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Stefano ErmonView profile

Associate Professor

• Machine Learning
• Generative AI
• Computational Sustainability
• +7 more

Stefano Ermon is an Associate Professor in the Department of Computer Science at Stanford University, affiliated with the Artificial Intelligence Laboratory and a Senior Fellow at the Woods Institute for the Environment. His research focuses on advancing machine learning and generative AI techniques to address societal and environmental challenges, including computational sustainability, geospatial analysis, and climate science. He holds a Ph.D. from Cornell University (2015). Education: Ph.D. in Computer Science, Cornell University (2015). Research Interests: Ermon’s work bridges foundational machine learning (e.g., diffusion models, generative AI, and optimization) with applications in sustainability, geospatial analysis (via satellite imagery), and earth observation systems. Notable contributions include predicting poverty using satellite data and developing scalable methods for molecule generation. Articles Trends: His recent work emphasizes diffusion models for generative tasks (e.g., text-to-image, molecule design), geospatial AI (e.g., environmental monitoring), and ethical AI (e.g., bias mitigation in LLMs). He also explores applications in robotics and scientific computing. Awards: He has received prestigious awards, including the ICML 2024 Best Paper Award, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, and the IJCAI Computers and Thought Award. Advising and Grants: Ermon teaches courses like Probabilistic Graphical Models (CS228) and has secured grants from NSF, ONR, AFOSR, and private foundations. His lab develops tools for climate science and sustainable development. Labs/Teams: Leads the Stanford AI Lab group focused on computational sustainability and generative AI, collaborating with institutions like the Woods Institute for environmental applications.

• Epigenetics
• Retroelements
• Transposable elements
• +5 more

Didier Trono is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Laboratory of Virology and Genetics (LVG) within the School of Life Sciences. Formerly, from 2004 to 2012, he served as the founding dean of EPFL's Faculty of Life Sciences, orchestrating its development and growth during this formative period. Trono received his medical education at the University of Geneva, followed by clinical training in pathology, internal medicine, and infectious diseases in Geneva and at Massachusetts General Hospital in Boston. His scientific career began at the Whitehead Institute of MIT, and in 1990 he was recruited by the Salk Institute of San Diego to launch an AIDS research center. After seven years in the United States, he returned to Europe and eventually joined EPFL. Dr. Trono's research has evolved significantly over his career. Initially focusing on virus-host interactions, he studied pathogens like HIV and Hepatitis B virus, creating HIV-derived genetic transfer tools that are now successfully used in gene therapy. For approximately the last fifteen years, his research has centered on epigenetics, particularly exploring the impact of retroelements and their control mechanisms on development and physiology of higher organisms, including humans. His laboratory investigates how transposable elements and KRAB zinc finger proteins regulate gene expression, with important implications for understanding cancer biology and developing new diagnostic and therapeutic approaches. Analysis of his recent publications (2022-2024) reveals a strong focus on transposable elements, KRAB zinc finger proteins, and their roles in gene regulation and cancer. His work combines molecular biology, genomics, and bioinformatics approaches to understand how these ancient viral remnants have been co-opted by the host genome to regulate development and cellular functions. The research spans basic molecular mechanisms to potential clinical applications in cancer diagnosis and therapy, with some recent work also addressing SARS-CoV-2 and immune responses. Throughout his career, Professor Trono has mentored numerous PhD students, including Bojkowska Karolina, Brandão Sanches Vong Martins Filipe Amândio, Bulliard Yannick, Coluccio Andrea, Corsinotti Andrea, Coudray Alexandre, De Tribolet-Hardy Jonas Caspar, and Dorschel Iris Arianna. His laboratory has received significant funding to support research at the intersection of virology, genetics, and epigenetics, contributing to EPFL's reputation as a leading institution in life sciences research. The Trono Laboratory continues to be at the forefront of research on retroelements and their regulatory mechanisms, maintaining a vibrant research environment that bridges fundamental biological questions with potential medical applications, particularly in cancer research and precision medicine.

Pascal FrossardView profile

Professor

• Machine Learning
• Data Science
• Graph Signal Processing
• +6 more

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.

