Pascal Frossard is a Full Professor at the Department of Electrical Engineering in the School of Engineering (STI) at EPFL, with a courtesy appointment in the School of Computer and Communication Sciences. He founded and directs the LTS4 laboratory since 2003, co-leads the EPFL AI Center and Swiss Data Science Center, and serves as Associate Dean for Research at STI. Research Focus: Machine Learning, Graph Signal Processing, AI Applications in Healthcare, Computer Vision Academic Leadership: IEEE Fellow, ELLIS Fellow, Conference Chair roles Key Projects: Digital Pathology for Oncology, Cardiac Digital Twins, Robust Machine Learning Research Interests: His work bridges signal processing, machine learning, and applied mathematics, emphasizing biomedical applications. Recent research includes adversarial robustness in classifiers, network representation learning, and 360-degree video analysis. Scientific Awards: IEEE Fellow ELLIS Fellow Leadership in IEEE technical committees Advising & Grants: Supervised 20+ PhD students and postdocs. Secured major grants from PHRT, Hasler Foundation, FNS-Sinergia, Armasuisse, Google, and Cisco.
Romain Lopez is an Assistant Professor of Computer Science and Biology at New York University, with a joint appointment in the Courant Institute of Mathematical Sciences and the Department of Biology. He will be joining NYU in September 2025, bringing expertise at the intersection of machine learning and computational biology. Prior to joining NYU, he was a Postdoctoral Fellow at Genentech and Stanford Medicine from 2021 to 2025, working with Jonathan Pritchard and Aviv Regev. Dr. Lopez received his educational training at prestigious institutions: PhD in Computer Science (2021) from the University of California, Berkeley, advised by Mike Jordan and Nir Yosef M.S. in Applied Mathematics (2016) from École polytechnique, Palaiseau, France Dr. Lopez's research focuses on developing machine learning methods to understand biological systems at the cellular level. His work bridges computational techniques with biological applications, particularly in single-cell and spatial omics analysis. He pioneered probabilistic approaches for single-cell analysis with scVI and co-developed scvi-tools, now widely adopted tools in the field. His research spans deep generative models, causal inference, perturbation modeling, and representation learning for biological data. His publication record demonstrates a consistent trajectory of innovation in computational biology, with recent work focusing on spatial biology, disentangled representations of cellular perturbations, and causal modeling of cellular responses. He has made significant contributions to the field of single-cell analysis, developing methods that help scientists interpret complex cellular data and predict how cells respond to various perturbations. Dr. Lopez has received numerous honors and awards for his research: Best Paper Award from the ICML Workshop on AI for Science (2024) Best Paper Award Honorable Mention from the AAAI Conference on Artificial Intelligence (2021) Best Student Poster Award from the ICML Workshop on Computational Biology (2019) UC Berkeley EECS Departmental Graduate Fellowship (2016) Carnot Foundation Fellowship (2016) Monahan Foundation Fellowship (2016) French National Defence Medal, Bronze Echelon (2014) At NYU, Dr. Lopez will lead the Biological Machine Learning group, which develops probabilistic machine learning methods to uncover biological mechanisms governing cellular behavior and disease. His lab focuses on creating tools that transform complex cellular data into biological insights, with applications in understanding cancer, immune responses, and fundamental cellular processes. His work has significant implications for precision medicine and drug discovery.
