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Siddharth DeyView profile

Associate Professor

• Single-cell sequencing technologies
• Epigenetic regulation of gene expression
• Cell fate decisions
• +5 more

Siddharth Dey is an Associate Professor in both the Department of Bioengineering and the Department of Chemical Engineering at the University of California, Santa Barbara. His research focuses on developing novel single-cell sequencing technologies to study epigenetic regulation of gene expression and cell fate decisions. The Dey Lab investigates mechanisms underlying DNA methylation dynamics, cancer cell invasion, and HIV latency using advanced multiomics approaches and 3D organoid models. He leads the Dey Lab , which integrates synthetic biology, optogenetics, and computational modeling to understand cellular heterogeneity and disease processes. Key research areas include: (1) Single-cell multiomics integration, (2) Epigenetic regulation during human development, (3) Mechanotransduction in cancer progression, and (4) Engineering tools for bacterial transcriptomics. Recent work demonstrates breakthroughs in simultaneous profiling of 5mC/5hmC modifications with transcriptomes, enabling unprecedented insights into epigenetic maintenance fidelity and cellular decision-making. His research is supported by grants including the NSF CAREER award (2024) for spatial epigenetic studies in human development. The lab actively collaborates on projects involving photopatterning technologies, stress response signaling, and HIV latency therapies. Experimental approaches combine cutting-edge sequencing methods with microfluidics-based platforms to study cellular responses in physiologically relevant 3D environments. Publications highlight contributions to understanding: (1) DNA methylation erasure in germ cells, (2) YAP-driven cardiac hypertrophy mechanisms, (3) macrophage priming via Fc receptors, and (4) bacterial mRNA sequencing innovations. These studies bridge engineering principles with fundamental biological mechanisms to address critical questions in development, cancer biology, and infectious diseases.

Lara NonellView profile

Associate Professor

• Bioinformatics
• Cancer Diagnostics
• Machine Learning in Biomedicine
• +3 more

Lara Nonell is an Associate Professor in the Department of Biosciences at the University of Vic – Central University of Catalonia and Head of the Bioinformatics Unit. She holds a Doctorate in Biomedicine from Pompeu Fabra University (2019) and has expertise in bioinformatics, cancer diagnostics, and multiomics data analysis. Her research focuses on developing computational methods for biomarker identification and cancer progression modeling. Education Doctorate in Biomedicine, Pompeu Fabra University (2019) MSc in Information Technology Management, La Salle Ramon Llull University (2000) Bachelor’s in Mathematics, Autonomous University of Barcelona (1996) Research Interests Dr. Nonell’s work integrates machine learning, bioinformatics pipelines, and multiomics data to drive precision oncology. Key areas include: Biomarker development for cancer diagnostics Epithelial-mesenchymal transition mechanisms in metastasis Gene expression profiling in leukemias and lymphomas Systems biology approaches to comorbidities like diabetes and cancer Publications Her 20+ peer-reviewed articles (2019-2023) span oncology, immunology, and bioinformatics. Recent work includes: Epigenetic drivers of hepatoblastoma progression MYC-driven gene signatures in melanoma prognosis MicroRNA regulation in cutaneous lymphomas Grants & Labs Leads the Bioinformatics Unit supporting VHIO research. Collaborations include multi-institutional projects in Barcelona’s biomedical ecosystem. No specific grants mentioned in the text.

Kevin HeView profile

Associate Professor

• Survival Analysis
• Healthcare Provider Profiling
• Machine Learning
• +7 more

Kevin He is an Associate Professor in the Department of Biostatistics at the University of Michigan School of Public Health. He serves as Associate Director of the Kidney Epidemiology and Cost Center (KECC) and leads statistical innovations in survival analysis, healthcare provider profiling, and data integration for biomedical applications. PhD in Biostatistics (University of Michigan, 2012) MS in Biostatistics (University of Michigan, 2008) BS in Statistics (Queen’s University, 2006) MS in Epidemiology (Queen’s University, 2004) BM in Clinical Medicine (Dalian Medical University, 2002) His research focuses on survival analysis for large-scale datasets, machine learning for healthcare provider profiling, and statistical genetics in organ transplantation and chronic disease. He develops data integration frameworks for polygenic risk scores and statistical optimization algorithms for time-varying effects in national registries. The 15 most recent articles highlight his work in survival modeling with time-varying coefficients, federated learning for privacy-preserving data integration, and genomic applications in inflammatory diseases. His methodological contributions span Kronecker product algorithms , proximal optimization , and penalized partial likelihood . He mentors a team including software developers and graduate researchers working on deep learning , frailty models , and distributed computing . His lab maintains the surtvep R package for scalable survival analysis.