Tommi JaakkolaView profile

Professor

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +7 more

Tommi Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. He received his MSc in theoretical physics from Helsinki University of Technology in 1992 and his PhD from MIT in computational neuroscience in 1997. After completing a postdoctoral position in computational molecular biology as a DOE/Sloan fellow at UCSC, he joined the MIT EECS faculty in 1998. His research advances how machines can learn, predict or control, and do so at scale in an efficient, principled, and interpretable manner. His work in machine learning extends from foundational theory to modern applications, focusing especially on statistical inference and estimation tasks that lie at the heart of complex learning problems. He designs new methods, theory and algorithms to automate the use and generation of semi-structured data such as natural language text, images, molecules, or strategies. Jaakkola applies and develops algorithms to solve multi-faceted recommender, retrieval, or inferential tasks (particularly in biomedical contexts), design and optimize molecules or reactions for drug design, and model strategic, game theoretic interactions. His recent work heavily focuses on diffusion models, protein structure prediction, molecular design, and generative AI, with significant publications in top conferences including ICML, NeurIPS, and ICLR. His scientific contributions span multiple disciplines with significant impact in both theoretical machine learning and practical applications in computational biology and chemistry, including notable work on antibiotic discovery published in Cell. Current advisees: Julia Balla, Bowen Jing, Hannes Stärk, Peter Holderrieth, Chenyu Wang Recent graduates: Gabriele Corso (Boltz PBC), Ezra Erives (DE Shaw), Jason Yim (Xaira) Jaakkola maintains an active research program through MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Data, Systems, and Society (IDSS), with his office located in the Stata Center (32-G470). His work bridges theoretical machine learning with practical applications, making significant contributions to both the academic field and potential real-world impact in healthcare and drug discovery.

Peter BühlmannView profile

Professor

• High-dimensional Statistics
• Causal Inference
• Machine Learning
• +3 more

Peter Bühlmann is a Professor at ETH Zürich within the Seminar für Statistik , focusing on high-dimensional statistics, causal inference, and machine learning. His work bridges theoretical advancements with practical software implementations in R packages like pcalg , mboost , and glmmlasso , impacting fields such as genomics, proteomics, and intensive care analytics. Key Contributions : Causal structure learning, stability selection, anchor regression, and deconfounding. Software : Developed widely used R packages for statistical modeling and causal inference. Teaching : Courses on high-dimensional statistics at ETH Zürich and international institutions. Research Trends : Recent articles emphasize causal robustness, domain adaptation, and applications in medicine. His work addresses challenges in heterogeneous data, missing values, and covariate shifts using methods like spectral deconfounding and residual prediction tests. Scientific Recognition : Co-author of a paper designated as a New Hot Paper (Meinshausen and Bühlmann, 2006) by Essential Science Indicators, indicating significant impact in high-dimensional multiple testing.

• Machine Learning
• Explainable AI
• Neural Networks
• +3 more

Jonas Fischer is the head of the Explainable Machine Learning group at the Max Planck Institute for Informatics, Department of Computer Vision and Machine Learning. His research focuses on interpreting complex machine learning models, particularly in genomics and healthcare, aiming to enhance robustness and alignment with human decision-making. Prior to his role at MPI, he was a postdoctoral fellow at Harvard University's Department of Biostatistics, where he worked on interpretable models for gene regulatory systems in cancer. Education: PhD in Computer Science from Saarland University (2022), with a thesis titled More than the sum of its parts , exploring the intersection of pattern mining and deep learning. He has contributed to advancing methods in neural network pruning, federated learning, and low-dimensional embeddings (e.g., dtSNE, Mercat). His work bridges computational biology, data mining, and machine learning, with applications in DNA methylation analysis, graph-based differential networks, and biomedical informatics. Key research areas include: (1) Explainable AI and neural network interpretability, (2) Biomedical applications of machine learning (e.g., gene regulatory networks, cancer genomics), (3) Low-dimensional embeddings and visualization techniques, (4) Federated learning for privacy-preserving collaborative models, and (5) Pattern mining for error analysis in NLP and classification tasks. Publications span top venues like NeurIPS, ICLR, Bioinformatics, and Genome Biology. His group develops tools such as BONOBO for omics data integration and node2vec2rank for scalable graph analysis. He actively collaborates with biomedical researchers to address challenges in data-driven healthcare and precision medicine.

Han LiuView profile

Professor

• Artificial Intelligence
• Computer Systems
• Statistical Machine Learning
• +4 more

Han Liu is a Professor in the Department of Computer Science at Northwestern University's McCormick School of Engineering. He directs the MAGICS (Modern Artificial General Intelligible and Computer Systems) Lab and the Center for Foundation Models and Generative AI at Northwestern, with prior roles as director of the Deep Reinforcement Learning Center at Tencent AI Lab and professor at Princeton and Johns Hopkins Universities. PhD in Machine Learning and Statistics from Carnegie Mellon University (2012), advised by John Lafferty and Larry Wasserman Han Liu's research focuses on integrating artificial intelligence with computer systems, particularly through foundation models and probabilistic graphical models. His work aims to revolutionize science, engineering, and business by deploying statistical machine learning methods in edge and cloud computing environments. Recent research trends include transformer-based models, modern Hopfield networks, genomic foundation models, and theoretical analysis of attention mechanisms. His 2025 publications explore topics like species differentiation with DNA embeddings, universal approximation capabilities of transformers, and metaverse spatial reasoning. Alfred P Sloan Fellowship in Mathematics IMS Tweedie New Researcher Award ASA Noether Young Scholar Award NSF CAREER Award Presidential Early Career Awards for Scientists and Engineers Han Liu serves as associate editor for the Journal of American Statistical Association, Electronic Journal of Statistics, Technometrics, and the Journal of Portfolio Management. He has directed research centers at Northwestern and contributed to major conferences as area chair (NeurIPS, ICML, ICLR).

