the Faculty of Philosophy , University of Cambridge, and a Life Fellow at Fitzwilliam College. Previously at Balliol College, Oxford (D.Phil. in pure mathematics), he has held research fellowships at St Andrews, Harvard, and Stirling University (AHRC-funded). His work bridges historical and systematic philosophy, focusing on History of analytic philosophy (Frege, Russell, Wittgenstein, Ramsey) Philosophy of mathematics and logic Philosophy of religion Potter's research has produced major works like Set Theory and its Philosophy (OUP 2004) and The Rise of Analytic Philosophy (Routledge 2019), with significant contributions to understanding the Metaphysical underpinn't> of the Tractatus Neo-Fregean logicism Paradoxes in mathematical foundations He has supervised research on topics spanning dialetheism, predicative mathematics, and modal ontological arguments. Scientific awards include the AHRC Senior Research Fellowship (2004-2005). His recent publications analyze Transcendental arguments in Wittgenstein Sortition-based democratic theory Philosophical implications of Ramsey's infinite proofs via peer-reviewed chapters and journal articles.
Freda Shi is an Assistant Professor at the David R. Cheriton School of Computer Science at the University of Waterloo and a Faculty Member at the Vector Institute, where she holds a Canada CIFAR AI Chair. She joined the University of Waterloo in July 2024 after completing her Ph.D. at the Toyota Technological Institute at Chicago. Educational Background: Ph.D. in Computer Science, Toyota Technological Institute at Chicago (2024), advised by Professors Karen Livescu and Kevin Gimpel Bachelor's degree in Intelligence Science and Technology (Computer Science Track) with a minor in Sociology, Peking University (2018) Dr. Shi's research focuses on computational linguistics and natural language processing, particularly on deeper understandings of natural language and the human language processing mechanism. She is especially interested in learning language through grounding, computational multilingualism, and related machine learning aspects. Her work aims to inform the design of more efficient, effective, safe, and trustworthy NLP systems. She leads the CompLING Lab at the University of Waterloo, which investigates how language models process spatial relationships and acquire linguistic structures through grounded experiences. Her publication record shows a consistent trajectory of high-impact research, with recent work focusing on spatial reasoning in vision-language models, multilingual chain-of-thought capabilities, and grounded language acquisition. She has published in top-tier conferences including ACL, EMNLP, ICLR, and NAACL, with several papers receiving notable recognition including Best Paper Nominee status at multiple venues. Her research bridges theoretical linguistics with practical NLP applications, demonstrating how linguistic insights can improve AI systems. Scientific Recognition: Canada CIFAR AI Chair (2024) Google Ph.D. Fellowship Thesis of Distinction for her doctoral work Multiple Best Paper Nominee awards at major NLP conferences Dr. Shi teaches CS 784: Computational Linguistics and CS 486/686: Introduction to Artificial Intelligence at the University of Waterloo. She actively contributes to the NLP research community through conference participation, program committee service, and collaborative projects. Her research has significant implications for creating more robust, human-like language understanding systems and advancing the field of grounded language learning in artificial intelligence.
Ekaterina Shutova is an Associate Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam. She concurrently holds a Visiting Associate Professor position in the Computer Science Department at Stanford University. She leads the Amsterdam Natural Language Understanding Lab and heads the NLP & Digital Humanities research unit at ILLC. An ELLIS Scholar, she earned her PhD from the University of Cambridge Computer Laboratory and Pembroke College. Her research has been funded by ERC, Innovate UK, British Academy, Leverhulme Trust, Google, Meta, and Deloitte. Her research spans natural language processing and machine learning, with core interests in: Few-shot learning for NLP Multilingual and cross-lingual systems Joint modeling of language and vision Cognitive processing and semantic representation Figurative language interpretation Computational social science applications Her recent publications (2024-2025) predominantly focus on multimodal learning, cultural alignment in AI, metaphor processing, and evaluation methodologies for language models. These works reflect a trend toward integrating cognitive science with multilingual systems and ethical considerations. Awards & Fellowships: ERC Consolidator Grant (2025) ELLIS Scholar Outstanding Paper Award at ACL 2023 Finalist for Outstanding Certification by TMLR Runner-up Best Paper Award at NAACL-HLT 2016 Research Leadership: She directs the Amsterdam Natural Language Understanding Lab, supervising 8 PhD students, 1 MSc student, and 34 alumni. Her projects include an ERC-funded initiative on globally accessible language technology and an AI Democratization grant for hate speech detection.
