جست‌وجوی استاد و پژوهشگر

Romain Lopezمشاهده پروفایل

استادیار

• Single-cell omics
• Spatial transcriptomics
• Deep generative models
• +۵ مورد دیگر

Romain Lopez is an Assistant Professor of Computer Science and Biology at New York University, with a joint appointment in the Courant Institute of Mathematical Sciences and the Department of Biology. He will be joining NYU in September 2025, bringing expertise at the intersection of machine learning and computational biology. Prior to joining NYU, he was a Postdoctoral Fellow at Genentech and Stanford Medicine from 2021 to 2025, working with Jonathan Pritchard and Aviv Regev. Dr. Lopez received his educational training at prestigious institutions: PhD in Computer Science (2021) from the University of California, Berkeley, advised by Mike Jordan and Nir Yosef M.S. in Applied Mathematics (2016) from École polytechnique, Palaiseau, France Dr. Lopez's research focuses on developing machine learning methods to understand biological systems at the cellular level. His work bridges computational techniques with biological applications, particularly in single-cell and spatial omics analysis. He pioneered probabilistic approaches for single-cell analysis with scVI and co-developed scvi-tools, now widely adopted tools in the field. His research spans deep generative models, causal inference, perturbation modeling, and representation learning for biological data. His publication record demonstrates a consistent trajectory of innovation in computational biology, with recent work focusing on spatial biology, disentangled representations of cellular perturbations, and causal modeling of cellular responses. He has made significant contributions to the field of single-cell analysis, developing methods that help scientists interpret complex cellular data and predict how cells respond to various perturbations. Dr. Lopez has received numerous honors and awards for his research: Best Paper Award from the ICML Workshop on AI for Science (2024) Best Paper Award Honorable Mention from the AAAI Conference on Artificial Intelligence (2021) Best Student Poster Award from the ICML Workshop on Computational Biology (2019) UC Berkeley EECS Departmental Graduate Fellowship (2016) Carnot Foundation Fellowship (2016) Monahan Foundation Fellowship (2016) French National Defence Medal, Bronze Echelon (2014) At NYU, Dr. Lopez will lead the Biological Machine Learning group, which develops probabilistic machine learning methods to uncover biological mechanisms governing cellular behavior and disease. His lab focuses on creating tools that transform complex cellular data into biological insights, with applications in understanding cancer, immune responses, and fundamental cellular processes. His work has significant implications for precision medicine and drug discovery.

Alexis Battleمشاهده پروفایل

دانشیار

• Genomics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Alexis Battle is an Associate Professor at Johns Hopkins University with appointments in Biomedical Engineering , Computer Science , and Genetic Medicine (secondary). She directs the Malone Center for Engineering in Healthcare and serves as Deputy Director of the Data Science and AI Institute . Educated at Stanford University (PhD in Computer Science, 2013), Battle transitioned to academia after leadership roles at Google. Research Focus: Battle’s work bridges genomics and machine learning , emphasizing the impact of genetic variation on human health. Her lab develops tools like Watershed to predict functional effects of rare variants, aiming to enhance rare disease diagnosis. Key themes include non-coding DNA analysis , personalized genomics , and systems biology , with applications in cardiovascular disease and neurodegenerative disorders . Publications & Awards: Over 60 peer-reviewed articles in journals like Nature , Science , and Genome Biology , with recent emphasis on single-cell transcriptomics , multiomics integration , and telomere biology . Recipient of the President’s Frontier Award (2022), Microsoft Investigator Fellowship (2019), and Searle Scholar (2016). Scientific Awards: 2022 President’s Frontier Award 2019 Microsoft Investigator Fellowship 2019 Johns Hopkins Discovery Award 2017 Johns Hopkins Catalyst Award 2016 Searle Scholar Advising & Funding: Mentors 11 PhD students, 3 undergraduates, and postdoctoral fellows. Her research is funded by NIH, Searle Scholars, and institutional grants. The Battle Lab collaborates on projects like the GTEx Consortium , focusing on gene regulation and clinical genomics .

