Olga Fink is a Tenure Track Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Department of Intelligent Maintenance and Operations Systems (IMOS) within the School of Architecture, Civil and Environmental Engineering (ENAC). She also holds roles in PhD program committees for Civil and Environmental Engineering (EDCE) and Robotics, Control, and Intelligent Systems (EDRS). Her research focuses on machine learning for infrastructure monitoring, predictive maintenance, and physics-informed AI models. She teaches courses on machine learning, data science for infrastructure, and advanced deep learning topics. Fink advises multiple PhD students and is involved in interdisciplinary projects such as ThermoNeRF (multimodal 3D thermal modeling) and physics-informed neural networks for fault diagnostics. Her work bridges AI and engineering with applications in smart infrastructure, energy systems, and industrial IoT. Education: PhD in Engineering (inferred from role) Affiliations: IMOS Lab, ENAC-SGC, EPFL PhD Committees (EDCE, EDRS) Key Research Themes: Explainable AI, Digital Twins, Structural Health Monitoring, Domain Adaptation Her publications (2023–2025) emphasize robust AI for industrial systems, including fault detection in high-voltage equipment, multimodal data fusion, and physics-consistent models. She collaborates on EU and industry-funded projects, focusing on real-world applications like predictive maintenance and energy efficiency.
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.
Aapo Hyvärinen is a Professor of Computer Science at the University of Helsinki , affiliated with the Helsinki Institute for Information Technology and the Helsinki Probabilistic Machine Learning Lab . He previously held the position of Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London (2016-2019). Education : Undergraduate Mathematics at University of Helsinki, Vienna, and Paris; Ph.D. in Information Science from Helsinki University of Technology (1997) His research focuses on machine learning and computational neuroscience , particularly: Independent Component Analysis (ICA) Natural Image Statistics Causal Representation Learning Neural Signal Processing Applications to brain imaging (MEG, CryoEM) Recent publications emphasize causal discovery , identifiable machine learning , and nonlinear ICA . Key projects include: VETURI (AI for health) DIGIMIND (AI in mental health) CIFAR grants (2022-2025) Scientific awards : Highly Cited Researcher (2010) He serves as Action Editor for the Journal of Machine Learning Research and Neural Computation , and has held Area Chair roles at NeurIPS, ICML, ICLR, AISTATS, and UAI conferences. His work bridges theoretical machine learning with neuroscience and philosophical implications of artificial intelligence .
Clark Olson is a Professor in the Division of Computing & Software Systems at the University of Washington Bothell, part of the School of Science, Technology, Engineering & Mathematics. He earned his Ph.D. in Computer Science from UC Berkeley (1994), M.S. in Electrical Engineering (1990), and B.S. in Computer Engineering (1989) from the University of Washington, Seattle. Education: Ph.D. in Computer Science (2017) from University of California, Berkeley M.S. in Electrical Engineering (1990) from University of Washington, Seattle B.S. in Computer Engineering (1989) from University of Washington, Seattle His research focuses on computer vision, robot navigation, and clustering algorithms. He has developed techniques for Mars rover terrain mapping, subspace clustering, and geometric feature matching. His work bridges theory and application in autonomous systems and image analysis. Analysis of his publications reveals expertise in computer vision (8 papers), clustering algorithms (4 papers), and robotics (5 papers). Key subtopics include Mars exploration (3 papers), Hough transforms (3 papers), and probabilistic methods (3 papers). Professor Olson teaches courses ranging from introductory programming (CSS 161-162) to advanced topics in computer vision (CSS 487-587) and algorithm design (CSS 549). He also advises on the CSSE Capstone (CSS 497) projects requiring rigorous prerequisites and structured evaluation criteria.