Shaker A. Zahra is a Professor of Strategy and Entrepreneurship at the Carlson School of Management, University of Minnesota , where he holds the Robert E. Buuck Chair of Entrepreneurship. He has served as Department Chair, Director of the Holmes Center for Entrepreneurship, and Academic Co-Director of Carlson Ventures Enterprises, and was Founding Co-Director of the Center for Integrative Leadership. Previously, he held the Paul T. Babson Distinguished Professorship at Babson College and faculty roles at Georgia State University and the University of Twente (Netherlands). Education : PhD in Business Administration (University of Mississippi), MBA (National University), B. Commerce (Al-Azhar University) His research focuses on corporate and international entrepreneurship , dynamic capabilities , and social entrepreneurship , particularly in technology-based industries. He explores industry emergence, strategic novelty, and firm heterogeneity, applying theories across global markets. His recent articles emphasize corporate entrepreneurship, international new ventures, and the role of dynamic capabilities in innovation. His work spans industries like robotics, software, pharmaceuticals, and nanotechnology, with studies in China, Brazil, India, and Europe. Scientific Awards : 2014 Global Award for Entrepreneurship Research, 5 Honorary PhDs, Fellowships from AOM, IFERA, and others, and over 20 Best Paper Awards. He has advised 75+ PhD students, taught across undergraduate to executive programs, and consulted for global technology firms. His leadership includes roles on 25+ journal editorial boards, organizing conferences, and delivering 40+ keynote speeches worldwide.
Deborah Richards is a Professor in the School of Computing at Macquarie University, affiliated with the Ethics and Agency Research Centre, Performance and Expertise Research Centre, and Frontier AI Research Centre. She holds roles as Industry and External Relations Director for the School and Director of the Virtual Reality Lab. Her research focuses on Intelligent Virtual Agents, Virtual Reality applications in education and health, knowledge acquisition, and cybersecurity ethics. She has authored over 425 publications and led 19 research projects, including studies on AI in healthcare and ethical AI design. Education: PhD (Artificial Intelligence, Macquarie University), MAppSc (Information Studies), BBus (Computing and MIS), and a Postgraduate Certificate in Higher Education (Leadership and Management). She advises on PhD and MRes students in Intelligent Virtual Agents research. Research interests include virtual agents for health behavior change, AI ethics, and educational technology. Notable projects include the eADVICE web-based management system for urinary incontinence and the development of embodied conversational agents for wellbeing support. Awards include the ABC Science Media Fellowship (2005) and Vice-Chancellor’s Citations for Outstanding Contributions to Student Learning (2017). Grants and collaborations span health informatics, cybersecurity, and AI ethics. She co-leads initiatives like the Respectful Maternity Care study and the Virtual Reality Lab’s immersive learning environments. Future work emphasizes ethical AI integration in education and healthcare.
Elaine J. Francis is a Professor of English and Linguistics at Purdue University, where she also serves as the Associate Head of the Department of English. She holds affiliate appointments in the Department of Linguistics and the Department of Speech, Language, and Hearing Sciences. At Purdue, she directs the Experimental Linguistics Lab and teaches linguistics courses at both graduate and undergraduate levels. Francis completed her B.A. in Linguistics at the College of William and Mary in 1993, followed by her M.A. (1995) and Ph.D. (1999) in Linguistics at the University of Chicago under the direction of Salikoko Mufwene. Her dissertation examined variation among members of the same lexical category in English using Sadock's Autolexical Grammar framework. Prior to joining Purdue in 2003, she served as an Assistant Professor at the University of Hong Kong from 1999 to 2002, where she collaborated with Stephen Matthews on research concerning syntactic categories and relative clauses in Cantonese. Her research focuses on syntax and its interfaces with semantics, discourse information structure, and language processing in production and comprehension. Francis employs experimental methods to investigate syntactic, semantic, discourse-pragmatic, and cognitive factors underlying the grammar and usage of complex sentence structures. Her specific interests include word order alternations, filler-gap dependencies, resumptive pronouns, relative clauses, grammatical categories, and syntactic alternations. She has published extensively in top linguistics journals including Language and Cognition, Glossa Psycholinguistics, Linguistics, Lingua, Cognitive Linguistics, and the Journal of Psycholinguistic Research. Analyzing her recent publications reveals a consistent focus on experimental approaches to syntactic phenomena. Her work demonstrates a strong interest in gradient acceptability, syntactic priming, cross-linguistic comparison (particularly involving English and Cantonese), and the relationship between grammatical