Qianwen Wangمشاهده پروفایل

استادیار

• Interactive Visualization
• Interpretable Machine Learning
• Human-AI Collaboration
• +۵ مورد دیگر

Qianwen Wang is a tenure-track Assistant Professor in the Computer Science department at the University of Minnesota, Twin Cities. Her research combines interactive visualization with interpretable machine learning to foster intuitive, efficient, and reliable Human-AI collaboration. She actively seeks motivated students, research assistants, and interns to join her dynamic research team at UMN CS. Dr. Wang's research focuses on three primary themes: Human-AI Collaboration, where she designs tools to facilitate Human-AI interaction; Automatic & Intelligent Visualization, where she develops techniques to make visualizations accurately interpreted and easily used; and VIS+(X)AI in Biomed/Healthcare, where she studies how visualization and explainable AI can promote scientific discoveries in biomedicine and healthcare. Her work has made significant contributions to visualization, human-computer interaction, and bioinformatics, with particular applications in biomedical knowledge graphs and single-cell omics analysis. Her recent publications demonstrate a strong trend toward integrating visualization with large language models and graph neural networks for biomedical applications. She has published extensively at top venues including IEEE VIS, ACM CHI, and Nature Medicine, with a focus on making AI systems more interpretable and useful for domain experts, particularly in healthcare contexts. Her work often bridges theoretical advances in visualization with practical applications in genomics and healthcare. Two IEEE VIS Honorable Mention Awards (2022, 2024) Best Paper Award from IMLH@ICML 2021 Two Best Abstract Awards from BioVis ISMB (2021, 2022) HDSI Postdoctoral Research Fund Award Research covered by MIT News and Nature Technology Features Dr. Wang actively contributes to the academic community through service roles including General Chair for ISMB BioVis, VisNotes and Poster Chair for IEEE PacificVis, and Program Committee member for IEEE VIS, ACM CHI, and ACM IUI. Her research has been supported by various grants that enable her team to develop innovative visualization techniques and explore their practical applications in biomedical domains. Her research group maintains an active presence in the visualization and HCI communities, with members participating in major conferences and workshops. The lab focuses on creating tools that bridge the gap between complex AI models and human understanding, with particular emphasis on making these technologies accessible and useful for domain experts in biomedical research.

• Fibrosis Research
• Liver Fibrosis
• Kidney Fibrosis
• +۹ مورد دیگر

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

Laura Eloمشاهده پروفایل

استاد

• Computational Biomedicine
• Medical Bioinformatics
• Data Science
• +۷ مورد دیگر

Laura Elo serves as Professor of Computational Medicine and Head of the Medical Bioinformatics Centre at the University of Turku, Finland. She concurrently holds the position of Research Director at Turku Bioscience Centre and acts as InFLAMES Flagship Contact, driving interdisciplinary biomedical research initiatives. Her academic foundation includes a PhD in Applied Mathematics (2007) and Adjunct Professorship in Biomathematics (2011), establishing her quantitative expertise before transitioning into biomedical applications. Her research program focuses on transforming molecular and clinical datasets through statistical modeling and advanced machine learning . Key thrusts include robust computational tools for proteome/epigenome analysis, AI-driven digital health diagnostics, and computational systems immunology for immune-mediated diseases. This work directly addresses challenges in reproducibility and scalability of high-throughput biotechnology data. Analysis of her recent publications reveals dominant themes in type 1 diabetes biomarker discovery , multi-omics integration , and immune system modeling , with strong emphasis on clinical translation through collaborations with experimental and medical teams. Her scientific recognition includes: JDRF Career Development Award Professor Elo actively trains MSc/PhD students and postdoctoral fellows while leading major research initiatives including ERC grants. Her teaching portfolio spans Bioinformatics Journal Club, AI in Diagnostics, and Systems Biology courses. The Elo Lab (https://elolab.utu.fi) operates as a hub for computational biomedicine, developing open-source tools like CellRomeR while maintaining close ties with Turku Bioscience Centre's experimental facilities for validating computational predictions in immunology and metabolic disease contexts.

