University of North Carolina at Chapel HillUnited States
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.
Owen R. White is a Professor in the Department of Epidemiology & Public Health at the University of Maryland School of Medicine, serving as Associate Director of the Institute for Genome Sciences and Associate Director of Research Collaboration & Development. He leads a team of 25 scientists and engineers developing genomic annotation pipelines and data analysis tools for state-of-the-art research in microbiome and multi-omic studies. His academic background includes: BS in Biotechnology from the University of Massachusetts (1985) PhD in Molecular Biology from New Mexico State University (1992) Postdoctoral Fellowship in Genome Informatics at the Institute for Genomic Research (TIGR) (1994) Dr. White's research spans bioinformatics, genomics, transcriptomics, and metagenomics with emphasis on data management, metadata standards, ontologies, and cloud systems. His work has been foundational for large-scale initiatives like the Human Microbiome Project (HMP) and Integrative Human Microbiome Project (iHMP), generating over 50,000 datasets totaling 10 terabytes of multi-omic data. Analysis of his recent publications reveals a strong trend toward neuroscience multi-omics (BRAIN Initiative), cloud-based data infrastructure, and ethical data sharing frameworks. His work consistently bridges microbiome research with emerging fields like single-cell analysis and Alzheimer's disease biomarker discovery through integrated data platforms. Notable awards include: Benjamin Franklin Award for Open Access in the Life Sciences (2015) Kumho Science International Award in Plant Molecular Biology and Biotechnology (2001) As Principal Investigator for major NIH-funded centers, he has secured sustained support for the HMP Data Analysis and Coordination Center and iHMP Data Coordination Center. His team's work combines fee-for-service models with collaborative research funding to maintain cutting-edge genomic analysis capabilities. The Institute for Genome Sciences houses his computational team responsible for developing production annotation pipelines, database systems, and visualization tools that serve researchers across the University of Maryland School of Medicine and national consortia.
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.
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.
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.
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.
Karoline Faust is an Associate Professor at KU Leuven, affiliated with the Laboratory of Molecular Bacteriology (Rega Institute) and the Faculty of Medicine . She contributes to the iSi Health and Leuven One Health institutes, and serves on senior academic councils. Her research spans microbial systems biology, focusing on community dynamics and network analysis. Education: PhD in bioinformatics (2010, KU Leuven) Affiliations: KU Leuven, ISME Journal editorial board, Belgian Society for Microbiology Her research investigates microbial community dynamics , systems biology approaches to microbiomes, and bioinformatics tool development . She specializes in modeling human gut microbiota , synthetic microbial communities , and environmental microbiomes (e.g., microplastic impacts on Daphnia microbiomes). Her work integrates metabolic modeling , network analysis , and experimental systems to understand microbial interactions. Recent publications highlight her contributions to microbial network inference , 16S rRNA sequencing protocols , microfluidics , and ecological modeling of microbiomes. She develops tools like manta , miaSim , and CoNet to analyze community structures. Teaching: Karoline co-teaches courses in microbiology, bioinformatics, and network analysis at KU Leuven, and has contributed to international workshops on microbial network inference. Scientific Engagement: She serves as Senior Editor at ISME Journal and Secretary of the Belgian Society for Microbiology .
California Institute of Technology (Caltech)United States
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.
Leibniz Institute for Zoo and Wildlife ResearchGermany
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 .
Kenneth Hoehn is an Assistant Professor in the Department of Biomedical Data Science at the Geisel School of Medicine, Dartmouth College. As a computational immunologist with expertise in evolutionary biology, he develops computational evolutionary approaches to trace cellular lineages, particularly B cells, in contexts such as infection, vaccination, cancer, and autoimmune diseases. His research focuses on understanding adaptive immunity in conditions like COVID-19 Food allergies Myasthenia gravis through collaborations with experimental teams. Key projects include: Phylogenetic modeling of B cell responses Evolutionary signatures in immune repertoires Tracking B cell dissemination in autoimmune diseases Epigenetic regulation of memory B cells Recent publications highlight trends in single-cell immunology , phylogenetic inference , and computational tools for analyzing B cell dynamics. His lab at Dartmouth integrates evolutionary genetics with high-resolution immune profiling.
Xiaoyu Cai is an Assistant Professor at the Department of Medicine, Loyola University Chicago, specializing in lung regeneration, aging biology, and stem cell plasticity. Her research focuses on the molecular mechanisms governing alveolar type 2 (AT2) stem cell dynamics during aging and chronic lung diseases. Education: Bachelor of Medicine (Peking University, 2012), Master of Science (Peking University, 2015), PhD in Biology of Aging (USC & Buck Institute, 2021) Key Research Areas: Lung regeneration, inflammation resolution, stem cell aging, 3D organoid cultures Methodologies: Single-cell multiome, mouse genetics, multicellular organoid systems Collaborations: Translational partnerships with clinical teams for bench-to-bedside applications Dr. Cai's recent work explores lineage plasticity in aged lung stem cells, ferroptosis suppression via CRISPR screens, and cellular aging atlases across species. She previously held a postdoctoral position at Genentech Inc. and maintains a professional lab website. Contact: xcai2@luc.edu | Office: CTRE 123
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.
Anders Krogh is a Professor at the Department of Computer Science, University of Copenhagen, and also holds a position at the Department of Public Health in the Section for Health Data Science and AI. He serves as the head of the Center for Health Data Science (HeaDS) in the Faculty of Health and Medical Sciences. Previously, he was affiliated with the Department of Biology at the University of Copenhagen until 2020. Dr. Krogh earned his PhD in theoretical physics but transitioned into machine learning and bioinformatics during his doctoral studies. His research spans both theoretical foundations and practical applications in these fields. He is particularly renowned for his pioneering work on hidden Markov models for biological sequences, which has had significant impact in computational biology. In recent years, Krogh's research has focused on deep generative models applied to gene expression data and other biomedical applications. His work bridges computer science with healthcare, developing AI-driven approaches for precision medicine, cancer diagnostics, and analysis of complex biological systems. His current research integrates machine learning with quantum computing applications in biomolecular modeling. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence to healthcare challenges, particularly in rare diseases, cancer diagnostics, and personalized medicine. His work increasingly incorporates federated learning approaches to address privacy concerns while enabling collaborative research across institutions. There's also a growing emphasis on quantum computing applications in biomolecular modeling and drug discovery. As head of the Center for Health Data Science, Krogh leads interdisciplinary research efforts that bring together computer scientists, medical researchers, and clinicians. His team develops novel computational frameworks like MOSAIC for multimodal analysis of rare cancers and multiDGD for multi-omics data integration. These tools are designed to translate AI innovations into clinical practice while addressing the unique challenges of medical data.
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 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.
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