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 .
Mona Singh is a Professor of Computer Science at Princeton University, with affiliations to the Lewis-Sigler Institute for Integrative Genomics and the Department of Molecular Biology. She has been a faculty member since 1999. Ph.D., Massachusetts Institute of Technology, 1995 A.B. and S.M. degrees in Computer Science from Harvard University Her research focuses on computational molecular biology, integrating machine learning and algorithms to analyze biological networks, protein interactions, and mutational impacts. Key areas include DNA/RNA binding prediction, protein structure analysis, and network-based disease gene discovery. Her recent work highlights trends in protein language models, kinase-substrate prediction, and equitable MHC binding algorithms. These span sub-fields like structural bioinformatics, network biology, and functional genomics. Scientific Awards: Presidential Early Career Award for Scientists and Engineers (PECASE) Rheinstein Junior Faculty Award ACM Fellow (2019) ISCB Fellow (2018) She has taught an introductory computational biology course with Professor Coleen Murphy, covering sequence analysis, phylogenetics, and network reconstruction. Her group has developed tools like dPUC , nCOP , and DiffMut . Her lab collaborates with institutions including Carnegie Mellon, Duke University, and the Broad Institute, advancing applications in cancer genomics, metabolic disease, and precision medicine.
Joshua B. Gross is an Associate Professor in the Department of Biological Sciences at the University of Cincinnati, where he has been conducting research since 2010. His work focuses on evolutionary biology, particularly using the Mexican cavefish ( Astyanax mexicanus ) as a model system to study adaptation to extreme environments. Dr. Gross received his academic training at prestigious institutions: Ph.D. in Organismic and Evolutionary Biology from Harvard University (2005) M.S. with Distinction in Biological Sciences from University of Denver (2001) B.A. in Psychology from Miami University (1995) Dr. Gross's research explores the genetic and developmental basis of evolutionary changes, with a focus on how organisms adapt to extreme environments. His primary model system is the Mexican cavefish ( Astyanax mexicanus ), which exists in both surface-dwelling (with eyes and pigmentation) and cave-dwelling (blind and depigmented) forms. His work integrates quantitative genetics, transcriptomics, and phenotypic analysis to understand the genetic changes underlying cave adaptation, including both regressive traits (like eye loss) and constructive traits (like enhanced taste systems). Analysis of Dr. Gross's recent publications reveals a strong focus on sensory adaptation and craniofacial evolution in cavefish. His work has increasingly incorporated genomic and transcriptomic approaches to understand how cavefish adapt to low-oxygen environments, changes in sensory systems (particularly taste and lateral line), and craniofacial modifications. There's a clear trajectory toward understanding the integration between different biological systems, such as how sensory neuromasts influence skeletal development. Dr. Gross has received several notable awards and recognitions: National Academies Education Fellow in the Life Sciences (2014-2015) Young Investigator Winner, Sigma Xi, University of Cincinnati Chapter (2016) Honorable Mention, Excellence in Doctoral Mentoring Award Nominated for 2018 Dean's Award for Innovative Instruction Young Anatomist's Publication Award from the American Association of Anatomists (2004) As a principal investigator, Dr. Gross has secured substantial funding from the National Science Foundation and National Institutes of Health, including multiple R01 grants from NIH and major awards from NSF. His current projects include "The developmental basis for sensory-skeletal integration: The osteo-inductive role of neuromasts" (NSF IOS-2205928, 2022-2026) and "The constructive evolution of gustation: Molecular, organismal and environmental attributes of taste tuning" (NSF DEB-2343857, 2024-2028). He has mentored numerous undergraduate and graduate students through research projects and has been recognized for his teaching excellence, particularly in Human Genetics. Dr. Gross leads a research laboratory focused on evolutionary and developmental biology at the University of Cincinnati. His team employs a multidisciplinary approach combining field work in Mexican caves, laboratory experiments, genomic analysis, and developmental studies. He has organized international scientific meetings, including the Astyanax International Meeting, fostering collaboration among researchers studying cave-adapted organisms worldwide.
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.