• Bayesian statistics
• Stochastic simulation
• Markov chain Monte Carlo
• +5 more

Professor Chris Holmes is a Professor of Biostatistics at the University of Oxford, where he moved from Imperial College London in February 2004. He is a Fellow at St Anne's College and works in the Department of Statistics. His research focuses on applications and statistical methods development in genomic sciences and genetic epidemiology, holding a prestigious Programme Leaders Grant in Statistical Genomics from the Medical Research Council. Prior to his position at Oxford, Professor Holmes completed his doctorate in Bayesian statistics at Imperial College London, investigating novel nonlinear pattern recognition methods. This was followed by a post-doctoral position and then a lectureship at Imperial. Before his academic career, he worked in industry for several years in scientific computing, developing techniques for real-time pattern recognition models in defense and SCADA systems. Professor Holmes has a broad interest in the theory, methods and applications of statistics and statistical modeling, with a particular foundation in Bayesian statistics which he views as providing a unified framework for stochastic modeling and information processing. His specific research interests include: Bayesian statistics and stochastic simulation Markov chain Monte Carlo methods Pattern recognition and nonlinear, nonparametric methods Spatial statistics Statistical genetics and genomics Genetic epidemiology His recent publications (2023-2025) demonstrate a strong focus on the intersection of biostatistics, artificial intelligence, and healthcare applications. His work spans multiple domains including AI-driven disease classification in neurology, genomic data analysis for health equity, machine learning tools for healthcare prediction, and addressing bias in medical AI systems. A notable trend across his research is the application of advanced statistical methods to solve pressing problems in genomics, epidemiology, and medical diagnostics, with an increasing emphasis on health equity and the ethical implications of AI in healthcare. Professor Holmes currently supervises PhD students Oscar Clivio, Sahra Ghalebikesabi, and Natalia Garcia Martin. His research is supported by multiple grants, including the MRC Programme Leaders Grant in Statistical Genomics which funds his work in statistical genomics. He is actively involved in three research groups at Oxford that reflect the breadth of his scholarly interests: Computational Statistics and Machine Learning Statistical Genetics and Epidemiology Statistical Theory and Methodology

Florentina BuneaView profile

Professor

• Statistical machine learning theory
• High-dimensional statistical inference
• Optimal transport
• +6 more

Florentina Bunea is a Professor in the Department of Statistics and Data Science at Cornell University’s Bowers College of Computing and Information Science, and an active member of the Graduate Fields of Statistics, Applied Mathematics, and Computer Science. She also serves on the Diversity and Inclusion Council of her college, championing workforce diversity in data-science disciplines. Education & Institutional Roles Professor, Department of Statistics and Data Science, Cornell University Member, Graduate Fields of Statistics, Applied Mathematics, Computer Science Member, Diversity and Inclusion Council, Bowers College of Computing and Information Science Research Interests Professor Bunea’s research lies at the intersection of statistical machine-learning theory and high-dimensional inference. She develops rigorous methodology supported by sharp theoretical guarantees to tackle core problems in modern data science. Recent themes include: Soft-max mixtures for understanding large-language-model/AI algorithms Optimal transport for high-dimensional mixture distributions Wasserstein-distance inference for sparse mixing measures in topic models Latent-space clustering and cluster-based inference in high dimensions Network modeling and hidden-structure inference Applications spanning genetics, systems immunology, neuroscience, sociology, and economics Research Funding & Awards Her work is supported by grants from the National Science Foundation (NSF-DMS). She is a Fellow of the Institute of Mathematical Statistics and a recipient of the IMS Medallion Award. Editorial & Service Contributions Associate Editor: Annals of Statistics, Bernoulli, JASA, JRSS-B, EJS, Annals of Applied Statistics Co-Editor: Chapman & Hall/CRC Statistics and Applied Probability Monograph Series Advising & Collaboration Professor Bunea has mentored numerous doctoral and post-doctoral researchers, including Xin Bing, Shuyu Liu, Seth Strimas-Mackey, and Yang Ning, among others. Collaborative projects extend across Cornell and external institutions, producing widely-used software packages and high-impact publications. Contact Office: 1184 Comstock Hall, Cornell University Email: fb238@cornell.edu Phone: (607) 255-8449