theory and language processing. Her 2022 book Gradient Acceptability and Linguistic Theory represents a major contribution that synthesizes experimental findings with theoretical linguistics frameworks. Francis plays an active role in the broader linguistics community. She regularly teaches short courses at the Linguistic Society of America Linguistic Institutes, serves on the LSA Ethics Committee, and is on the editorial board of Glossa Psycholinguistics. She has also edited several books and special journal issues, including Mismatch: Form-Function Incongruity and the Architecture of Grammar (2003) and Polymorphous Linguistics: Jim McCawley's Legacy (2005). As an educator and administrator, Francis has supervised numerous graduate students and currently serves as Associate Head of the Department of English at Purdue. Her Experimental Linguistics Lab provides research opportunities for students interested in the intersection of theoretical syntax and experimental methodology. While she recently announced she will not be accepting new graduate students for the 2025-2026 cycle, her established mentorship record demonstrates her commitment to training the next generation of linguists. The Experimental Linguistics Lab, which she directs, serves as a hub for research combining theoretical linguistics with experimental methods. Her collaborative work extends across disciplines, including collaborations with researchers in speech-language pathology, cognitive science, and computational linguistics, reflecting the interdisciplinary nature of modern linguistic research.
John Kingston is a Professor of Linguistics and Director of the Phonetics Lab at the University of Massachusetts Amherst, where he has been since 1990. He holds a BA and MA from the University of Chicago (1976–1977) and a PhD from UC Berkeley (1985). His research focuses on the interplay between phonetics and phonology, particularly speech perception and its influence on phonological representations. He co-founded the Laboratory Phonology Conference series in 1987 and has conducted fieldwork on Otomanguean languages. His work emphasizes experimental methods to study phonological questions, including studies on vowel perception, tone systems, and cross-linguistic phonetic patterns. Kingston’s academic journey includes roles at the University of Texas, Austin (1984–1986) and Cornell University (1986–1990). His research explores how auditory processing and linguistic knowledge shape speech perception, with notable contributions to understanding tonogenesis, perceptual contrast effects, and vowel category learning in second languages. He collaborates on grants examining Ganong effects and phonological inventories, advocating for theories that bridge perceptual and structural aspects of language. His lab, the Phonetics Lab, supports experimental work on speech perception and production. Kingston is also the Honors Program Coordinator, mentoring students in linguistics and related fields. Despite no explicit awards listed, his extensive publications and conference leadership reflect his scholarly impact.
Ellen Riloff serves as Department Head and Professor in the Department of Computer Science at the University of Arizona, where she leads research at the intersection of natural language processing (NLP) and artificial intelligence. Her work bridges theoretical advancements with real-world applications in social computing, planetary science, and crisis response systems. Education: Ph.D. in Computer Science, University of Massachusetts at Amherst (1994) Research Focus: Dr. Riloff specializes in affective computing and information extraction , developing techniques to recognize emotion, social cues, and embodied expressions in text. Her methodologies frequently employ bootstrapping, stacked learning, and semantic lexicon induction. Recent projects address crisis informatics (e.g., social cue recognition in emergencies) and interdisciplinary applications like the Mars Target Encyclopedia for planetary science data extraction. Publication Trends: Analysis of her 15 most recent publications (2021–2025) reveals three dominant trajectories: (1) affective event modeling in social contexts with applications to crisis response; (2) domain-specific NLP for planetary science and food systems; and (3) advanced language model techniques including retrieval-augmented generation and multi-view prompting. Her work increasingly integrates deep learning with traditional linguistic features. Grants and Leadership: Dr. Riloff has directed multiple NSF-funded projects, including RI: Small: Recognizing Implicit Personal States in Natural Language (2016) and RI: Small: Acquiring Domain Knowledge from Text through Cooperative Bootstrapping (2010). These initiatives pioneered bootstrapping frameworks for affective event recognition and information extraction. She also co-organized the Workshop on Pattern-based Approaches to NLP (2023), highlighting her leadership in advancing hybrid NLP methodologies. Collaborative Infrastructure: She co-developed the Mars Target Encyclopedia—a large-scale information extraction system that processes planetary science literature to create structured databases of Mars surface targets. This project demonstrates her commitment to building reusable scientific infrastructure through NLP.