Prof. Christian Heipke is a distinguished academic serving as Dean of the Faculty of Civil Engineering and Geodetic Science at Leibniz University Hannover, Germany. He also holds the position of Executive Director at the Institute of Photogrammetry and GeoInformation (IPI), one of the leading research institutions in geospatial sciences within the faculty. His leadership extends across multiple committees including the Curriculum and Teaching Committee, Admissions and Examination Boards for Geodetic Science and Geoinformatics, and Navigation and Environmental Robotics. As a Professor at IPI, he maintains active research while overseeing significant academic and administrative responsibilities at the university. Professor Heipke's research spans multiple domains within geospatial sciences, with particular emphasis on: Advanced photogrammetric techniques and algorithms Remote sensing applications for environmental monitoring Computer vision approaches for geospatial data analysis Urban development monitoring using satellite imagery Machine learning applications in geoinformatics Disaster prediction and management systems His recent scholarly output reveals a strong focus on integrating cutting-edge computer vision and deep learning techniques with traditional photogrammetric methods. Analysis of his 15 most recent publications shows a clear trajectory toward more sophisticated AI-driven approaches for processing geospatial data, with particular attention to time-series analysis, uncertainty quantification, and multi-view systems. His work bridges theoretical advancements with practical applications in flood forecasting, deforestation monitoring, urban planning, and construction materials analysis. The geographic scope of his research has expanded significantly, with recent projects focusing on international case studies in the Philippines and tropical regions. Professor Heipke leads the Institute of Photogrammetry and GeoInformation, a major research hub that has celebrated 75 years of contributions to the field. His leadership extends to the Graduiertenkolleg 2159: "Integrity and Collaboration in Dynamic Sensor Networks," where he serves as a professor overseeing doctoral research. The institute maintains state-of-the-art facilities for processing satellite imagery, aerial photography, and developing novel algorithms for geospatial data analysis. Under his direction, the institute has strengthened its international collaborations and interdisciplinary research approaches, particularly in addressing Sustainable Development Goals through geospatial technologies.
Andrea Cavallaro is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) and Director of the Idiap Research Institute. He holds dual appointments in the School of Engineering (STI) within the Institute of Electrical Engineering and Measurements (IEM) and the School of Engineering's Education Unit (SEL-ENS). His research focuses on machine learning for multimodal perception, privacy-preserving AI, and autonomous systems. Cavallaro earned his PhD in Electrical Engineering from EPFL in 2002 and has held leadership roles including Director of Research at Queen Mary University of London and Turing Fellow at The Alan Turing Institute. Education: PhD in Electrical Engineering (EPFL, 2002) Leadership: Idiap Director, Affiliate at ELLIS Society Editorial Roles: Editor-in-Chief of Signal Processing: Image Communication (2020–2023), Senior Area Editor for IEEE Transactions on Image Processing Research Interests: Machine learning for audio-visual sensing, privacy in AI, autonomous systems perception, and ethical AI frameworks. Key projects include AlignAI (trustworthy AI alignment) and CORSMAL (multimodal object manipulation). Recent articles explore privacy-aware AI models, adversarial attacks, and multimodal perception systems. His work bridges theoretical advancements with practical applications in robotics, healthcare, and education. Awards include the Royal Academy of Engineering Teaching Prize and IAPR Fellowship. Teaching: Leads courses on deep learning ethics and multimodal AI at EPFL. Advising: Supervises 11 PhD students in areas like privacy-preserving algorithms and autonomous systems. Labs/Teams: Coordinates Idiap’s Audiovisual Intelligence and Learning Lab (LIDIAP) and collaborates on projects like GraphNEx (explainable AI via graph neural networks).
Prof. Bryan Ford leads the Decentralized/Distributed Systems (DEDIS) lab at EPFL. He focuses on secure decentralized systems, including blockchain technology, privacy, and systems security. He earned his Ph.D. from MIT and held faculty positions at Yale University and EPFL. His work spans distributed consensus protocols, peer-to-peer networking, and privacy-preserving systems. Key projects include QuePaxa (timeout-free consensus), UIA (global connectivity for mobile devices), and MedCo (secure healthcare data sharing). He advises numerous PhD students and contributes to open-source projects like Bitcoin collective signing and privacy networks like Riffle. Education: Ph.D., MIT; Postdoctoral work at Yale Research interests include blockchain scalability, consensus algorithms, and cryptographic privacy. His lab develops systems like TRIP for coercion-resistant voting and F3B to mitigate blockchain front-running. His work on NAT traversal and peer-to-peer protocols (e.g., STUN/ICE) remains foundational in network architecture. He emphasizes practical, auditable security solutions such as CertiKOS and atomic cross-chain transactions (Atom). Notable contributions: CoSi (collective signing), OmniLedger (sharded blockchain), and privacy-preserving protocols like PURBs (Protected Unsealable Recursive Boxes). His lab collaborates with Swiss Post to audit e-voting systems and designs democratic cryptocurrencies like PoPCoin.
