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Romain LopezView profile

Assistant Professor

• Single-cell omics
• Spatial transcriptomics
• Deep generative models
• +5 more

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.

• Fibrosis Research
• Liver Fibrosis
• Kidney Fibrosis
• +9 more

Karen Piper Hanley is Professor of Molecular Medicine and Head of Division in the Division of Diabetes, Endocrinology & Gastroenterology at the University of Manchester. She serves as the fibrosis lead in the Wellcome Trust Centre for Cell-Matrix Research and previously served as Director of Core Research Technologies (2019-2022). She is also an editorial board member for Scientific Reports. Her research focuses on fibrosis (scarring) mechanisms across multiple organs, particularly liver, kidney, and lung fibrosis. Her lab investigates the cellular and molecular basis of fibrotic disease, with emphasis on how pathological scar components are produced and how they signal to surrounding cells. Key areas include the role of SOX9 transcription factor, integrin beta-1 signaling, Group 1 PAKs, and YAP1 in fibrosis progression. Her recent publications reveal trends toward increased clinical translation of fibrosis research, with studies on health inequalities in liver cancer care, post-COVID diabetes incidence, and innovative approaches to liver disease detection. Her work increasingly integrates spatial transcriptomics and advanced omics technologies to understand cellular transitions in liver disease. Career-track postdoctoral fellowship from the University of Southampton (2006) As senior postgraduate tutor, she has mentored numerous PhD students who have presented at key conferences and authored high-quality manuscripts. All her PhD students have submitted and been awarded their degrees within 4 years. She has successfully guided previous Academic Clinical Fellows to win MRC clinical training fellowships. Her teaching extends to undergraduate Personal & Professional Development and supervision of MRes and BSc students, many of whom have achieved first-class degrees and prestigious awards. Her lab maintains active collaborations with Professor Neil Hanley's group on human development, stem cells, and regenerative medicine, bridging developmental biology with fibrosis research to understand why regeneration is impaired in chronic diseases.

• Immunology
• Genomics
• Multiomics Analysis
• +3 more

Calliope Dendrou is an Associate Professor in Clinical Pathology and Inflammation at the Kennedy Institute of Rheumatology (KIR), University of Oxford, leading the Immune Disease Multiomics Laboratory. She previously held a Wellcome & Royal Society Sir Henry Dale Fellowship at the University of Oxford’s Centre for Human Genetics before joining KIR in 2023. Her research focuses on immune disease mechanisms using multiomics approaches, including genomic profiling to identify therapeutic targets across tissues and immune-mediated diseases. She co-leads large-scale projects like the Oxford-J&J Cartography Consortium and the Chan Zuckerberg Initiative’s LEGACY Network, and teaches on the MSc in Genomic Medicine program. Educational Background: BSc (Biology, Imperial College London, 2005; Forbes Memorial Medal Winner); PhD in Infection & Immunity (University of Cambridge, 2010). Postdoctoral training at the Weatherall Institute of Molecular Medicine under Prof. Lars Fugger. Research interests include immunogenetics, cytokine signaling pathways, drug repositioning, and cross-disease pathophysiology. Her work integrates single-cell and spatial transcriptomics to dissect immune-cell interactions in diseases like rheumatoid arthritis, inflammatory bowel disease, and celiac disease. Recent articles highlight her contributions to understanding vaccine adjuvant responses, Th17 cell roles in spondyloarthritis, and immune-epithelial networks in celiac disease. Collaborations emphasize multi-omic data analysis (e.g., Panpipes pipeline) and translational studies toward precision medicine. Awards: Forbes Memorial Medal (BSc), Wellcome & Royal Society Sir Henry Dale Fellowship. Leadership roles include Equality, Diversity, and Inclusion Champion and 'Single-Cell & Spatial Omics for Precision Medicine' Module Lead. Lab & Teams: Immune Disease Multiomics Lab at KIR. Active in collaborative initiatives such as the LEGACY Network, focusing on large-scale immune profiling in ancestrally diverse populations.