Ilker Yildirim is an Assistant Professor of Psychology at Yale University, where he leads the Cognitive and Neural Computation Lab (CNCL). He received his Ph.D. from the University of Rochester in 2014. His research focuses on understanding how perception transforms raw sensory signals into meaningful representations of objects and scenes through computational modeling approaches. His research interests include: Computational modeling of visual perception and cognition Intuitive physics and physical reasoning Bayesian inference and probabilistic models in cognition Integration of graphics and physics engines in cognitive modeling Neural mechanisms of object representation Attention and resource allocation in dynamic scenes Yildirim's lab develops computational frameworks that bridge cognitive processes with neural mechanisms, using tools including probabilistic models, simulation engines, and deep learning. His work often involves testing these models through behavioral and neural experiments to create unified accounts of perception and cognition. His recent publications reveal a strong focus on how humans perceive physical properties of objects, particularly soft materials and liquids, and how attention dynamically allocates resources during scene perception. Among his notable achievements is receiving an NSF CAREER award for his project "CAREER: CompCog: Reverse-engineering neural mechanisms of object cognition with multilevel computational modeling," which will run from 2025 to 2030. His Nature Human Behaviour paper by Qi Lin was featured on CBS News, Germany's NZZ, and Wired Italy. Yildirim mentors a diverse group of graduate students and postdocs working on various aspects of perception, cognition, and computational modeling. His students have received prestigious awards including the Jane Olejarczyk Award and the Leonard J. Savage Prize.
Linda Argote is the Thomas Lord Professor of Organizational Behavior and Theory at the Tepper School of Business, Carnegie Mellon University. She serves as Director of the Center for Organizational Learning, Innovation, and Knowledge. Her academic journey includes prior roles such as David M. Kirr Professor and Carnegie Bosch Professor. She holds a Ph.D. in Organizational Psychology from the University of Michigan and a B.S. in Psychology from Tulane University. Her research focuses on organizational learning, innovation, knowledge transfer, and group processes, with notable contributions to transactive memory systems and team performance. Argote has received prestigious awards like the Joseph E. McGrath Award and an Honorary Doctorate. She actively contributes to academic leadership through roles in editorial boards, grant reviews, and university committees. Her teaching includes courses on organizational learning and strategic management.
Matthew B. Blaschko is a Professor in the Department of Electrical Engineering at KU Leuven, Belgium. He serves as director of the KU Leuven ELLIS unit and is a fellow in the ELLIS Health program. He is a Core PI in the Flanders AI Research Program, working as a workpackage lead for Decision Support Systems and Medical Imaging. Blaschko is also a member of the KU Leuven Institute for Artificial Intelligence and one of the leaders of the working group on Machine Learning and Data Science. Professor Blaschko received his B.S. from Columbia University, M.S. from the University of Massachusetts Amherst, and Dr. rer. nat. from Technische Universität Berlin (awarded for work at Max Planck Institutes Tübingen). He was a Newton International Fellow at the University of Oxford and received his Habilitation (HDR) from École Normale Supérieure de Cachan. Prior to joining KU Leuven, he was a Permanent Research Scientist in the INRIA Saclay Research Center and a Faculty Member at Ecole Centrale Paris. His research focuses on machine learning techniques applied to visual data, with particular emphasis on calibration in deep learning, medical image analysis, and federated learning. Blaschko's work bridges theoretical foundations with practical applications, as evidenced by technology developed in his research being incorporated into MONA, software for ophthalmic image analysis. His research group has made significant contributions to the fields of model calibration, uncertainty estimation, and medical imaging analysis, with recent publications showing strong trends toward improving reliability of AI systems in medical contexts and advancing theoretical understanding of calibration metrics. Professor Blaschko has been recognized with several awards including the Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award, Best Paper Award at CVPR 2008, Main Award at DAGM 2008, and Best Student Paper Award at ECCV 2008. Professor Blaschko has supervised numerous PhD and Master's students, with current and former students including Deniz Soysal, Claire Marchal, Dongli Xu, Sebastian Gruber, Jiameng Li, Marco Mezzina, and many others working on diverse topics from Alzheimer's disease analysis to surgical phase recognition. His research has been supported by various funding sources including the Flanders AI Research Program. He has co-organized several influential workshops including the "Another Brick in the AI Wall: Building Practical Solutions from Theoretical Foundations" at CVPR 2025, Commands 4 Autonomous Vehicles workshop at ECCV 2020, and the Learning from Limited Labeled Data workshop series at NIPS 2017 and ICLR 2019. His laboratory focuses on machine learning for medical image analysis, with applications in ophthalmology, neurology, and surgical robotics. The group maintains active collaborations with medical institutions and participates in international challenges such as the KNee OsteoArthritis Prediction (KNOAP2020) challenge.
