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Gerald QuonView profile

Associate Professor

• Computational Biology
• Single Cell Genomics
• Epigenomics
• +5 more

Gerald Quon is an Associate Professor in the Department of Molecular and Cellular Biology at the University of California, Davis. He is affiliated with the Genome Center and participates in multiple graduate programs, including Integrative Genetics and Genomics, Neuroscience, Computer Science, Biostatistics, and Biomedical Engineering. Education: PhD in Computer Science from the University of Toronto (2012) MSc in Biochemistry from the University of Toronto (2006) Research Interests: Dr. Quon applies computational approaches to genetics and genomics problems, focusing on the genetics of human disease , models of cell population dynamics , and neurogenomics . His lab builds neural network models to understand how genetic variation affects disease risk through molecular and cellular phenotypes, with applications to obesity, Alzheimer’s disease, psychiatric disorders, and Rett syndrome. Recent Research Trends: Recent publications highlight work in neuroplasticity , single-cell multimodal analysis , brain evolution , morphological variation modeling , and microbiome-based classification . His team combines sequencing and imaging technologies to model cellular interactions and gene expression dynamics. Scientific Awards: NIH New Innovator Award (2021) Grants & Collaborations: He received NSF funding (2019) for computational tools in single-cell analysis and collaborates across disciplines, including neuroscience, biomedical engineering, and computational biology. His lab develops software like scProjection , siVAE , and scAlign .

Maria GolsonView profile

Assistant Professor

• Endocrinology
• Beta-cell Function
• Insulin Secretion
• +6 more

Maria Golson, PhD, is an Assistant Professor of Medicine in the Division of Endocrinology, Obesity and Metabolism at the Johns Hopkins School of Medicine. She joined the institution in 2020 and leads research focused on the intrinsic and extrinsic regulation of beta-cell function and insulin secretion. Her work is central to understanding pancreatic endocrine cell development and its implications in diabetes. Education: B.S. in Biology (Genetics concentration), Duke University, 2000 Ph.D. in Cell and Molecular Biology (Genetics and Gene Regulation), University of Pennsylvania, 2008 Postdoctoral Training, Vanderbilt University Dr. Golson's research spans molecular mechanisms of beta-cell function, epigenetic regulation in diabetes, and single-cell analysis of pancreatic islets. She employs advanced genomic and epigenomic tools to explore cellular states in both type 1 and type 2 diabetes, with a focus on identifying pathways that could be targeted for regeneration or functional enhancement of insulin-producing cells. Her recent publications, appearing in journals such as Nature Metabolism and Diabetes , reflect a strong trend toward multiomics and single-cell technologies to uncover novel cellular states in human islets. These studies integrate transcriptomics, epigenetics, and functional validation to dissect disease mechanisms. Dr. Golson has been supported by institutional affiliations and collaborations, including membership in the HPAP Consortium and the American Diabetes Association. While no formal awards are listed, her publication record indicates significant scientific contributions. She has mentored researchers through collaborative projects, though no formal advisees are named. Her lab engages in interdisciplinary research involving genetics, molecular biology, and bioinformatics to advance diabetes therapeutics.

Doron BetelView profile

Assistant Professor

• Computational Genomics
• Bioinformatics
• Single-cell RNA-seq Analysis
• +3 more