LEONG Tze Yun is a Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). She holds S.B., S.M., and Ph.D. degrees in Computer Science from the Massachusetts Institute of Technology (MIT). Her academic career spans both research and industry experience, with significant contributions to the fields of artificial intelligence and health informatics. Dr. Leong's educational background includes: Ph.D. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.M. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.B. in Computer Science & Engineering, Massachusetts Institute of Technology Her primary research interests focus on responsible AI, dynamic decision-making, neurocognitive modeling, reinforcement learning, artificial general intelligence, and biomedical and health informatics. Her work bridges the gap between theoretical AI development and practical healthcare applications, with an emphasis on ethical considerations and human-centered design. She directs the Medical Computing Laboratory at NUS, a multidisciplinary research program exploring human-aware decision modeling in complex environments. Analysis of her recent publications reveals a strong trend toward responsible AI development, with significant contributions to reinforcement learning techniques, causal inference methods, and applications of AI in healthcare. Her work increasingly integrates ethical considerations with technical AI development, particularly evident in her 2024 publications on medical AI and human values. Her scientific recognition includes: Fellow of the American College of Medical Informatics (ACMI) Founding Fellow of the International Academy of Health Sciences Informatics (IAHSI) Member of Eta Kappa Nu (Honor Society for Electrical Engineers) Dr. Leong has supervised numerous doctoral and master's students throughout her career, many of whom have gone on to prominent positions at institutions like Google, Netflix, Mayo Clinic, and academic institutions worldwide. Her advisory work extends to significant policy development, including contributions to WHO guidance on ethics and governance of AI for health. She currently serves on the World Health Organization (WHO) Expert Group on Ethics and Governance of AI for Health, the World Economic Forum (WEF) AI Governance Alliance, and the Advisory Council on AI in Uzbekistan. Her laboratory work focuses on developing adaptive systems that evolve with changing technical functionalities, system infrastructures, usage patterns, and operational contexts, with applications spanning prediction and decision analytics, human-aware robotics, game artificial intelligence, personalized education, and assistive care for elderly with neurocognitive disorders.
Todd Sorensen is an Associate Teaching Professor at the University of California, Merced (UC Merced), affiliated with the School of Social Sciences, Humanities & Arts and the Economics and Business Management department. He holds fellowships at IZA (Institute of Labor Economics) and GLO (Global Labor Organization). Prior to UC Merced, he served as an Associate Professor at the University of Nevada, Reno (2014–2023) and an Assistant Professor at UC Riverside (2007–2014). His professional journey includes a visiting role as Visiting Associate Professor at UC Merced before his current appointment in 2022. Education: PhD in Economics, University of Arizona, 2007 Research Interests: Todd's work focuses on labor market structures , exploring topics like monopsony power and wage-setting dynamics; immigration , including its demographic and policy implications; and discrimination , particularly in criminal sentencing and labor markets. His research has been published in journals like ILR Review , Labour Economics , and the Journal of Population Economics , with findings cited in the U.S. Economic Report of the President. Scientific Awards: Fellow at IZA (2022–present) Fellow at GLO (2022–present) Advising & Grants: Todd has advised students across various levels but no specific advisees are listed. His grants and funding sources are not detailed in the provided text. He collaborates with interdisciplinary teams and maintains affiliations across UC campuses through his and his spouse’s academic careers. Labs/Teams: Engaged with research networks at IZA and GLO, focusing on labor market policies and migration studies. Active in collaborative projects analyzing immigration trends, labor market competition, and discrimination metrics.
Alexis Battle is an Associate Professor at Johns Hopkins University with appointments in Biomedical Engineering , Computer Science , and Genetic Medicine (secondary). She directs the Malone Center for Engineering in Healthcare and serves as Deputy Director of the Data Science and AI Institute . Educated at Stanford University (PhD in Computer Science, 2013), Battle transitioned to academia after leadership roles at Google. Research Focus: Battle’s work bridges genomics and machine learning , emphasizing the impact of genetic variation on human health. Her lab develops tools like Watershed to predict functional effects of rare variants, aiming to enhance rare disease diagnosis. Key themes include non-coding DNA analysis , personalized genomics , and systems biology , with applications in cardiovascular disease and neurodegenerative disorders . Publications & Awards: Over 60 peer-reviewed articles in journals like Nature , Science , and Genome Biology , with recent emphasis on single-cell transcriptomics , multiomics integration , and telomere biology . Recipient of the President’s Frontier Award (2022), Microsoft Investigator Fellowship (2019), and Searle Scholar (2016). Scientific Awards: 2022 President’s Frontier Award 2019 Microsoft Investigator Fellowship 2019 Johns Hopkins Discovery Award 2017 Johns Hopkins Catalyst Award 2016 Searle Scholar Advising & Funding: Mentors 11 PhD students, 3 undergraduates, and postdoctoral fellows. Her research is funded by NIH, Searle Scholars, and institutional grants. The Battle Lab collaborates on projects like the GTEx Consortium , focusing on gene regulation and clinical genomics .