Philip Poole is a Professor of Plant Microbiology at the University of Oxford's Department of Plant Sciences and Senior Research Fellow at Somerville College. His research focuses on plant-microbe interactions, nitrogen fixation, and rhizosphere microbiology. He has led major international projects including the BBSRC-NSF Synthetic Symbioses program (2014-2019) and the India-UK Nitrogen Fixation Consortium (2016-2019). With 26 grants as PI from the UK's BBSRC, he has secured over £10.5 million in funding. His work includes pioneering bacterial Lux biosensors for metabolite analysis, transcriptomics under sterile conditions, and metatranscriptomics in soil to study microbiome-plant interactions. Current projects model nitrogen fixation biochemistry in legume nodules and investigate rhizobia lifecycle transitions from rhizosphere colonization to symbiotic bacteroid differentiation. He co-directs the Oxford Centre for Plants for the 21st Century and serves on editorial/advisory boards for Microbiology UK, The Journal of Bacteriology, and Pivot Bio. His contributions include elucidating the ammonia-alanine pathway for nitrogen secretion and demonstrating symbiotic auxotrophy dependencies in bacteroids. Key achievements include developing global mutagenesis strategies (INSeq) and advancing understanding of microbial community structures in the rhizosphere. His research integrates molecular, genetic, and systems biology approaches to address global challenges in sustainable agriculture.
Christopher Buckley is the Kennedy Professor of Translational Rheumatology and Director of Clinical Research at the Kennedy Institute of Rheumatology, University of Oxford. He holds concurrent roles as Director of NIHR Infrastructure for Birmingham Health Partners. His research focuses on fibroblast biology in rheumatoid arthritis (RA), stromal cell interactions, and translational medicine approaches to stratified therapy. He leads the Arthritis Therapy Acceleration Programme (A-TAP), advancing precision medicine strategies for immune-mediated inflammatory diseases. Educations: BSc Biochemistry, University of Oxford (1985) MBBS Medicine, Royal Free Hospital, London (1990) DPhil in Molecular Medicine (Wellcome Trust Fellowship) under Prof. John Bell (Oxford) Research Interests: Pathogenic fibroblast subpopulations in RA and systemic sclerosis Tissue-resident memory T cells in chronic inflammation Spatial transcriptomics of synovial and tendon tissues Pro-resolving fibroblast networks during inflammation resolution Development of biomarkers for disease flare/remission Awards & Leadership: MRC Senior Clinical Fellowship (2001) Arthritis Research UK Professorship (2002) Director, Birmingham NIHR Clinical Research Facility (2012-2017) Key Projects: Leading A-TAP's stratified pathology approach for drug development Investigating Wnt signaling in stromal inflammation Developing cellular atlases of joints using spatial transcriptomics
Bertram Müller-Myhsok is a Research Professor and Research Group Leader at the Max Planck Institute of Psychiatry in Munich, Germany. His research focuses on statistical genetics and transcriptomic data analysis in psychiatric disorders, particularly major depression, PTSD, schizophrenia, and their treatment responses. He integrates machine learning with genetic and clinical data to develop predictive models and stratified treatment approaches. Professional activities include leadership roles in the International Max Planck Research School for Translational Psychiatry and collaborations with institutions like the Institut du Cerveau (Paris) and Bernhard Nocht Institute (Hamburg). His work spans genetic epidemiology, psychiatric genomics, and precision medicine, with over 400 publications in high-impact journals. Key research areas include identifying genetic risk factors for mental disorders, developing polygenic scores, and leveraging omics data to uncover disease mechanisms. He leads projects like Psych-STRATA, a Horizon Europe-funded initiative advancing personalized psychiatry through pharmacogenomics.