Doron Betel serves as an Assistant Professor at Weill Cornell Medicine's Graduate School of Medical Sciences, with affiliations in both the Physiology, Biophysics & Systems Biology and Computational Biology programs. He directs the Applied Bioinformatics Core (ABC), a central service group providing specialized computational and analytical support for biomedical research across multiple institutions. Dr. Betel's research focuses on developing computational genomic tools for studying human diseases and cellular development, with emphasis on integrative analyses of genomic and epigenomic data from high-throughput assays. His work addresses specific questions related to disease progression, treatment response, stem cell differentiation, and neurological processes through two closely interacting research groups: the Applied Bioinformatics Core and his independent research lab. The analysis of his recent publications reveals a strong emphasis on single-cell and spatial genomics, cross-species data integration, and machine learning applications in cancer immunology and neurodegenerative disease modeling. His research spans multiple high-impact areas including cancer immunotherapy, stem cell biology, diabetes research, and cardiovascular regeneration, with numerous publications in top journals like Nature, Cell, and Nature Immunology. Through the Applied Bioinformatics Core, Dr. Betel provides extensive analytical support across various genomic platforms including single-cell RNA-seq, spatial transcriptomics, ChIP-seq, ATAC-seq, and variant calling. The Core serves as a vital resource for researchers at Weill Cornell Medicine and the broader Tri-Institutional network, offering specialized analysis, computational pipelines, and training services. Dr. Betel maintains extensive collaborations with leading researchers including Lorenz Studer at MSKCC for stem cell and neurodegenerative disease research, Tuomas Tammela for cancer genomics, and multiple immunology researchers studying T cell function in autoimmunity and cancer. His work bridges computational methodology development with direct biomedical applications across multiple disease areas.

• Genomic Medicine
• Translational Medicine
• Immunogenetics
• +4 more

Julian Knight is a Professor of Genomic Medicine at the University of Oxford, with affiliations including the Centre for Human Genetics , Merton College , and leadership roles in the NIHR Oxford Biomedical Research Centre and Central and South NHS Genomic Medicine Service . His work bridges clinical practice and research, focusing on translational genomics. Principal Investigator Deputy Director, Centre for Human Genetics Honorary Consultant Physician Tutor and Fellow, Merton College Director, Medical Sciences Division Graduate School Genomic Medicine Theme Lead, NIHR Oxford BRC Research interests include mechanisms of dysregulated immune responses in sepsis , autoimmune disease , and infection . Key contributions involve RNA signature stratification for sepsis outcomes and HLA allele associations in COVID-19 immunogenicity. Current work explores genetic/epigenetic modulators of innate immunity and causal relationships in multi-omic datasets. Recent publications highlight diverse applications of his group’s work: from pleural infection endotyping (2025) to TLR7 variants in severe COVID-19 (2024), with methodological advancements in single-cell demultiplexing (2024) and pathway analysis (2025). Keywords span genomic medicine , immunology , and multi-omic integration . Knight’s leadership extends to clinical implementation of genomics, education (DPhil/MSc programs), and public engagement. Collaborations span institutions including Imperial College , Wellcome Sanger Institute , and Queen Mary University of London .

• Computational Systems Biology
• Multi-omics Data Analysis
• Single-cell Sequencing
• +5 more

Nikolaus (Nik) Fortelny is a Group Leader in Computational Biology at the University of Salzburg, Austria, where he leads the Computational Systems Biology research group within the Department of Biological Sciences & Medical Biology. His research focuses on understanding biological systems at the molecular level through advanced computational approaches. Dr. Fortelny's research interests include: Computational Systems Biology Multi-omics data integration and analysis Single-cell and spatial biology Machine learning applications in biology Network science approaches to biological regulation Immune system modeling His recent publications demonstrate a strong focus on applying computational approaches to understand complex biological systems, particularly in immunology and cellular regulation. His work often involves collaboration with experimental biologists to generate and analyze large-scale datasets from multi-omics experiments collected at single-cell or spatial resolution. Dr. Fortelny is actively involved in research recruitment and is currently hiring for professor positions in Medical Systems Biology and Animal Physiology at the University of Salzburg, with an application deadline of April 19th, 2025. His group regularly seeks students, PhD candidates, postdocs, and staff scientists to join their team.