Arthur Gretton is a Professor at University College London (UCL), leading the Gatsby Computational Neuroscience Unit and serving as director of the Centre for Computational Statistics and Machine Learning. He also works as a Research Scientist at Google DeepMind. His research focuses on causal inference, representation learning, and nonparametric hypothesis testing with applications in machine learning and computational neuroscience. Academic Affiliations Gatsby Computational Neuroscience Unit, UCL Centre for Computational Statistics and Machine Learning, UCL Google DeepMind Arthur's research spans several key areas in modern machine learning, including: Kernel methods for causal effect estimation and two-sample testing Deep learning architectures for proxy causal learning with complex confounding Adaptive gradient flows for generative modeling Nonparametric statistical tests with theoretical guarantees Applications in distributional reinforcement learning and Bayesian inference His work addresses both methodological advancements and practical implementations, particularly for high-dimensional data settings. Recent publications demonstrate his focus on causal inference with hidden confounders (AISTATS 2025), deep learning adaptivity in instrumental variable regression (ICLR 2025), and novel approaches to two-sample testing with adaptive kernel selection (NeurIPS 2024). He has also contributed extensively to distributional reinforcement learning and hypothesis testing frameworks. As an advisor, he supervises multiple PhD students including Jakub Wornbard, Zikai (Steve) Shen, and Zonghao (Hudson) Chen, while co-supervising others. His methodological contributions are implemented in various software packages available at the Gatsby Unit, including tools for kernel-based covariate shift correction, independence testing, and maximum mean discrepancy calculations.
Siva Balakrishnan is an Associate Professor at Carnegie Mellon University with a joint appointment in the Department of Statistics and Data Science and the Machine Learning Department . He holds an affiliation with the Dietrich College of Humanities and Social Sciences. Previously, he was a postdoctoral researcher at UC Berkeley's Department of Statistics, advised by Martin Wainwright and Bin Yu, and earned his Ph.D. in Computer Science from CMU's Language Technologies Institute under Jaime Carbonell. His research focuses on statistical machine learning, causal inference, and high-dimensional statistics, with notable contributions to domain adaptation, optimal transport, and robust statistics. Education: Ph.D. in Computer Science, Carnegie Mellon University (Language Technologies Institute) Postdoctoral Researcher, University of California, Berkeley (Department of Statistics) Research Interests: His work bridges theoretical foundations and algorithmic development, emphasizing robust statistical methods and their applications in causal inference, public policy, and machine learning. Key areas include nonparametric methods, optimization, and high-dimensional data analysis. He has pioneered techniques in domain adaptation, such as the RLSbench framework for relaxed label shift scenarios. Awards & Grants: Amazon Research Award (2021) Google Research Scholar Award (2021) NVIDIA Pioneer Award (2018) IMS Lawrence D. Brown Student Award (2020, 2022) National Science Foundation Grants (CCF-1763734, DMS-1713003, etc.) Professional Activities: He serves as an Associate Editor for JASA and on the editorial boards of Foundations and Trends in Statistics . His work has been featured in top venues like NeurIPS, ICML, and the Annals of Statistics. He currently holds a sabbatical at UC Berkeley's Department of Statistics (Spring 2025). Labs & Collaborations: He actively participates in the Statistics and Machine Learning Reading Group and the Causal Inference Working Group , fostering interdisciplinary research in CMU's vibrant academic community.
David W. Jacobs is a Professor in the Department of Computer Science at the University of Maryland, with a joint appointment at the University of Maryland Institute for Advanced Computer Studies (UMIACS). He also served as the interim Director of the University of Maryland Center for Machine Learning starting in 2018. University: University of Maryland School: College of Computer, Mathematical, and Natural Sciences Department: Department of Computer Science Academic Rank: Professor Education: He received his B.A. from Yale University, and M.S. and Ph.D. in Computer Science from MIT. Research Interests: His research primarily focuses on computer vision and machine learning, particularly visual object recognition, lighting variation modeling, 3D reconstruction, perceptual organization, motion understanding, and the integration of vision with graphics and human-computer interaction. A major applied contribution is the development of Leafsnap , an electronic field guide app for plant identification, which has been downloaded over 1.5 million times and used in biodiversity and educational contexts. Publication Trends: His recent scholarly output centers on deep learning, convolutional networks, residual architectures, generative models (especially GANs), and interpretability. His work often bridges theoretical insights with practical applications in vision and AI. Scientific Awards: Honorable Mention, Best Paper Award, CVPR 2000 Best Student Paper Award, UIST 2003 Best Paper Award, Eurographics 2016 2011 Edward O. Wilson Biodiversity Technology Pioneer Award for Leafsnap Teaching and Advising: He has taught advanced courses such as CMSC 422 (Introduction to Machine Learning) and CMSC 828L (Deep Learning). He mentors students through course projects and research, though specific advisees are not listed. He has collaborated with institutions like Columbia University and the Smithsonian on impactful interdisciplinary projects. Labs and Teams: He is affiliated with UMIACS and leads research efforts in vision and learning, contributing to the University of Maryland Center for Machine Learning. His team has developed several mobile applications including Leafsnap, Birdsnap, and Dogsnap, demonstrating a strong focus on real-world deployment of vision technology.