Matthias MannView profile

Professor

• Mass Spectrometry
• Proteomics
• Systems Biology
• +6 more

Professor Matthias Mann is a world-leading scientist serving as Director of the Proteomics and Signal Transduction department at the Max Planck Institute of Biochemistry in Martinsried, Germany, and Director of the Proteomics department at the Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Denmark. With an h-index exceeding 277 and over 350,000 citations, he is recognized as the highest cited German researcher and one of the most influential scientists globally in proteomics. His educational background includes: Ph.D. in Chemical Engineering from Yale University (1988) Master's Degree in Physics from Georg August University Göttingen (1984) Bachelor's of Arts in Mathematics from Georg August University Göttingen (1982) Professor Mann's research focuses on advancing mass spectrometry-based proteomics to understand biological systems at the protein level. His work spans technological developments in mass spectrometry, bioinformatics and computational analysis, signal transduction and posttranslational modifications, and clinical proteomics applications for disease diagnosis and treatment. The Mann lab has pioneered groundbreaking methods like SILAC for quantitative proteomics and MaxQuant for proteome data analysis. Their vision is to translate proteomics knowledge into clinical practice for predictive, diagnostic, and preventive medicine, with recent work focusing on AI-guided platforms for analyzing proteomes from minimal tissue samples. Analysis of Professor Mann's recent publications reveals a strong trend toward clinical applications of proteomics, particularly in cancer research, metabolic diseases, and neurodegenerative disorders. His work increasingly integrates spatial proteomics, single-cell resolution techniques, and artificial intelligence approaches to uncover disease mechanisms and identify potential biomarkers, with a clear shift from basic technology development toward direct clinical applications and personalized medicine. Professor Mann has received numerous prestigious awards throughout his career: 2025: Elected member of the American National Academy of Sciences 2024: Dr. H.P. Heineken Award for Biochemistry and Biophysics 2023: Otto Warburg Medal 2019: Nominated member of the Bavarian Academy of Sciences 2013: Elected member of Leopoldina German National Academy of Sciences 2012: Körber European Science Award, Louis-Jeantet Foundation Prize for Medicine, Ernst Schering Prize, and Leibniz Prize Professor Mann leads a highly collaborative research team involved in multiple international networks including the Bill & Melinda Gates Foundation, Michael J. Fox Foundation for Parkinson's Research, CLINSPECT-M, and Munich Heart Alliance. His lab has mentored numerous successful researchers, with several former postdocs receiving prestigious ERC Starting Grants. The Mann group has developed innovative clinical proteomics pipelines for analyzing archived tissue specimens and body fluids, aiming to identify protein markers for early detection of diseases such as diabetes and cancer. The Mann lab operates across two major research centers with state-of-the-art mass spectrometry facilities. Their Clinical Knowledge Graph platform integrates multi-omics data with extensive metadata, creating an ecosystem for machine learning applications in proteomics. Current research focuses on developing highly sensitive methods that can profile thousands of proteins from minimal cell samples, enabling the identification of critical disease-related proteins and supporting the development of individualized therapies.

Daiwei ZhangView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Spatial Omics
• +3 more

Daiwei (David) Zhang, PhD, is an Assistant Professor (tenure-track) in the Department of Biostatistics at the University of North Carolina at Chapel Hill School of Medicine, with a joint appointment in the Department of Genetics. His research focuses on developing AI frameworks for analyzing high-dimensional biomedical data, particularly in spatial omics, computational pathology, and medical imaging. Education: MS (Biostatistics) and PhD (Biostatistics and Scientific Computing) from the University of Michigan. Postdoctoral Training: University of Pennsylvania. Research interests include applying machine learning to address biomedical challenges such as tumor heterogeneity, immune interactions, and tissue architecture. His work spans computational methods for spatial transcriptomics, proteomics, and histology integration. Recent publications emphasize spatial multi-omics analysis of cancer ecosystems, tertiary lymphoid structures, and metabolic coordination. These studies leverage advanced machine learning algorithms and interdisciplinary approaches to advance precision medicine. No scientific awards are explicitly mentioned, but his work reflects significant contributions to biomedical AI research. Grants and advising details are not provided in the text.