Yang Luo is a Kennedy Trust Senior Research Fellow in Data Science at the University of Oxford's Kennedy Institute of Rheumatology. His research bridges statistical genomics and computational immunology to unravel genetic contributions to immune-mediated traits, with a focus on the major histocompatibility complex (MHC) region. His work leverages large biobank datasets (UK Biobank, Biobank Japan), gene expression resources (GTEx), and proteomic data to decode molecular mechanisms linking genetic variation to disease risk. Specific interests include tuberculosis genetics, multi-ancestry polygenic risk scores, and single-cell eQTL modeling. Recent publications highlight expertise in HLA association studies, evolutionary immunogenetics, and disease-specific cell state dynamics. Key contributions include constructing a global HLA haplotype panel and developing novel statistical methods for admixed population genetics. Scientific Awards: Kennedy Trust Senior Research Fellow in Data Science His lab integrates computational and experimental approaches to translate genetic findings into clinical applications for immune disorders.
Dr. Jacques Archambault is a Professor in the Department of Microbiology and Immunology at McGill University , and an associate member of the Division of Experimental Medicine since 2016. His research focuses on the molecular biology and pathogenesis of human papillomaviruses (HPVs) and polyomaviruses (HPyVs), with an emphasis on their replication mechanisms as episomes in host cells. The Archambault laboratory employs functional genomics, proteomics, and chemical biology approaches to identify cellular pathways exploited by these viruses and develop high-throughput assays for screening small molecule inhibitors of viral replication. Analysis of his recent publications reveals a strong focus on HPV and HPyV replication machinery, including studies on the E1 helicase, UAF1-USP1 interactions, and structural characterization of viral proteins involved in DNA replication. His work bridges virology, oncology, and drug discovery, particularly targeting oncogenic HPV types implicated in anogenital and oropharyngeal cancers, as well as HPyVs like BKPyV and JCPyV that cause pathologies in immunosuppressed patients. Current efforts in the lab aim to elucidate the molecular mechanisms by which HPVs and HPyVs replicate their genomes and to develop antiviral therapies targeting these processes. Techniques such as fluorescence anisotropy, NMR spectroscopy, and crystallography are frequently employed to study protein-DNA and protein-protein interactions critical to viral replication.
Vikas Singh is a Professor in the Department of Biostatistics at the University of Wisconsin-Madison, with appointments in Computer Sciences and Statistics. He also serves as a part-time Faculty Researcher at Google DeepMind. His research focuses on image analysis, machine learning, and medical imaging applications, particularly in neuroimaging and Alzheimer's disease studies. Singh holds a Ph.D. in Computer Science from SUNY Buffalo and has taught courses such as BMI/CS 767 (Medical Image Analysis) and CS 766 (Computer Vision). Affiliations: UW Computer Vision Group, Wisconsin Alzheimer's Disease Research Center (W-ADRC), Machine Learning@UW. Research: Develops algorithms for medical image analysis, including tools for neuroimaging and longitudinal biomarker studies. Grants: Collaborates on grants related to Alzheimer's progression modeling and imaging techniques. His work emphasizes interdisciplinary applications, bridging statistics, geometry, and optimization to solve real-world problems in healthcare and engineering.
Christian P Petersen, PhD is a Professor in the Department of Cell and Developmental Biology at the Weinberg College of Arts and Sciences , Northwestern University Feinberg School of Medicine. His research focuses on molecular mechanisms underlying regeneration in planarians and other organisms. PhD: MIT (2006) Research Interests: Planarian regeneration and tissue patterning Wnt signaling pathway regulation Stem cell biology in regenerative contexts Neurogenesis and injury response Molecular mechanisms of tissue repair Affiliations: Center for Reproductive Science Robert H. Lurie Comprehensive Cancer Center
Ying Ge is a Professor at the University of Wisconsin–Madison, jointly appointed in the Department of Cell and Regenerative Biology and the Department of Chemistry. Her research integrates chemistry, biology, and medicine, focusing on advanced mass spectrometry-based proteomic and metabolomic technologies to address cardiovascular diseases. Education: B.S., Peking University (1997) Ph.D., Cornell University (2002) Ying Ge's work centers on developing ultra high-resolution mass spectrometry platforms for top-down proteomics and metabolomics, applied to systems biology studies of heart failure and regenerative medicine. Key projects include myofilament protein modification mapping, stem cell therapy evaluation, and biomarker discovery for cardiac conditions. The 15 most recent articles highlight her lab's methodological innovations (e.g., photocleavable surfactants, native mass spectrometry) and biological discoveries in AMPK structural heterogeneity, RBM20-mediated cardiotoxicity, and sarcomere-metabolism cross-talk during regeneration. These publications span proteomics, metabolomics, structural biology, and clinical applications.