Valentina BoevaView profile

Assistant Professor

• Machine Learning
• Bioinformatics
• Genomics
• +3 more

Prof. Valentina Boeva is an Assistant Professor at the Department of Computer Science, ETH Zürich, specializing in biomedical informatics. Her research focuses on integrating machine learning and computational methods to address challenges in genomics, oncology, and precision medicine. She holds a position in the Professur für Biomedizininformatik (Biomedical Informatics) and is based at CAB G32.2, Universitätstrasse 6, Zürich, Switzerland. Her work emphasizes applications such as cancer biomarker discovery, tumor heterogeneity analysis, and epigenetic profiling. She teaches courses including Machine Learning Seminar, Data Science Lab, and Machine Learning for Genomics. Her research group develops computational tools like CDState and UniversalEPI to decode complex biological systems. She actively publishes in top-tier journals, with recent work on exosome-driven diagnostics and chromatin interaction modeling. Her scientific contributions span methodologies for single-cell data analysis, survival modeling, and drug response prediction. She collaborates across disciplines to bridge computational science with clinical applications in cancer research.

Emre SeferView profile

Associate Professor

• Bioinformatics
• Machine Learning
• Financial Networks
• +3 more

Dr. Emre Sefer is an Associate Professor at the Faculty of Engineering, Özyeğin University, specializing in machine learning and bioinformatics. He holds a Ph.D. in Computational Biology from Carnegie Mellon University (2015), an M.S. in Computer Science from University of Maryland College Park (2011), and a B.S. in Computer Engineering from Boğaziçi University (2008). His research bridges graph machine learning with financial networks, bioinformatics, and data engineering. Ph.D.: Computational Biology, Carnegie Mellon University M.S.: Computer Science, University of Maryland College Park B.S.: Computer Engineering, Boğaziçi University Research focuses on applying machine learning to financial and biological networks: Bioinformatics : 3D genome modeling, protein modifications, transcriptomic analysis Graph Machine Learning : GNNs for fraud detection, drug response prediction, and network evolution Financial Networks : Cryptocurrency investment strategies, asset price prediction His lab (OzU Machine Learning in Finance and Bioinformatics Lab) develops graph-based deep learning methods for cross-domain applications, including NFT market analysis and chromatin structure prediction. He received the Best research paper award at Recomb 2016 for work on 3D genome architecture. Former postdoc at CMU Machine Learning Department Industry experience as Quantitative Strategist at Goldman Sachs and JPMorgan

Zhandong LiuView profile

Associate Professor

• Bioinformatics
• Big Data analysis
• Machine Learning
• +5 more

Zhandong Liu is an Associate Professor at Baylor College of Medicine with joint appointments in the Department of Pediatrics and Department of Neurology . He serves as Chief of Computational Sciences at Texas Children's Hospital and co-directs the Quantitative & Computational Biosciences Graduate Program at Baylor. Education: B.S. in Computer Science, Nankai University (2001) M.S. in Computer Science, Wayne State University (2003) Ph.D. in Genomics and Computational Biology, University of Pennsylvania (2010) Dr. Liu's research integrates genomics , machine learning , and bioinformatics to advance understanding of neurological diseases. His work focuses on: Multi-omics data integration for disease mechanism discovery Development of cloud-based CRISPR analysis tools like CRISPRcloud Augmented reality platforms for biomedical data visualization Identification of disease genes through computational models Alternative splicing analysis in cancer and neurodegeneration Single-cell and spatial transcriptomics algorithms His recent publications emphasize Alzheimer's disease , MECP2 syndromes , and computational therapy prediction across multiple domains. Scientific awards include the 2018 Outstanding Service Award from the International Association for Intelligent Biology and Medicine. He has secured major grants from NIH, CPRIT, and NSF for projects including: NSF grant #199977 (2018-2020): Augmented reality therapy platforms CPRIT grant #RP170387 (2016-2019): Network-guided cancer analysis NIH #1R01AG057339 (2017-2022): Alzheimer's disease networks As head of the Liu Lab , he leads teams developing tools like: MARRVEL : Human-model organism gene variant integration CRISPRcloud : Secure CRISPR screen analysis platform CrypSplice : Cryptic splicing detection algorithm