Rex YingView profile

Assistant Professor

• Graph Neural Networks
• Geometric Representation Learning
• Explainable AI
• +3 more

Rex Ying is an Assistant Professor in the Department of Computer Science at Yale University's School of Engineering & Applied Science. He leads research in graph neural networks, geometric representation learning, and explainable AI, with applications spanning physical simulations, biology, knowledge graphs, and recommender systems. His lab actively recruits PhD students interested in geometric deep learning, graph neural networks, and trustworthy AI. Dr. Ying received his PhD in Computer Science from Stanford University under Jure Leskovec, with a thesis titled "Towards Expressive and Scalable Deep Representation Learning for Graphs." Prior to that, he graduated from Duke University in 2016 with highest distinction, majoring in Computer Science and Mathematics. His research focuses on three interconnected areas: advancing graph neural network architectures for improved expressiveness, scalability, and interpretability; innovating in geometric representation learning for data with diverse characteristics; and developing real-world applications across scientific domains. He has pioneered influential algorithms including GraphSAGE, PinSAGE, and GNNExplainer, and developed the first billion-scale graph embedding services at Pinterest as well as graph-based anomaly detection algorithms at Amazon. His recent publication trends show a strong focus on hyperbolic geometry for foundation models, non-Euclidean representation learning, and multimodal applications in computational biology. The research demonstrates increasing integration of geometric deep learning with large language models and foundation model architectures. KDD 2022 Dissertation Award 2019 Baidu Scholarship in Artificial Intelligence Dr. Ying actively serves the research community as a committee member for major conferences including AAAI, ICML, NeurIPS, ICLR, KDD, and WebConf for over seven years, and as area chair for LoG 2022. He co-leads the open-source PyTorch Geometric project and has organized numerous workshops on graph learning. His industry collaborations include Pinterest, Amazon, Facebook AI Research, DeepMind, Siemens, SLAC National Accelerator Laboratory, and Saudi Aramco. He teaches "Deep Learning for Graph-Structured Data" at Yale and mentors students in developing cutting-edge graph learning algorithms. His research lab collaborates with both academic institutions and industry partners to advance the state-of-the-art in graph representation learning, with particular emphasis on geometric deep learning and its applications to scientific discovery and real-world systems.

Long CaiView profile

Professor

• Spatial Genomics
• Cellular Biology
• Developmental Biology
• +4 more

Long Cai is a Professor at the California Institute of Technology, affiliated with the Biology and Biological Engineering department. He pioneered the field of spatial genomics and co-developed transformative technologies such as seqFISH and MEMOIR. Research Interests: His work focuses on decoding biological systems through spatial genomics, integrating molecular imaging with computational analysis to uncover cellular organization in tissues. Key areas include developmental biology, neuroscience, kidney regeneration, and cancer biology. Publications: Recent studies highlight applications of spatial transcriptomics in kidney disease, brain nuclear architecture, and multi-omics tissue mapping. His research emphasizes creating high-resolution atlases of cellular dynamics. Scientific Awards: NIH Director’s Pioneer Award (2022) Labs & Collaborations: He leads the Cai Lab, which develops cutting-edge imaging tools in collaboration with the Elowitz Lab and other interdisciplinary teams.

Xiuwei ZhangView profile

Assistant Professor

• Computational Biology
• Bioinformatics
• Single-Cell Data Analysis
• +5 more

Xiuwei Zhang is the J.Z. Liang Early-Career Assistant Professor in the School of Computational Science and Engineering (SCoSE) at Georgia Institute of Technology, part of the College of Computing. Her research focuses on computational biology and bioinformatics, particularly in developing machine learning methods for analyzing single-cell omics data, including multi-modal, temporal, and spatial data integration. She leads a lab that designs tools like scDART , scMoMaT , and scMultiSim , which address challenges in multi-omics integration, lineage reconstruction, and simulation. Before joining Georgia Tech, she held postdoctoral positions at UC Berkeley (Nir Yosef’s group), the European Bioinformatics Institute (EBI), and École Polytechnique Fédérale de Lausanne (EPFL). She earned her PhD in computer science from EPFL under Bernard Moret. Her Erdős number is 3, reflecting her collaborative work across computational fields. Her research spans four key areas: multi-batch/single-cell data integration, temporal analysis of cell differentiation, spatial-temporal omics dynamics, and simulation tools for benchmarking methods. She has received prestigious awards, including the NSF CAREER Award (2022) and NIH MIRA (2021). She actively participates in conferences (RECOMB, ISMB) and serves on editorial boards (Journal of Computational Biology). Her group’s recent work includes the scMultiSim simulator (2025), which generates multi-omics spatial data, and LinRace (2023), reconstructing cell lineage histories. She mentors over 15 students and collaborates internationally on projects like the InQuBATE Workshop on Single-Cell Transcriptomics.