Rohit SinghView profile

Assistant Professor

• Computational Biology
• Machine Learning
• Single-Cell Genomics
• +4 more

Rohit Singh is an Assistant Professor at Duke University with appointments in the Departments of Biostatistics & Bioinformatics, Cell Biology, Computer Science, and Electrical and Computer Engineering. He is a member of the Duke Cancer Institute and the Division of Integrative Genomics. Academic Rank : Assistant Professor Departments : Biostatistics & Bioinformatics, Cell Biology, Computer Science, Electrical and Computer Engineering University Affiliations : Duke Cancer Institute, Division of Integrative Genomics His research focuses on computational biology, machine learning for drug discovery, and decoding disease mechanisms through single-cell genomics and protein language models. Current projects include SAME, Velorama, Raygun, and Allo-Allo. Recent publications span single-cell analysis, protein language models, and causal inference. He develops tools like Schema , D-SCRIPT , and Sceodesic . Grants : NIAID, Chan Zuckerberg Initiative, Foundation for Prader-Willi Research Awards : MIT Sprowls Award, Stanford Stephenson Award, RECOMB Test of Time Award He advises students in computational biology and has contributed to patents in protein interaction modeling and viral detection. The lab welcomes postdocs, rotation students, and collaborators.

Supriyo BhattacharyaView profile

Academic

• Advanced bioinformatics
• Multiomics data integration
• Protein structure and dynamics
• +5 more

Supriyo Bhattacharya, Ph.D., is an Assistant Research Professor at the Department of Computational and Quantitative Medicine, City of Hope National Medical Center. His work focuses on integrating multiomics data with protein structure and dynamics to advance computational drug discovery, particularly for intrinsically disordered proteins (IDPs). Education: Ph.D. in Chemical Engineering (North Carolina State University, 2006), B.Tech. in Chemical Engineering (IIT Kharagpur, 2000) Key Research Areas: Multiomics-structure integration via AI (e.g., AlphaFold) IDP dynamics and drug targeting GPCR allosteric communication His publications emphasize computational methods for protein dynamics, with trends in Bayesian networks (2024), machine learning for conformational transitions (2025), and IDP interactions in diseases like cancer and diabetes (2021–2024). Recent work (2025) explores paxillin-GPCR dynamics and β-cell homeostasis. Scientific Recognition: NIH R01 grants (2015, 2023) GPCR DOCK 2010 top-3 ranking Editorial roles (Frontiers, Biomolecules MDPI) Anton supercomputer grant Grants & Collaborations: Co-investigator in NIH R01LM013876-02 (2023), key personnel in multi-institutional immunotherapy projects (2019), and computational resource grants. Lab & Teams: Leads integrative genomics core at City of Hope, collaborating with molecular immunology and computational therapeutics teams.

Kathrin PlathView profile

Professor

• Stem Cell Biology
• Epigenetics
• Gene Regulation
• +5 more

Kathrin Plath, Ph.D., is a Professor and Vice Chair in the Department of Biological Chemistry at the David Geffen School of Medicine, University of California, Los Angeles (UCLA) . Her research focuses on epigenetic regulation of cell fate transitions, particularly in stem cell biology, reprogramming, and X-chromosome dynamics. Education: Ph.D., Biochemistry, Humboldt University, Germany (1999) Post-doctoral Fellowships: Stem Cell Biology (Whitehead Institute, 2006), Chromatin Biology (UC San Francisco, 2003), Cell Biology (Harvard Medical School, 2000) Research Interests: Plath investigates mechanisms governing pluripotency, X-chromosome inactivation, and nutrient regulation of stem cell differentiation. Her lab develops single-cell omics technologies like SEC-seq to study transcriptomes and secretomes simultaneously, and explores 3D genome organization's role in gene regulation. Scientific Awards: HHMI Faculty Scholar (2016) ISSCR Board of Directors (2011-2017) NIH Director’s New Innovator Award (2007) Kimmel Foundation Scholar (2007) V Foundation Scholar (2007) Advising & Collaborations: Mentors students like Clara Cano and Sasha Barinsky. Collaborates with Jason Ernst (UCLA), Dino Di Carlo (UCLA), and international researchers on projects involving iPSC reprogramming, X-chromosome dosage compensation, and lung disease modeling.