Maria Timofeeva is an Associate Professor in the Epidemiology, Biostatistics and Biodemography (EBB) department at the University of Southern Denmark (SDU), with additional affiliation at the Danish Institute for Advanced Study (DIAS). She holds an Honorary Fellow position at the University of Edinburgh since December 2019. Her research focuses on cancer prevention and prediction, particularly studying the effects of environmental and genetic factors on cancer risk and progression. Dr. Timofeeva earned her Dr.sc.hum in Epidemiology from Heidelberg University (2005-2009), with a dissertation on genetic polymorphisms as risk factors for early onset lung cancer. Prior to her current position, she worked as a Statistical Geneticist at the University of Edinburgh (2013-2019) and as a Postdoctoral Fellow at the International Agency for Research on Cancer (2009-2013). Her research interests center around understanding the genetics of cancer risk through multi-omic analysis. She leads several significant projects, including the Interdisciplinary Project on Adherence to Colorectal Cancer Screening, meta-analysis of factors associated with false-positive and false-negative FOBT results (registered in PROSPERO ID: CRD42022315767), and the COlorectal Cancer screening Among RElatives (CoCARE) twin-family study in Denmark. Her methodological expertise spans observational epidemiological studies (case-control, population-based cohort studies, twin studies), meta-analysis, umbrella reviews, and multi-omics data analysis. Analysis of her recent publications reveals a strong focus on colorectal cancer genetics, with particular emphasis on genome-wide association studies, Mendelian randomization approaches, and trans-ancestry analyses. Her work frequently leverages large datasets including the UK Biobank and international consortia, with applications in cancer risk prediction and understanding gene-environment interactions. Dr. Timofeeva has an extensive publication record with 73 publications listed in her profile. Her research has been cited across multiple platforms, with mentions in news outlets, social media, and academic readership platforms like Mendeley. She is actively involved in academic service, serving as a peer reviewer for journals including BMC Cancer and Scientific Reports, and participating in conferences such as the 26th Nordic Congress of Gerontology. She also serves on evaluation committees, including with the World Cancer Research Fund International (April-May 2024). Her teaching activities include courses on evidence-based drug utilization and biostatistics, as well as supervision of research projects on gene expression in twins. Dr. Timofeeva has engaged with the public through media contributions, including an interview titled 'Jeg vil forstå, hvorfor vi får kræft' (November 15, 2021), where she discussed understanding why we get cancer.
Xenophon Papademetris is a Professor of Biomedical Informatics & Data Science and Radiology & Biomedical Imaging at Yale School of Medicine. He serves as Associate Director of Biomedical Imaging Data Sciences at Yale Biomedical Imaging Institute and directs the Medical Software and Medical Artificial Intelligence Certificate Program. PhD in Electrical and Information Sciences from Yale University (2000) BA from Cambridge University (1994) Postdoctoral Fellowship at Yale University (2002) His research focuses on medical image analysis, machine learning, and biomedical software development. He has developed tools like BioImage Suite Web and contributed to standards committees at the Association for the Advancement of Medical Instrumentation (AAMI). His work spans modalities including MRI, CT, PET, and optical imaging. Recent publications emphasize neuroimaging analysis, explainable AI in healthcare, and multimodal data integration across species. He leads NIH-funded research under the BRAIN Initiative (R24 MH114805) and has authored a textbook on Medical Software published by Cambridge University Press. IEEE Senior Member Yale Brown-Coxe Postdoctoral Fellowship Harding Bliss Prize for Excellence in Engineering He directs the BioImage Suite Project, creating web-based image analysis tools using JavaScript and WebAssembly. His teaching includes both academic courses and a Coursera program on Medical Software with over 14,000 enrollments.