Anna Gottard is an Associate Professor of Statistics at the University of Florence, where she leads the Department of Statistics, Computer Science, and Applications. She directs the Florence Center for Data Science (FDS) and participates in the Technical Scientific Committee of the Tuscan Center for Big Data, Data Science, and AI (CBDAI). Her research focuses on multivariate statistical models, particularly graphical models, and extends to statistical machine learning, fair models, and directional data analysis. She is an Associate Editor for the Journal of the Royal Statistical Society Series A (JRSSA) and Statistical Methods & Applications (SMA). Her recent work includes Bayesian approaches for mixed graphical models, uncertainty-aware classification trees, and methodological advancements in latent uncertainty models. Her contributions span theoretical developments and applied research in interdisciplinary areas like biostatistics and sustainability. Her research interests emphasize bridging statistical theory with practical applications, including fairness in machine learning, interpretable models, and tree-based methodologies. She has actively contributed to open-source software, notably the Mix3Trees R package for mixed-effect tree models. Her work addresses challenges in variable selection, graphical model inference, and ethical AI practices. Current projects explore Bayesian frameworks for complex data structures and methodological improvements in graphical model interpretability. Anna has advised on interdisciplinary collaborations, such as studies on GDPR compliance in biobanking and epidemiological modeling of the SARS-CoV-2 pandemic in Tuscany. She collaborates with institutions like the CBDAI to advance data science applications in regional policy and healthcare. Her research trajectory reflects a commitment to both foundational statistical theory and real-world problem-solving across diverse domains.
Harri Lähdesmäki is an Associate Professor (tenured) at the Department of Computer Science, Aalto University, where he leads the Computational Systems Biology research group. His work focuses on probabilistic machine learning and deep generative models with applications in biomedicine and molecular biology. Key Research Interests: Probabilistic machine learning, deep generative models, computational biology, bioinformatics, longitudinal data modeling Contact: harri.lahdesmaki@aalto.fi | Konemiehentie 2, 02150 Espoo, Finland His recent publications highlight advancements in: Gaussian process priors for scalable deep generative models Single-cell analysis of immune repertoires in leukemia and diabetes Probabilistic deconvolution methods for RNA-seq data Epigenetic analysis using hidden Markov and mixed models Transformer-based survival prediction and missing data handling Harri’s work integrates mechanistic modeling with Bayesian inference, particularly applied to immunology, cancer biology, and early disease prediction.
Eli Ben-Michael is an Assistant Professor jointly appointed in the Heinz College of Information Systems and Public Policy and the Department of Statistics & Data Science at Carnegie Mellon University. He is affiliated with the CMU-NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC), contributing to cutting-edge research at the intersection of statistics, policy analysis, and artificial intelligence. His educational background includes a PhD in Statistics from U.C. Berkeley and undergraduate studies at Columbia University where he earned a dual degree in Computer Science and Statistics. Prior to his current position, he completed a postdoctoral fellowship at Harvard University's Institute for Quantitative Social Science and Department of Statistics. Ben-Michael's research focuses on developing innovative statistical and computational methods for causal inference and policy evaluation, with particular emphasis on integrating machine learning techniques to address complex problems in public policy and social science. His work bridges theoretical statistics with practical applications in healthcare, criminal justice, education, and social policy. Current research directions include safe policy learning, sensitivity analysis for clustered data, and methodological innovations for the synthetic control method. His publication record shows a strong trajectory in top-tier journals including Journal of the American Statistical Association, Journal of the Royal Statistical Society, and Proceedings of ICML. Recent work demonstrates increasing focus on policy-relevant applications including abortion legislation impacts, pre-trial risk assessment, and healthcare disparities, while maintaining methodological rigor in causal inference frameworks. Ben-Michael has developed open-source software tools including augsynth and multical R packages, which implement his methodological contributions for synthetic controls and multilevel calibration weighting. These packages have been adopted by researchers in multiple disciplines for causal inference applications.