Milena HasanView profile

Researcher

• Single-cell transcriptomics
• Immunology
• Infectious disease research
• +5 more

Milena Hasan is a Researcher at the Institut Pasteur in Paris, France, specializing in UTechS Single Cell Biomarkers unit. Her work focuses on cutting-edge single-cell transcriptomics , immune profiling , and cytometry data analysis across diverse conditions including viral infections, autoimmune diseases, and cancer immunotherapy. Lead investigator for multiple projects (TPAI, TTP, VARIANCE) involving single-cell technology training and advanced immune response analysis Key contributor to the Milieu Intérieur program studying immune system heterogeneity She actively develops tools for multi-omics data integration and leads courses on Single Cell Gene Expression and Beyond (2022, 2025) and Advanced Immunology (2025). Her research spans host-pathogen interactions , T cell dynamics , and inflammatory disease mechanisms through innovative technologies like spectral cytometry and single-cell proteomics.

Marylyn RitchieView profile

Professor

• Genetics
• Genomics
• Bioinformatics
• +12 more

Marylyn Ritchie serves as Professor of Genetics while holding key leadership positions including Vice Dean of Artificial Intelligence and Computing, Director of the Institute for Biomedical Informatics, and Co-Director of the Department of Biostatistics, Epidemiology and Informatics. Her work bridges computational methods with clinical genomics to address complex biomedical challenges through translational research. Her research program focuses on big data analysis in genetics and genomics, developing advanced bioinformatics approaches for polygenic risk scoring, multi-omics integration, and machine learning applications. She emphasizes inclusive research design through diverse population studies and addresses health disparities in genetic testing access. Current work includes developing ancestry-specific risk prediction models and fair AI diagnostics for neurodegenerative and ophthalmic conditions. Recent publications (2025) demonstrate leadership in Alzheimer's disease AI diagnostics, glaucoma genetics in African populations, hidradenitis suppurativa genomics, and cardiovascular risk modeling. She pioneers methods for enhancing polygenic score performance across ancestries while addressing ethical considerations in AI-driven healthcare. Professional recognition includes: Fellow of the American College of Medical Informatics (FACMI) Her administrative leadership in directing the Institute for Biomedical Informatics and Center for Translational Bioinformatics indicates active mentorship and research direction, though specific student lists and grant details aren't provided in available materials. She leads interdisciplinary teams in the Institute for Biomedical Informatics and Center for Translational Bioinformatics, developing computational tools for precision medicine implementation and advancing methods for translating genomic discoveries into clinical practice through multi-omics data integration.

• Analysis and Number Theory
• Systems Biology
• Genomics
• +3 more

Jean-Louis is a Professor at the University of Lorraine, affiliated with the Department of Mathematics, Computer Science, and Mechanics under the Analysis and Number Theory team. Current position: Professor Institution: University of Lorraine Department: Mathematics, Computer Science, and Mechanics Research team: Analysis and Number Theory His research spans mathematical analysis applied to biological systems, with a focus on metabolic disorders , genetic variations , and multi-omics modeling . Key areas include vitamin B12 deficiency pathophysiology, microbiome dynamics, and systems medicine approaches for inherited diseases. Computational modeling of metabolic pathways Epigenetic signatures in disease states Integration of genomics with clinical outcomes Mathematical frameworks for personalized medicine Biostatistical analysis of population health data Jean-Louis has published extensively in translational medicine journals , addressing topics like sepsis biomarkers, drug hypersensitivity, and dietary toxicants. His work bridges mathematical rigor with clinical applications in metabolic and genetic disorders.

Laura SmythView profile

Lecturer

• Molecular Epidemiology
• Kidney Disease
• Multi-omics
• +11 more

Laura Smyth is a Lecturer at Queen's University Belfast in the School of Medicine, Dentistry and Biomedical Sciences, specifically within the Centre for Public Health. Her research focuses on the molecular epidemiology of kidney disease and multi-omic risk factors for diseases with a focus on aging populations. She maintains an active research program with numerous publications, projects, and collaborations. Dr. Smyth's research interests span Genetics, Epigenetics, Genomics, Transcriptomics, DNA Methylation, Bioinformatics, Kidney Disease, Renal Disease, Complex Diseases, Chronic Diseases, and Healthy Ageing. Her work contributes to several UN Sustainable Development Goals related to health and wellbeing. She employs multi-omics approaches to understand complex disease mechanisms, particularly in diabetic kidney disease and age-related conditions. Her recent publications demonstrate a strong focus on integrating genetic, epigenetic, and clinical data to improve disease understanding and diagnosis. The research shows particular emphasis on kidney disease in diabetic populations, epigenetic clocks as biomarkers of aging, and polygenic risk scores for age-related conditions like macular degeneration. Scientific Awards: European Diabetic Nephropathy Study Group (EDNSG) Rising Star Award (2023) Finalist, Vice Chancellor's Research Culture Prize (2020) QUB Staff Excellence Award shortlist for Making a Difference (2022) QUB Staff Excellence Award shortlist for Team of the Year (2019) QUB STAR award recipient (2020) Dr. Smyth actively supervises research projects (BMS3112 since 2015) and mentors students in professional skills courses. She leads and contributes to significant research grants including 'Innovative multi-omics approaches to improve detection, diagnosis and prognosis of medical conditions arising from metabolic syndrome' (as PI) and 'Transforming Diabetic Kidney Disease Care' (as CoI). She has secured substantial funding for her research in diabetic kidney disease and aging. She is actively involved in the NICOLA (Northern Ireland COhort for the Longitudinal study of Ageing) research program and collaborates extensively within the kidney research community, including organizing events like 'Kidney Canvas' and 'The Future of Kidney Research is STEAM Powered' public engagement event.