• Computational Biology
• Single-Cell Genomics
• Machine Learning
• +6 more

Dana Pe'er is Chair of the Computational and Systems Biology Program at the Sloan Kettering Institute (SKI) and an Investigator at the Howard Hughes Medical Institute (HHMI). She holds the Alan and Sandra Gerry Endowed Chair and leads an interdisciplinary lab combining single-cell genomics, machine learning, and computational modeling to study cancer biology, immunity, and development. Pe'er earned her PhD at the Hebrew University in Jerusalem and focuses on cellular plasticity, epigenetic regulation, and tumor-immune interactions. Her lab develops tools like CellRank , Wishbone , and SEACells to analyze single-cell data and uncover mechanisms in cancer progression and immunotherapy. Key research areas: Computational Biology, Single-Cell Genomics, Cancer Systems Biology, Epigenetics, Immunotherapy Recent trends: Articles from 2025-2024 emphasize spatial transcriptomics, tumor microenvironment mapping, and regulatory network inference using machine learning. Scientific honors include the NIH Director’s Pioneer Award , AACR Academy Induction , and Packard Fellowship . Her work has direct clinical implications for precision medicine and cancer immunotherapy. Labs & Teams: Leads the Dana Pe'er Lab at SKI, directs the Single Cell Research Initiative (SCRI), and collaborates with the SAIL program.

Jonathan WeissmanView profile

Professor

• Protein Folding
• Systems Biology
• Genomics
• +6 more

Jonathan Weissman is a Professor of Biology at the Massachusetts Institute of Technology (MIT) and a Member of the Whitehead Institute. He is also an Investigator of the Howard Hughes Medical Institute and the Landon T. Clay Professor of Biology. His research spans protein folding mechanisms, ribosome profiling, CRISPR-based tools (CRISPRi/a), and genetic interaction mapping. Whitehead Institute Member MIT Professor HHMI Investigator Co-founder, Maze Therapeutics & KSQ Therapeutics Research Interests focus on: Protein folding in cellular contexts Endoplasmic reticulum (ER) function and stress responses Genome-wide CRISPR screening for gene regulation High-density genetic interaction maps in mammals Mitochondrial protein targeting and quality control Epigenomic engineering with synthetic tools Scientific Awards include: Protein Society Irving Sigal Young Investigator Award (2004) Raymond & Beverly Sackler Prize (2008) National Academy of Sciences election (2009) NAS Award for Scientific Discovery (2015) Genetics Society of America Ira Herskowitz Award (2020) Labs & Collaborations : Leads the Weissman Lab at MIT/Whitehead Institute, co-leads the Laboratory for Genomic Research with GlaxoSmithKline, and chairs the Stowers Institute Scientific Advisory Board.

Michael SkinniderView profile

Assistant Professor

• Computational Biology
• Machine Learning
• Metabolomics
• +4 more

Michael Skinnider serves as Assistant Professor at Princeton University's Lewis-Sigler Institute for Integrative Genomics and Assistant Member of the Ludwig Princeton Branch. His research develops AI-driven computational methods to identify unknown small molecules in mass spectrometry data, with applications in cancer biology and forensic drug detection. His educational background includes: BArtsSc from McMaster University (2015) PhD from University of British Columbia (2021) MD from University of British Columbia (2023) Skinnider's work centers on illuminating the "metabolomic dark matter" —unidentified chemical entities in mass spectrometry data. His lab pioneers machine learning approaches for metabolite identification, focusing on connections between unknown metabolites, cancer risk, and the microbiome. Recent innovations include chemical language models that transform mass spectrometry outputs into chemical structures, with applications spanning cancer diagnostics to forensic analysis of designer drugs. His research bridges computational biology, chemistry, and clinical medicine through low-data learning techniques. Publication trends reveal three dominant themes: (1) AI-driven metabolite identification (25% of recent work), (2) single-cell/spatial data analysis (40%), and (3) molecular interaction networks (35%). His 2024 Nature Machine Intelligence paper demonstrated that invalid SMILES strings enhance chemical language models , overturning previous assumptions. Articles consistently apply computational methods to biological discovery, with growing emphasis on cancer metabolism and translational applications. Major recognitions include: Forbes 30 Under 30 (2022) International Birnstiel Award (2022) Dan David Prize Borealis AI Fellowship NIH Award C&EN's Talented Twelve (2023) Young Explorer Award Grand Prize Skinnider leads the Skinnider Research Lab at Princeton's Carl Icahn Laboratory, which collaborates with forensic laboratories and Ludwig cancer researchers. The lab specializes in transforming mass spectrometry data into biological insights through innovative algorithms. During his undergraduate studies, he co-founded Adapsyn Bioscience to translate natural product discovery research into commercial applications. Current projects include developing metabolome-wide identification tools and exploring diet-derived metabolites that modulate cancer progression.