Adam IrwinView profile

Associate Professor

• Infectious Diseases
• Pediatric Medicine
• Antimicrobial Resistance
• +3 more

Associate Professor Adam Irwin is an NHMRC Emerging Leadership Fellow and Associate Professor in Infectious Diseases at the University of Queensland (UQ) and Queensland Children’s Hospital. He holds positions within the UQ Centre for Clinical Research, Faculty of Health, Medicine and Behavioural Sciences. His work focuses on early sepsis recognition, antimicrobial stewardship, and pediatric infectious diseases. Dr. Irwin completed his medical degree at the University of Birmingham and his PhD in 2016 from the University of Liverpool’s Institute of Infection and Global Health. He has led initiatives like the Queensland Paediatric Sepsis Program, recognized with a Global Sepsis Alliance Award and Queensland Health Excellence Awards. His research spans sepsis pathway implementation, antimicrobial resistance trends, and rapid diagnostic tools. Notable grants include NHMRC funding for studies on multi-drug resistant infections and Children’s Hospital Foundation support for genomics and antimicrobial stewardship projects. He supervises PhD candidates in areas like bloodstream infections and molecular diagnostics. Dr. Irwin collaborates across institutions, contributing to projects such as the RAPIDS Study Group and the PAEDS surveillance network. Awards include the NHMRC Emerging Leadership Fellowship and recognition for sepsis program excellence. His work emphasizes translating research into clinical practice, improving outcomes for critically ill children through evidence-based protocols and innovative diagnostics.

• Uncertainty Reasoning and Decision Making in Artificial Intelligence
• Electronic Business
• Systems Analysis and Design
• +7 more

Liping Liu is a Professor in the Department of Management at The University of Akron's College of Business. He holds a Ph.D. in Business from the University of Kansas (1995), Master of Engineering in Systems Engineering (1991), and dual bachelor's degrees in Applied Mathematics (1986) and River Dynamics (1987). Ph.D., University of Kansas MS, Huazhong University of Science and Technology B.E., Wuhan University BS, Huazhong University of Science and Technology His research spans Artificial Intelligence , Electronic Business , Systems Analysis , Data Quality , and Belief Function Theory . He pioneered coarse utility theory and linear belief functions , now taught in top Ph.D. programs across multiple disciplines. Key trends in his publications include Belief Function Applications (2012-2024), Medical Data Systems (2003-2015), and Decision Theory (2004-2014). Recent works focus on Gamma Belief Functions (2024) and computational improvements in linear belief function operations (2019-2016). Scientific contributions recognized via: Microsoft Azure Educator Grant (2014-2016) Inclusion in Who's Who in America (2010-2013) and Who's Who in the World (2011-2013) As an editor and committee member for major conferences (INFORMS, AMCIS, Belief Functions conferences), he bridges academic research with practical systems implementation in e-business and healthcare domains.

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +6 more

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

Alexis BattleView profile

Associate Professor

• Genomics
• Machine Learning
• Computational Biology
• +3 more

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 .

Martin T. WellsView profile

Professor

• Applied Statistics
• Theoretical Statistics
• Bayesian Statistics
• +7 more

Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.

Alexandre Bouchard-CôtéView profile

Professor

• Computational Statistics
• Bayesian Inference
• Monte Carlo Methods
• +4 more

Alexandre Bouchard-Côté is a Professor of Statistics at the University of British Columbia (UBC), affiliated with the Department of Statistics within the Faculty of Science. His research focuses on computational statistics, Bayesian methods, and Monte Carlo techniques, with applications in evolutionary biology, cancer genomics, and computational linguistics. Education : PhD in Computer Science (with Designated Emphasis in Statistics) from UC Berkeley (2010), BSc in Mathematics and Computer Science from McGill University (2005). Affiliations : Director of the Blang probabilistic programming project and leader of the Bouncy Particle Sampler research group. Research Interests : Bouchard-Côté develops scalable Bayesian computational methods, including non-reversible Monte Carlo algorithms like the Bouncy Particle Sampler, and applies these to problems in cancer phylogenetics, evolutionary dynamics, and historical linguistics. His work emphasizes bridging theoretical foundations with practical tools for data science. Publications Trends : Recent work spans distributed sampling frameworks (e.g., Pigeons.jl), variational phylogenetic inference, and cancer clonal evolution modeling. His articles often address algorithmic scalability and interdisciplinary applications in biology and astronomy. Awards : CRM-SSC Prize in Statistics (2024) PIMS-UBC Mathematical Sciences Young Faculty Award (2018) Tweedie New Researcher Award (2016) Advising & Grants : Supervises graduate students (e.g., Son Luu, Nikola Surjanovic) and leads funded projects on distributed MCMC and cancer genomics. Collaborates with institutions like the Simons Foundation and the Canadian Statistical Sciences Institute (CANSSI). Labs/Teams : Core member of the UBC Statistical Machine Learning group, contributing to open-source tools like Blang and the Bouncy Particle Sampler implementation.