John F. Canny is the Paul and Stacy Jacobs Distinguished Professor of Engineering at the University of California, Berkeley, in the Department of Electrical Engineering and Computer Science (EECS). He joined the faculty in 1987 and is affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Center for New Media (BCNM), and other research institutes. His research focuses on artificial intelligence, robotics, human-computer interaction, and computational geometry. Canny is renowned for developing the Canny edge detector and contributions to motion planning, privacy-preserving algorithms, and real-time simulation. Education: B.Sc. in Computer Science and Theoretical Physics (Adelaide University, 1979), B.E. (Hons) in Electrical Engineering (Adelaide University, 1980), M.S. and Ph.D. in Electrical Engineering (MIT, 1983 and 1987). Research Interests: His work spans AI, robotics (including universal planar manipulation), HCI (e.g., MultiView video conferencing), and security (privacy in collaborative filtering). He has pioneered algorithms for edge detection, non-linear FEM simulation, and computational algebra. Awards: ACM Fellow (2020), Okawa Research Grant (2003), AAAI Classic Paper Award (2002), NSF PYI (1989), Packard Fellowship (1988), and ACM Doctoral Dissertation Award (1987). Advising & Grants: Advised over 30 Ph.D. students, many of whom hold prominent roles in academia and industry. His grants include NSF and Packard funding for foundational research in robotics and computational methods. He currently teaches CS188: Introduction to Artificial Intelligence. Labs/Teams: Leads research at the Berkeley Institute of Design (BiD), focusing on RISC robotics, activity-based computing, and privacy-enhanced telepresence systems.
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.
Jelle Hellings is an Assistant Professor in the Department of Computing and Software at McMaster University , Canada. His research focuses on high-performance large-scale data management systems with a strong theoretical and algorithmic component, including resilient systems (blockchains) , graph databases , and external-memory algorithms . He previously worked as a Postdoc Scholar at the University of California, Davis and earned his PhD from Hasselt University in Belgium. Education: Doctor of Sciences in Computer Science (2018), Hasselt University Master of Science in Computer Science and Engineering (2011), Eindhoven University of Technology His research interests include scalable resilient systems with Byzantine fault tolerance, database theory, graph query languages, constraints on graph data, and external-memory algorithms for large graph datasets. He has authored numerous high-impact publications on blockchain-based resilient systems, query optimization in graph databases, and theoretical advancements in relation algebra expressiveness. Hellings actively contributes to academic service through program committee memberships and tutorial organization, and he currently teaches courses on future resilient databases and foundational computer science topics.
Carlotta Domeniconi is an Associate Professor in the Department of Computer Science at George Mason University. Her research focuses on machine learning , data mining , and clustering techniques with applications in text mining , social network analysis , and financial data mining . Research Interests include Classification and clustering algorithms Subspace and multi-view learning Bayesian and ensemble methods Scientific Awards include the NSF CAREER award (2005-2010) ORAU Ralph E. Powe Junior Faculty Enhancement Award (2004) She has taught courses such as Design and Analysis of Algorithms , Data Mining , and Advanced Pattern Recognition at George Mason University since 2002. Her projects have been funded by the National Science Foundation, US Army, and other organizations.
Oswald Lanz is a tenured full professor at the Faculty of Engineering of the Free University of Bozen-Bolzano , leading the Visual Computing Lab . He holds a Ph.D. in Computer Science and a Mathematics degree from the University of Trento. Prior to his current role, he was a researcher and head of research at FBK Trento. He is an endowed professor collaborating with Covision Lab , an AI hub in Bressanone, and coordinates the board of professors for the PhD in Computer Science program since 2025. His research focuses on Computer Vision, Deep Learning, and Video Analytics , with applications in sports technology, medical imaging, and industrial automation. Key achievements include the Amazon AWS Machine Learning Research Award (2020) , ACM Multimedia Best Paper (2015) , and Best Student Paper at ICIAP (2007) . He co-organized the ELLIS-VISMAC Winter School (2025) and chaired ICIAP 2019 . His work spans novel view synthesis, action recognition, and anomaly detection, supported by patents in video tracking and detection. He teaches courses like Deep Learning and Artificial Intelligence in undergraduate and graduate programs. Recent projects such as 5VREAL integrate 5G, edge computing, and AI for sports analysis. His collaborations bridge academia and industry, exemplified by his role in Covision Lab and multidisciplinary initiatives like DSS4LCO for food supply chains. Lanz’s publications emphasize spatiotemporal modeling, neural architecture search, and hybrid machine vision systems.
Prof. Dr. Julia Vogt is an Assistant Professor at the Department of Computer Science at ETH Zürich, leading the Professur für Medizin. Datenwiss. Her research focuses on medical machine learning, data science, and AI applications in healthcare. She specializes in developing interpretable AI systems for clinical decision support, particularly in pediatric diabetes management, medical imaging analysis, and anomaly detection. Her work bridges translational gaps by emphasizing causal approaches and clinical validation. Her academic role includes teaching courses like the Data Science Lab (263-3300-00L/10L) and Topics in Medical Machine Learning (263-5100-00L). Her lab's research spans predictive modeling for nocturnal hypoglycemia, echocardiogram analysis for pulmonary hypertension detection, and multimodal learning in radiology. She also contributes to national pediatric data initiatives like SwissPedHealth. Key technical areas include concept bottleneck models, stochastic AI frameworks, and generative models for medical signal denoising. Her work often emphasizes model interpretability, fairness, and robustness to distribution shifts. She collaborates on projects involving wearable devices for pediatric monitoring and AI-driven rehabilitation tools for post-stroke gait analysis.