Yolanda GilView profile

Research Professor

• Knowledge capture
• Semantic Workflows
• Ontology Tools
• +7 more

Dr. Yolanda Gil is a Research Professor in Computer Science and Spatial Sciences at the University of Southern California, where she serves as Principal Scientist and Senior Director for Strategic Initiatives in Artificial Intelligence and Data Science at the Information Sciences Institute (ISI). She is also the Director of AI and Data Science Initiatives in the Viterbi School of Engineering and leads the USC Center for Knowledge-Guided Interdisciplinary Data Science (CKIDS). Dr. Gil received her Licenciatura in Computer Science from the Polytechnic University of Madrid and her M.S. and Ph.D. in Computer Science from Carnegie Mellon University, with a focus on artificial intelligence and cognitive science. Her research focuses on developing AI approaches that use knowledge to accelerate scientific discovery processes. Her key research interests include knowledge capture and representation, semantic workflows, ontology tools, scientific discovery methods, task-based collaboration, provenance tracking, knowledge networks, reproducibility in science, and machine learning for data analysis. She collaborates with scientists across multiple domains to improve how scientific knowledge is created, shared, and used. Dr. Gil's work has significant impact across multiple scientific domains including climate science, neuroscience, and omics research. Her projects demonstrate her commitment to building knowledge-guided systems that transform how scientists conduct research. She has pioneered approaches to capture the provenance of scientific experiments and to automate the analysis of complex scientific data. Fellow of the Association for Computing Machinery (ACM) Fellow of the Association for the Advancement of Science (AAAS) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) 24th President of the Association for the Advancement of Artificial Intelligence Co-chair of the CRA/AAAI 20-Year Artificial Intelligence Research Roadmap for the US Initiator and leader of the W3C Provenance Group that resulted in a widely-used standard for web trust As an educator and leader, Dr. Gil directs the Data Science Program in Computer Science and serves as Co-Director of multiple joint MSc programs including Communication Data Science, Spatial Data Science, Environmental Data Science, Public Policy Data Science, and Healthcare Data Science. She also leads the new dual degree USC-Tsinghua University on Communication Data Science. Her leadership extends to mentoring numerous students and researchers in AI and data science. Through the USC Center for Knowledge-Guided Interdisciplinary Data Science (CKIDS), Dr. Gil organizes DataFest events each semester, fostering collaboration and innovation in data science across disciplines.

Omer BayraktarView profile

Researcher

• Neural Diversity
• Spatial Transcriptomics
• Glia Biology
• +3 more

Omer Bayraktar is a Group Leader at the Wellcome Sanger Institute , leading research in the Cellular Genomics Programme. His work focuses on decoding human brain cellular diversity using spatial transcriptomics , imaging , and functional screening to study neural complexity in health and disease. Bayraktar's educational background includes a PhD from HHMI under Chris Doe, investigating neural diversity development in Drosophila , followed by postdoctoral work at University of California, San Francisco and University of Cambridge as a Life Sciences Research Foundation Fellow. He developed a spatial transcriptomic pipeline during his postdoc to analyze astrocyte heterogeneity in the cerebral cortex. His research explores neural cell type mapping , glial-neuronal interactions , and cellular pathways in neurodevelopmental disorders . Recent publications emphasize 3D tissue mapping , multi-omic integration , and computational tools like Cell2fate and WebAtlas. His work bridges neurogenetics and computational biology to advance understanding of human tissue ecosystems. Bayraktar's lab collaborates with the Human Cell Atlas initiative and develops technologies such as automated histology pipelines and highly-multiplexed smFISH for molecular cell typing. His team also investigates glia-based therapies and astrocyte functional heterogeneity in neurodevelopmental contexts. Key scientific contributions include: Discovering astrocyte layer patterns independent of neuronal laminae Developing cell2location for spatial cell mapping Characterizing Drosophila neural stem cell models with human relevance Notable awards include the Life Sciences Research Foundation Fellowship during his postdoctoral training. His current group includes a PhD student , Senior Data Scientists , and Bioinformaticians .