Jiangwen SunView profile

Assistant Professor

• Machine Learning
• Data Mining
• Medicine
• +6 more

Jiangwen Sun is an Assistant Professor in the Department of Computer Science at Old Dominion University (ODU), within the College of Science. He directs the ODU Computational Systems Medicine Lab and focuses on machine learning approaches for analyzing multi-dimensional biological data (phenome, genome, transcriptome, etc.) to advance precision medicine and its automation. His research is supported by ODU and federal agencies like NIH and NSF. Education: Ph.D., University of Connecticut M.E., Nanjing University, China B.M., Secondary Military Medical University, China Research Interests: Machine learning/data mining for medicine, health, drug discovery, and bioinformatics Multi-view bi-clustering and integrative analysis of genomic/phenotypic data Phenotype refinement and genetic association studies for complex diseases Applications in addiction medicine, cardiovascular biology, and bovine development Publications: Over 40 peer-reviewed articles in top venues like NIPS, ICML, Bioinformatics, and BMC Genomics. Recent work focuses on cryo-EM protein structure analysis, single-cell multiomics, and epigenetic modeling. Awards/Grants: NIH/NSF funding pending; previously supported by UConn's Health Informatics Lab and collaborations with University of Pennsylvania. Teaching: Courses include Machine Learning (CS722/822), Data Structures (CS361), and Deep Learning in Medicine (CS795/895). Labs/Teams: Leads the ODU Computational Systems Medicine Lab and collaborates with UConn Health Informatics Lab, Penn Medicine, and others.

Jun WuView profile

Assistant Professor

• Stem Cell Biology
• Genome Editing
• Interspecies Chimeras
• +6 more

Dr. Jun Wu is an Assistant Professor in the Department of Molecular Biology at UT Southwestern Medical Center. He holds a PhD in Life Science from the University of Tennessee and completed postdoctoral training at the University of Southern California and the Salk Institute. His research focuses on stem cell biology, genome editing, and interspecies chimeras to advance regenerative medicine and developmental biology. Education: Bachelor of Medicine, Shandong University School of Medicine (China) PhD in Life Science, University of Tennessee (2013) Postdoctoral Fellowships: USC (2013-2015) and Salk Institute (2015-2017) Research Interests: Generation of pluripotent stem cells with distinct molecular/phenotypic features Development of interspecies blastocyst complementation systems Modeling peri-implantation human development using stem cell embryo models Study of species-specific developmental barriers and evolutionary biology Application of chimeras to study cancer resistance and organ size determination Lab Activities: The Wu Lab develops stem cell models to study mammalian development and create regenerative therapies. Key projects include: Creation of interspecies chimeras (human-monkey, rat-mouse) Development of blastoids and peri-gastruloids Analysis of species-specific cell competition mechanisms Engineering cross-species organogenesis systems Grant Activities: Focuses on NIH-funded projects related to stem cell biology, interspecies chimerism, and regenerative medicine applications. Collaborates with institutions like Salk Institute and University of California campuses. Lab Team: Includes postdoctoral researchers like Dr. Yi Ding and a multidisciplinary team of molecular biologists, bioengineers, and computational biologists.