Mudassar IqbalView profile

Senior Lecturer

• Gene Regulatory Networks
• Machine Learning
• Bioinformatics
• +4 more

Mudassar Iqbal holds the position of Senior Lecturer in Health Data Sciences within the Division of Informatics, Imaging & Data Sciences. His research focuses on integrating computational methods with biological data to study gene regulatory networks, circadian rhythms, and cancer biology. He earned a Doctor of Philosophy from the University of Kent in 2009, specializing in machine learning applications for protein-protein interaction prediction. His work contributes to UN Sustainable Development Goals related to health and innovation. Key research interests include spatial transcriptomics, single-cell multiomics analysis, and computational modeling of biological systems. His recent studies explore age-related gene networks in AML, chronotype associations with psoriasis, and algorithm development for integrating imaging and genomic data. He collaborates internationally on projects analyzing gene expression, circadian biology, and cancer genomics. His methods like CellPie and TriTan advance scalable tools for spatial and multi-omics data analysis. Research affiliations include the Digital Futures, Lydia Becker Institute, and Christabel Pankhurst Institute. He has supervised three academic works and published widely in journals like Nucleic Acids Research and eLife , focusing on bioinformatics-driven discoveries in health and disease.

Kevin LinView profile

Assistant Professor

• Single-cell Biology
• Statistical Genetics
• Biostatistics
• +3 more

Kevin Lin is an Assistant Professor in the Department of Biostatistics at the University of Washington, joining in Fall 2023. His research focuses on developing statistical methods for analyzing single-cell data to uncover cellular mechanisms in diseases like Alzheimer’s and immune resistance. He holds a PhD from Carnegie Mellon University and completed postdoctoral training at the University of Pennsylvania. His work bridges computational methods with biological questions, emphasizing matrix factorization, network modeling, and changepoint detection. Education: PhD in Statistics & Data Science (Carnegie Mellon University, 2020) Previous Role: Postdoctoral Researcher at University of Pennsylvania (Wharton Statistics) Research interests include high-dimensional data analysis, statistical genetics, and single-cell RNA-Seq. Notable contributions include methods like Tilted-CCA for multimodal data integration and eSVD-DE for cohort-wide differential expression analysis. His work has been recognized with awards such as the Wikimedia Foundation Research Award (2023). Lin’s lab collaborates on projects involving endolysosomal dysfunction in Alzheimer’s, yeast cell division dynamics, and lineage-aware machine learning. Key achievements include over 20 publications in journals like Nature Biotechnology , PNAS , and Biometrics . He advises on grants related to single-cell technologies and serves on the EDI subcommittee for mental health initiatives at UW. Outside academia, Lin enjoys zumba and cooking.

Dima KozakovView profile

Research Professor

• Macromolecular recognition
• Protein-protein interaction networks
• Drugability of genome
• +3 more