• Computational Biology
• Spatial Omics
• Tumor Immunology
• +3 more

Madeleine Torcasso is an Assistant Professor in the Department of Medicine-Hematology and Oncology at the University of Chicago. She leads the Torcasso Lab, which investigates spatial patterns of disease within native tissue environments, bridging computational methods with clinical applications. Her research focuses on tumor-immune interactions using high-dimensional spatial proteomic and transcriptomic data to uncover mechanisms of disease progression and therapeutic resistance. Her lab integrates artificial intelligence with spatial omics to identify biomarkers and decode cellular communication in the tumor microenvironment. Key research interests include computational pathology, spatial biology, and translational oncology, with an emphasis on developing analytical tools for complex tissue data. Her sole recent publication (2024) introduces a computational method for classifying cells in multiplexed immunofluorescence images, advancing automated analysis of tissue microenvironments. No awards, students, or grants are detailed in available sources.

• Cancer Biology
• Mathematical and Computational Modeling
• Bioinformatics
• +7 more

Dana Pe'er is a Professor and Chair of the Computational and Systems Biology Program at the Sloan Kettering Institute (SKI) of Memorial Sloan Kettering Cancer Center. She is also an Investigator of the Howard Hughes Medical Institute and holds the Alan and Sandra Gerry Endowed Chair. Dr. Pe'er leads an interdisciplinary research group that combines advanced genomics approaches with machine learning to address fundamental questions in biomedical science, with particular focus on cancer biology, developmental biology, and immunology. Dr. Pe'er earned her PhD from Hebrew University in Jerusalem, Israel. Her academic journey includes a postdoctoral fellowship with George Church at Harvard Medical School. Before joining Memorial Sloan Kettering Cancer Center in 2016, she held faculty positions at Columbia University. Dr. Pe'er's research focuses on understanding cellular plasticity, the consequences of intra-tumor heterogeneity, cancer evolution and metastasis, and the mechanisms by which regulatory circuits go awry in disease. Her lab combines single-cell and spatial profiling technologies with machine learning approaches to investigate gene regulation, cellular plasticity, and cell-cell communication in the contexts of cancer, immunity, and development. They are particularly interested in how organisms develop from a single cell to generate diverse cell types, how epigenetic control rewires during development, and how cells communicate to execute multicellular responses. Analysis of Dr. Pe'er's recent publications reveals a strong focus on developing computational methods for single-cell and spatial genomics data analysis. Her work spans cancer types including pancreatic, prostate, colorectal, and breast cancer, with emphasis on tumor heterogeneity, metastasis mechanisms, and cellular plasticity. A significant portion of her research involves creating novel algorithms and tools like CellRank, REUNION, and SEACells that enable researchers to extract meaningful biological insights from complex genomic datasets. 2023 Class of 2023 Inductee - American Academy of Cancer Research (AACR) Academy 2023 Innovator Award - International Society for Computational Biology (ISCB) 2021 Fellow - International Society for Computational Biology (ISCB) Howard Hughes Medical Institute Investigator (2021) 2019 Ernst W. Bertner Memorial Award - University of Texas MD Anderson Cancer Center 2016 Lenfest Distinguished Faculty Award - Columbia University 2014 Director's Pioneer Award - National Institutes of Health 2014 Overton Prize - International Society for Computational Biology (ISCB) Dr. Pe'er is known for her dedicated mentorship approach, describing herself as "a mama bear" who cares deeply about her trainees while expecting independence, innovation, and hard work. She mentors numerous PhD students and postdocs in her lab. Her HHMI Investigator award provides approximately $9 million over seven years, enabling ambitious research directions. She also collaborates extensively with the Single-cell Analytics and Innovation Lab (SAIL) at MSK to generate new data from emerging technologies, working closely with wet-lab collaborators at MSK and beyond to apply computational methods to cutting-edge datasets across multiple disease areas. The Pe'er Lab is an interdisciplinary group of computational biologists with diverse backgrounds ranging from pure mathematics to clinical medicine. They work closely with wet-lab collaborators to apply their computational methods to cutting-edge datasets across cancer, immunology, and developmental biology. The lab is described as open, supportive, collaborative, and fun, with access to world-class facilities at the Sloan Kettering Institute. Dr. Pe'er's work continues to push the boundaries of computational biology and cancer research, with the ultimate goal of developing more effective, personalized therapies for cancer patients.