Rana K. GuptaView profile

Professor

• Adipose tissue biology
• Energy homeostasis
• Obesity and metabolic disease
• +5 more

Rana K. Gupta, PhD , is the W. David and Sarah W. Stedman Distinguished Professor of Medicine and Cell Biology at Duke University School of Medicine and serves as Section Chair of Basic Sciences within the Duke Molecular Physiology Institute . Previously, he spent ten years on the faculty at the University of Texas Southwestern Medical Center. His laboratory investigates the developmental biology and molecular regulation of adipose tissue, with the goal of translating insights into therapies for obesity and metabolic diseases. Education & Training: PhD, University of Pennsylvania (2006) Research Fellow, Dana-Farber Cancer Institute, Cell Biology (2006–2012) Research Interests: Dr. Gupta’s work centers on two inter-related themes: (1) the transcriptional networks that establish and maintain white, brown, and beige adipocyte lineages, with a spotlight on the zinc-finger factor ZFP423 ; and (2) the heterogeneity and functional specialization of adipocyte progenitor cells during healthy versus pathological adipose-tissue expansion. Using single-cell multi-omics, lineage tracing, and metabolic phenotyping in mice and humans, his team deciphers how distinct progenitor pools orchestrate adipogenesis, angiogenesis, and immune remodeling in obesity. Key Publications: Across 78 peer-reviewed papers (2010-2025), Dr. Gupta has defined ZFP423 as a molecular brake on adipocyte thermogenesis, uncovered PDGFRβ+ progenitor subpopulations that drive hyperplastic adipose growth, and demonstrated that inducible Zfp423 deletion can convert white adipocytes into energy-burning beige cells to reverse diet-induced obesity. Recent single-cell atlases further reveal sex- and depot-dependent progenitor heterogeneity, providing a roadmap for targeted metabolic therapies. Scientific Awards & Honors: W. David and Sarah W. Stedman Distinguished Professorship Funding & Training Leadership: Principal Investigator on 13 active NIH, ADA, and foundation grants (2021-2029) Director, Endocrinology and Metabolism Training Program (NIDDK T32) Co-Investigator, Medical Scientist Training Program (NIGMS T32) Mentor to 6 postdocs, 4 graduate students, and numerous undergraduates and research technicians Lab Teams & Collaborations: The Gupta Lab at Duke is a multi-disciplinary team of postdoctoral fellows (Pablo Morales, Wenxin Tong), graduate students (Ashley Truong), instructors (Jessica Cannavino), and research analysts (Krissy Campbell, Lavanya Vishvanath) collaborating closely with the Duke Molecular Physiology Institute to integrate genomics, physiology, and translational medicine.

• Computational Biology
• Molecular Biology
• Cell Biology
• +5 more

Roy Wollman is a Professor at the University of California, Los Angeles (UCLA) in both the Department of Integrative Biology and Physiology and the Department of Chemistry and Biochemistry within the College of Letters and Science. His work bridges experimental and computational approaches to study dynamic signaling networks and their impact on cellular decisions. Research Focus: Computational and systems biology of signaling pathways Key Techniques: Single-cell analysis, spatial transcriptomics, quantitative modeling Major Themes: Information transmission in biochemical networks, cellular decision-making, epigenetic regulation Recent work has emphasized spatial transcriptomics mapping of brain regions, wound response signaling, and multi-scale analysis from cell biology to physiology. His lab has developed computational tools like scPNMF for gene selection and JSTA for cell segmentation and annotation. Key findings include mechanisms of TNF-induced cell death tradeoffs and laminin scarring effects in stem cell function. Roy Wollman has received continuous NIH funding since 2009, including grants for studying NFκB dynamics (R01GM117134), corneal wound signaling (R01EY024960), and single-cell technologies for traumatic brain injury (R01NS117148). His research combines high-throughput microscopy with computational modeling to understand how cells process dynamic signals.