Dima Kozakov is a Research Professor in the Department of Biomedical Engineering at Boston University. He holds a PhD in Biomedical Engineering from Boston University and an M.Sci. in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology. His research focuses on macromolecular recognition, signaling protein-protein interaction networks, genome drugability, and protein design. He is affiliated with the Research Faculty at BU and has contributed to advancements in computational methods for structural biology and drug discovery. His work integrates advanced Fourier and optimization techniques to explore macromolecular interaction landscapes, including structure determination of macromolecular complexes from low-resolution data and validation of protein interaction networks. He develops methods for fragment-based drug design, identifying cryptic binding sites amenable to small molecule inhibition in cancer pathways, and designs peptidic chaperones for crystallization. His contributions include the ClusPro docking server and FTMap pharmacophore identification tools. Recent publications highlight his exploration of ligand interaction landscapes in E. coli, targeted protein degradation mechanisms, and AlphaFold-based structural predictions. He investigates multiomic profiles of immune cells and metabolic reprogramming in disease contexts. His research emphasizes druggable site identification, computational modeling of protein interactions, and advancing machine learning applications in structural biology. Key innovations include MHC-Fine for precise MHC-peptide complex prediction and E-FTMap for fragment-based drug discovery. His work bridges computational methods with experimental validation, addressing challenges in protein structure prediction and drug target identification. Kozakov collaborates on projects like the CASP15 CAPRI experiment, assessing computational methods for protein complex modeling.

Elizabeth NeumannView profile

Assistant Professor

• Neurological diseases
• Molecular imaging
• Mass spectrometry
• +4 more

Elizabeth Neumann is an Assistant Professor in the Department of Chemistry at the University of California, Davis, where her lab focuses on the molecular and cellular architecture of neurological diseases using advanced analytical tools. Her research integrates matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) with multimodal technologies like immunofluorescence, spectroscopy, and transcriptomics to decode complex biological systems. Education: BS in Chemistry from Baylor University, PhD from University of Illinois at Urbana-Champaign under Jonathan Sweedler Postdoctoral training: NIH Postdoctoral Fellow at Vanderbilt University under Richard Caprioli and Jeffrey Spraggins The Neumann lab specializes in developing high-resolution imaging methods for spatial lipidomics and metabolomics, with applications spanning neuroscience, nephrology, and electrochemistry. Their work enables non-destructive molecular profiling of tissues while preserving spatial context. Recent publications emphasize MALDI TIMS IMS for lipid distribution analysis, multimodal integration with CODEX immunofluorescence, and innovative approaches in single-cell chemical analysis. Collaborative efforts include contributions to the NIH Human Biomolecular Atlas Program. Scientific awards: NIH/NIDDK F32, NSF Graduate Research Fellowship, multiple institutional scholarships Leadership: Active in developing community guidelines for antibody-based imaging and FAIR data stewardship Research collaborations span Vanderbilt, University of Illinois, and cross-institutional consortia, with a focus on advancing analytical methods for biomedical discovery.

• Statistical Bioinformatics
• Computational Biology
• Spatial Transcriptomics
• +4 more

Dr. Shila Ghazanfar is an Australian Research Council DECRA Fellow at the University of Sydney, Faculty of Science. She is an expert in statistical and computational analysis of spatial transcriptomics and single-cell RNA-seq data, with a focus on developing bioinformatic approaches for integrating complex biological datasets across various omics modalities. Her educational background includes: Undergraduate studies in statistics and statistical bioinformatics at The University of Sydney PhD in statistics and statistical bioinformatics at The University of Sydney Royal Society Newton International Fellowship at The University of Cambridge under Dr. John Marioni in computational biology Dr. Ghazanfar's research interests center on developing statistical bioinformatic and biomedical data science approaches for meaningful integration of complex and high-dimensional biological datasets. Her multidisciplinary expertise spans statistics, statistical bioinformatics, and computational biology, enabling her to devise strategies to jointly model processes generating diverse data sources. Her work aligns with the University of Sydney Faculty of Science Research Strengths in Complex Systems, Precision and Digital Health, and Data and Decisions. Analysis of her recent publications (2021-2025) reveals strong focus areas including spatial transcriptomics infrastructure development, single-cell data integration methodologies, and applications in cancer research and cardiovascular biology. Key trends show progression from foundational method development to increasingly sophisticated multi-modal integration approaches, with significant emphasis on creating open-source computational tools for the research community. Her scientific recognition includes: Australian Research Council DECRA Fellowship Royal Society Newton International Fellowship Dr. Ghazanfar actively mentors research students, including Angel GUAN working on melanoma immunotherapy research. Her grant portfolio demonstrates substantial research support: 2023: Statistical Bioinformatics for Single Cell, Spatial and Multiomic Biotechnologies (Faculty of Science) 2022: Defining spatiotemporal mechanisms for peripheral nerve regeneration (Partnership Collaboration Award) 2022: Multiscale data integration for single cell spatial genomics (Chan Zuckerberg Initiative) Australian Research Council DECRA for statistical approaches in spatial genomics As a member of the Charles Perkins Centre, Dr. Ghazanfar participates in interdisciplinary research addressing complex health challenges through computational biology approaches, with collaborations spanning multiple domains from basic science to clinical applications.