Rong FanView profile

Professor

• Single-cell Omics
• Spatial Omics
• Immunology
• +5 more

Dr. Rong Fan is the Harold Hodgkinson Professor of Biomedical Engineering and Professor of Pathology at Yale University. His research focuses on developing and applying single-cell and spatial omics technologies to study immune systems, cancer, and aging. His lab has pioneered technologies like the IsoCode microchip for high-throughput protein profiling, and spatial multi-omics platforms (e.g., DBiT-seq, spatial-ATAC-seq) to analyze tissue complexity at cellular resolution. He co-founded IsoPlexis, Singleron Biotechnologies, and AtlasXomics to commercialize these innovations. Education: PhD in Chemistry from UC Berkeley (2006), B.S. in Applied Chemistry from University of Science and Technology of China (1999). Postdoctoral training at Caltech before joining Yale in 2010. Research interests include CAR-T cell therapy optimization, spatial epigenomics, and multi-omics integration. Key achievements include discovering biomarkers predictive of CAR-T efficacy and defining spatial genomic landscapes in cancer and neuroinflammation. Awards: NSF CAREER Award, Packard Fellowship, election to AIMBE, CASE, and NAI. Serves on advisory boards for Bio-Techne and Yale Ventures. Active in training future scientists via the Yale Biomedical Engineering and Yale School of Medicine programs.

Vitaly KheyfetsView profile

Associate Professor

• Pulmonary Arterial Hypertension
• Critical Care Medicine
• Pediatric Cardiology
• +4 more

Vitaly Kheyfets, PhD, serves as Associate Professor in the Department of Pediatrics-Critical Care Medicine at the University of Colorado Anschutz Medical Campus School of Medicine, where he directs research at the intersection of pediatric critical care and cardiopulmonary pathophysiology with emphasis on pulmonary arterial hypertension (PAH). His primary research focuses on right ventricular adaptation to pulmonary hypertension, utilizing machine learning-driven multi-omics analysis to identify disease biomarkers and molecular networks. He pioneers computational fluid dynamics approaches for hemodynamic modeling in congenital heart conditions like Glenn physiology, while also investigating sleep oscillatory patterns as neurodegenerative biomarkers. His methodology integrates proteomics, spatial transcriptomics, and pressure waveform analysis to dissect vascular remodeling mechanisms. Publication trends reveal a strong emphasis on translating computational models into clinical applications for PAH prognostication, with recent work developing AI-cooperative diagnostic platforms and characterizing microvascular changes in the right ventricle. Cross-disciplinary collaborations span proteomics, imaging, and sleep neuroscience, demonstrating consistent innovation in both pulmonary hypertension and neurodegenerative disease biomarker discovery.

James ZouView profile

Associate Professor

• Machine Learning
• Artificial Intelligence in Healthcare
• Biomedical Data Science
• +3 more

James Zou is an Associate Professor of Biomedical Data Science at Stanford University, with courtesy appointments in Computer Science and Electrical Engineering. His research focuses on advancing machine learning methodologies for healthcare applications, emphasizing reliability, fairness, and statistical rigor. He holds a Ph.D. from Harvard University and has held positions at Microsoft Research, Cambridge University (as a Gates Scholar), and UC Berkeley (Simons Fellow). Zou leads the Stanford Data4Health hub and is a Chan-Zuckerberg Investigator. His work spans AI-driven diagnostics, spatial transcriptomics, and ethical AI frameworks. Key achievements include the EchoNet AI system for echocardiography and foundational contributions to data valuation (e.g., Data Shapley). Awards include the Sloan Fellowship, NSF CAREER Award, and Google/Tencent AI awards. Education: Ph.D., Harvard University (2014); Postdoctoral roles at Microsoft Research, Cambridge, and Berkeley. Research Interests: Machine learning for healthcare, algorithmic fairness, interpretable AI, spatial omics, and translational bioinformatics. His lab develops tools like TextGrad (PyTorch for text agents) and frameworks for evaluating medical AI systems. Recent work addresses LLMs in peer review and clinical decision-making. Grants/Grants: Supported by NSF, Sloan Foundation, Chan-Zuckerberg Initiative, and industry partnerships (Google, Amazon, Adobe). Advises on over 20 doctoral students, many contributing to high-impact papers in Nature , Science , and top conferences (NeurIPS, ICML). Leads collaborations in cardiology, oncology, and veterinary medicine. Labs/Teams: Stanford AI Lab, Stanford Data4Health, and interdisciplinary groups in precision medicine. Active in open-source projects like FrugalML and MetaViz.