Hyowon Gweon is an Associate Professor in the Department of Psychology at Stanford University. As the leader of the Social Learning Lab, her research focuses on how humans learn from others and help others learn, employing interdisciplinary methods including developmental, computational, and neuroimaging approaches. She holds a PhD in Cognitive Science from MIT (2012) and joined Stanford in 2014 after a postdoc at MIT. Her research interests span computational approaches to social learning, developmental psychology, neuroimaging, and education. She has received notable awards such as the APS Janet Spence Award (2020), James S. McDonnell Scholar Award (2018), and Marr Prize (2010). Her work explores topics like counterfactual reasoning, social cognition in infants, and embodied AI benchmarks. Labs/Teams: Social Learning Lab Key Themes: Prosocial behavior, theory of mind, cognitive development, and human-AI interaction. Her recent articles investigate infant gaze behavior, temporal reasoning in children, and strategic communication in preschoolers. Grants and advising details are not explicitly listed in the provided text.
Haidy Geismar serves as Professor of Anthropology at University College London (UCL), where she is affiliated with the Department of Anthropology. She co-directs UCL's Digital Anthropology Program and is the founding director of the School for the Creative and Cultural Industries at UCL East. Geismar also serves as curator of the UCL Ethnography collections, facilitating exhibitions with community groups, students, and artists. Her institutional affiliations extend to the Tate and Victoria and Albert Museum in London, where she maintains long-term professional relationships. Geismar's research explores the intersections of intellectual and cultural property, indigenous rights, and colonial legacies with digital technologies and museum practices. Her work focuses on how digital platforms transform cultural representation, particularly in the South Pacific region including Vanuatu and New Zealand. She investigates how communities engage with digital collections, examining both the materiality of digital objects and their social implications. Her research demonstrates how indigenous knowledge systems interact with contemporary digital infrastructures to create new forms of cultural expression and preservation. Analysis of Geismar's recent publications reveals a clear trajectory toward examining the material dimensions of digital culture within museum and archival contexts. Her work increasingly addresses how digital technologies reshape cultural heritage practices while maintaining critical attention to postcolonial perspectives. There is a notable emphasis on sensory experiences in digital spaces, the politics of representation in digital collections, and the ways digital platforms enable or constrain indigenous knowledge systems. Geismar's professional recognition includes: Mellon Research Fellow at Tate (2019-2020) Fellow of the Royal Anthropological Institute Chairperson of Te Maru o Hinemihi (since January 2024) Photography Committee member of the Royal Anthropological Institute (2016-2024) While specific grant information isn't detailed in the available materials, Geismar's sustained research projects indicate successful funding acquisition. Her long-term collaborations with the Vanuatu Cultural Centre since 2000 and with Maori curators in New Zealand since 2004 suggest significant research support. Current projects like 'Finding Photography' and 'Collecting in Context' demonstrate ongoing research activity requiring funding. Geismar has created the Culture Lab at UCL East, an experimental curatorial space designed as a collections-rich environment for innovative display and research. This facility serves as a hub for her work with the School for the Creative and Cultural Industries, which brings together academic research with creative practice. The Culture Lab hosts exhibitions like 'Power!' and facilitates collaborations between university researchers, community groups, and students, creating a dynamic space for exploring the intersection of material culture and digital technologies.