Aviad Levis is an Assistant Professor at the University of Toronto's Department of Computer Science, starting July 2024. He is affiliated with the Dunlap Astronomical Data Science and Technology Group (DADDAA) and collaborates with the Toronto Computational Imaging Group alongside Kyros Kutulakos and David Lindell. Previously, he was a postdoctoral researcher at Caltech's Computing + Mathematical Sciences department under Katherine Bouman, working with the Event Horizon Telescope (EHT) collaboration. PhD in Electrical Engineering from the Technion (supervised by Yoav Schechner) Research focuses on computational imaging tools at the intersection of AI and physics Develops algorithms for 3D tomography in both cloud physics and black hole imaging Recipient of ERC Synergy grant for CloudCT space mission His research spans two major domains: Computational Climate Imaging through cloud tomography to improve climate models, and Black Hole Imaging with the EHT collaboration. He pioneered methodologies for 3D cloud structure recovery using scattered sunlight and contributes to dynamic 3D reconstructions of black hole environments. Current interests include non-linear inverse problems, equation discovery from data, and ML-accelerated scientific simulations. Recent publications highlight advancements in atmospheric tomography and black hole emission modeling. His work on CloudCT involves coordinated nano-satellites for 3D cloud imaging, while EHT contributions include first images of Sagittarius A* (2022) and ongoing development of algorithms for 3D structure recovery. The ERC Synergy grant underscores his impact on climate imaging technology. Personal Website Work Email
Jiajun Wu is an Assistant Professor of Computer Science and, by courtesy, of Psychology at Stanford University. He holds multiple affiliations including membership in Bio-X, Faculty Affiliate status at the Institute for Human-Centered Artificial Intelligence (HAI), and membership in both the Wu Tsai Human Performance Alliance and Wu Tsai Neurosciences Institute. Dr. Wu earned his Ph.D. and S.M. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology before joining Stanford. Dr. Wu's research program focuses on creating AI systems that understand and interact with the physical world through the integration of computer vision, machine learning, robotics, and cognitive science. His work emphasizes physics-based modeling combined with deep learning to develop systems capable of perceiving, reasoning about, and predicting physical interactions. Key research areas include 3D scene understanding, neurosymbolic AI approaches, multimodal perception (combining vision, sound, and language), and embodied intelligence for robotics applications. His lab develops novel frameworks that bridge the gap between neural networks and symbolic reasoning to create more interpretable and robust AI systems. Analysis of Dr. Wu's recent publications reveals a strong trajectory toward integrated multimodal understanding for embodied AI. His work increasingly combines vision, sound, and language processing with physical reasoning to create systems that can interact meaningfully with the physical world. There's a clear progression from foundational computer vision research toward practical robotics applications, with significant emphasis on foundation models for robotics, sim2real transfer techniques, and creating comprehensive datasets for embodied AI research. Dr. Wu's exceptional contributions have been recognized with numerous prestigious awards including the NSF CAREER award (2024), Young Investigator Programs from ONR (2024) and AFOSR (2023), the Okawa research grant (2024), and being named to IEEE Intelligent Systems' 'AI's 10 to Watch' (2024). He has received multiple best paper awards at leading conferences including ICRA (2024), SIGGRAPH Asia (2023), and CoRL (2023). Dr. Wu actively mentors a large cohort of students across multiple levels, serving as primary advisor for doctoral candidates, master's students, and numerous independent researchers. His research is supported by substantial funding from major technology companies including Google, Meta, Amazon, Samsung, and J.P. Morgan, as well as government agencies like NSF, ONR, and AFOSR, reflecting the significance and impact of his work in physical AI and multimodal perception systems. Dr. Wu leads a dynamic research group at Stanford that collaborates extensively with the Wu Tsai Neurosciences Institute and Institute for Human-Centered AI. Current projects include developing neurosymbolic models for computer graphics, creating multisensory datasets like OBJECTFOLDER 2.0 for sim2real transfer in robotics, and building foundation models for embodied intelligence that can understand and manipulate objects with human-like physical intuition.