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.
Dr. Hongtu Zhu is the Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science, and Genetics at the University of North Carolina at Chapel Hill (UNC). He holds affiliations with the Gillings School of Global Public Health and leads the Biostatistics and Imaging Genomics Analysis Lab. His expertise spans statistical learning, medical imaging, AI, and big data integration, with a focus on precision medicine and biomedicine. Dr. Zhu earned his PhD in Statistics from The Chinese University of Hong Kong (2000) and has held prior roles including DiDi Fellow/Chief Scientist (2018-2020) and Bao-Shan Jing Endowed Professor at MD Anderson Cancer Center (2016-2018). He has published over 345 peer-reviewed articles in top-tier journals like Nature, Science, and JASA, and actively contributes to editorial roles including Coordinating Editor of JASA. His research interests include neuroimaging analysis, knowledge graphs, and AI applications in healthcare. Notable awards include the COPSS Snedecor Award (2025), IEEE Fellowship (2025), and IMS Medallion (2027). He has mentored over 80 PhD students/postdoctoral fellows and serves on NIH grant review panels and professional organizations like the ASA's Section on Statistics in Imaging. Key Contributions: Imaging genomics, brain connectivity studies, ridesharing market optimization, medical AI frameworks Lab Innovations: Brain Imaging Genetics Knowledge Portal, Biomedical Knowledge Graph Interface Teaching: Advanced biostatistics courses (Generalized Linear Models, Deep Learning in Biomedicine) Recent work explores causal inference in healthcare, X chromosome's role in neurobiology, and AI ethics in medical vision-language models. His interdisciplinary projects bridge statistics, computer science, and clinical practice to address complex biomedical challenges.
Rama Cont is Statutory Professor of Mathematics at the University of Oxford and Head of the Oxford Mathematical and Computational Finance Group. He holds additional positions as Senior Research Fellow at the Institute for New Economic Thinking and Director of the Oxford Martin Programme on Systemic Resilience. Cont's research focuses on stochastic analysis and mathematical finance, particularly modeling extreme market risks, systemic risk, and developing pathwise approaches in stochastic analysis. His recent work explores causal functional calculus, fractional Ito calculus, and analysis of deep neural networks. His publications demonstrate consistent development of mathematical frameworks for financial risk analysis, including recent advances in causal transport theory and market microstructure modeling. Cont received the Louis Bachelier Prize in 2010 and was elected SIAM Fellow in 2017 for contributions to stochastic analysis and financial modeling.
Dr. Vasant Honavar is a Professor of Computer Science and Informatics at Pennsylvania State University, holding the Edward Frymoyer Endowed Chair. He serves as Director of the Center for Artificial Intelligence Foundations and Scientific Applications and Associate Director of the Institute for Computational and Data Sciences. His expertise spans artificial intelligence, machine learning, causal inference, and bioinformatics. Honavar has led over $60M in research grants and mentored 36 PhD students, 30 MS students, and numerous undergraduates. He is a Fellow of the AAAS and recipient of NSF Director’s Awards. Education: Ph.D., Computer Science and Cognitive Science, University of Wisconsin–Madison (1990) M.S., Computer Science, University of Wisconsin–Madison (1989) M.S., Electrical and Computer Engineering, Drexel University (1984) B.E., Electronics Engineering, Bangalore University (1982) Research Interests: His work focuses on machine learning, causal inference, knowledge representation, health informatics, and algorithmic fairness. Notable contributions include scalable algorithms for big data analytics and predictive modeling, as well as computational infrastructure for interdisciplinary science. Awards: Fellow, AAAS (2018) ACM Distinguished Member NSF Director’s Award for Superior Accomplishment (2013) Edward Frymoyer Endowed Chair (2013) Leadership: Honavar co-founded the Penn State Center for Artificial Intelligence and led the NIH-funded Biomedical Data Sciences Ph.D. program. He is a Co-PI of the North East Big Data Innovation Hub and serves on editorial boards of journals like IEEE/ACM Transactions on Computational Biology and Bioinformatics. Lab & Teams: Directs the Artificial Intelligence Research Laboratory and the Center for Big Data Analytics and Discovery Informatics, fostering collaborations across computer science, life sciences, and health sciences.