University of North Carolina at Chapel HillUnited States
Lucia Binotti is a Professor of Spanish and Digital Humanities at the University of North Carolina at Chapel Hill (UNC-CH), with dual affiliations in the Department of Romance Studies and interdisciplinary Digital Humanities initiatives. She serves as Graduate Advisor for Hispanic Linguistics and Director of the Honors Study Abroad Program in Rome. Her work bridges traditional philology with cutting-edge digital scholarship, emphasizing immersive learning experiences through virtual reality (VR) and augmented reality (AR) technologies. Binotti holds a Ph.D. from UC Santa Barbara (1990), an MA (1985), and a BA (1984) in Romance Philology from the Università degli Studi di Pisa. Her research focuses on linguistic identity, cultural heritage preservation, and innovative educational platforms like Amirabilia—the world’s first experiential learning platform. Notable projects include nXimena (healthcare training for Latinx abuse survivors) and Entiendelo (a linguistic/cultural navigation app for Latinx communities). Her scholarly interests span Early Modern Spanish literature, digital humanities applications, and the intersection of technology with social justice. She has directed grants totaling over $500K for projects involving AR/VR integration in education, including collaborations with the UNC Health Science Library and NCSU’s DELTA initiative. Recent awards include the John K. Walsh Award (2010) and Digital Innovation Fellow status (2014). Grants & Leadership: CFE-Lenovo Technology Grants (2018–2019) for AR/VR expansion iLRN 2020 International Co-Chair XR Faculty Learning Community Leader (UNC) Teaching: Courses span from introductory Hispanic literature to advanced topics like medieval intellectual history and digital humanities methodologies. Binotti’s work emphasizes ‘place, presence, and perspective’ in Cyber-Humanities education, particularly addressing marginalized communities through technology-driven solutions.
Dr. Vincent Fortuin is a tenure-track Assistant Professor at the Technical University of Munich (TUM) and a research group leader at Helmholtz AI in Munich. He leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group and holds multiple prestigious fellowships including the Branco Weiss Fellowship. His academic affiliations include the TUM School of Computation, Information and Technology, the Konrad Zuse School of Excellence in Reliable AI, and the Munich Center for Machine Learning. Dr. Fortuin earned his BSc in Molecular Life Sciences from the University of Hamburg (2012-2015), followed by an MSc in Computational Biology and Bioinformatics from ETH Zürich (2015-2017), where he received the ETH Excellence Scholarship and the Willi Studer Prize. He completed his PhD in Machine Learning at ETH Zürich (2017-2021) under the supervision of Gunnar Rätsch and Andreas Krause, supported by a Swiss Data Science Center PhD Fellowship. Prior to joining TUM, he was a Research Fellow at St. John's College, University of Cambridge (2022-2023). His research focuses on the intersection of Bayesian statistics and deep learning, specifically developing methods for more robust, data-efficient AI systems with reliable uncertainty estimates. His work addresses critical limitations in standard deep learning approaches, particularly their tendency to be overconfident in predictions and require large datasets for training. He investigates better priors and more efficient inference techniques for Bayesian deep learning, deep generative modeling, meta-learning, and PAC-Bayesian theory, with applications in scientific and biomedical domains. Dr. Fortuin's recent publications demonstrate a consistent focus on improving uncertainty quantification in deep learning systems, with increasing emphasis on practical applications in scientific contexts. His work spans from theoretical foundations of Bayesian deep learning to practical implementations in protein design, materials science, and medical applications. A notable trend is his exploration of how to make Bayesian methods more scalable and applicable to modern large-scale AI systems while maintaining theoretical guarantees. Branco Weiss Fellowship (2023) St John's College Research Fellowship (2022) Swiss National Science Foundation Postdoc.Mobility Fellowship (2022) Swiss Data Science Center PhD Fellowship (2018) ETH Excellence Scholarship (2015) Willi Studer Award (2018) Dr. Fortuin actively supervises PhD and Master's students through his ELPIS research group at Helmholtz AI. He serves as a regular reviewer and area chair for major machine learning conferences and is an action editor for TMLR. He co-organizes the Symposium on Advances in Approximate Bayesian Inference (AABI) and the ICBINB initiative, demonstrating his commitment to advancing the field through community building. His research group receives funding from multiple sources including Helmholtz AI, the Branco Weiss Fellowship, and collaborations with international institutions. Dr. Fortuin leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group at Helmholtz AI, which focuses on fundamental machine learning research motivated by real-world scientific problems. The group collaborates extensively with researchers across Helmholtz centers and international institutions, particularly in biomedical applications where reliable uncertainty estimates are crucial.