• Biomedical Data Privacy
• Genomics
• Machine Learning
• +۳ مورد دیگر

Hyunghoon Cho is an Assistant Professor at Yale School of Medicine in the Department of Biomedical Informatics & Data Science, with a secondary appointment in the Department of Computer Science. He received his PhD in Electrical Engineering and Computer Science from MIT (2019) and MS/BS in Computer Science from Stanford University (2013). His research focuses on computational challenges in biomedical data privacy, single-cell genomics, and network biology. Assistant Professor (Primary): Biomedical Informatics & Data Science Assistant Professor (Secondary): Computer Science Appointments: Yale School of Medicine | Broad Institute (Schmidt Fellow) Research Themes: Privacy-Enhancing Technologies for genomic and health data Scalable AI/ML tools for omics data analysis Structured biological modeling for system-level discovery His work includes secure GWAS, transcriptomic privacy assessment, and sfkit - a federated genomic analysis toolkit. He received the NIH Director's Early Independence Award and leads NSF-funded projects on confidential genome analytics. Awards: NIH Director's Early Independence Award Lab Members: Haris Smajlović (Postdoc), Vincent Angelo (CBB MS), Denis Loginov (Senior Software Engineer), Lucy Zheng (CBB PhD)

Britt Adamsonمشاهده پروفایل

دانشیار

• Stress Response Networks
• DNA Damage Response
• Genome Editing
• +۳ مورد دیگر

Britt Adamson is an Associate Professor in the Department of Molecular Biology and the Lewis-Sigler Institute for Integrative Genomics at Princeton University, where she serves as Director of the Undergraduate Program in Quantitative and Computational Biology. Her lab investigates molecular networks in human cells with focus on stress response mechanisms and genome editing technologies. She received her B.S. in Biology from the Massachusetts Institute of Technology (2005) and Ph.D. in Genetics and Genomics from Harvard University (2012), followed by postdoctoral training at UCSF under Jonathan Weissman supported by a Damon Runyon Cancer Research Foundation Fellowship. Adamson's research centers on how cells organize stress response networks during DNA damage and endoplasmic reticulum stress, developing CRISPR-based functional genomics and single-cell sequencing tools to map molecular behaviors. Her work bridges fundamental cell biology with therapeutic applications in genome editing. Analysis of her 15 most recent publications reveals dominant themes in precision genome editing (prime/base editing optimization) and systematic dissection of DNA repair pathways through combinatorial CRISPR screening. Her lab consistently integrates computational approaches with high-resolution experimental techniques to uncover context-dependent cellular behaviors. Her scientific recognitions include: Damon Runyon Cancer Research Foundation Postdoctoral Fellowship Princeton IP Accelerator Award (2025) STAT Who to Know: 10 Scientists leading a new generation of gene editors (2024) Adamson actively mentors eight graduate students (including alumni Ann Cirincione and Jun Hussmann) and two postdocs, with research funded through institutional awards and collaborative grants. Her lab's technological developments have enabled projects spanning virology, immunology, and developmental biology. The Adamson Lab operates within Princeton's Lewis-Sigler Institute for Integrative Genomics, fostering an interdisciplinary environment that merges cell biology, genomics, and computational science. Current projects focus on improving prime editing efficiency and understanding stress response adaptation in disease contexts.

Anirban Paulمشاهده پروفایل

دانشیار

• Neuroscience
• GABAergic inhibitory circuits
• Interneuron biology
• +۵ مورد دیگر

Anirban Paul is an Associate Professor in the Department of Neuroscience and Experimental Therapeutics at Pennsylvania State University, affiliated with the Penn State Neuroscience Institute. His research focuses on cellular and molecular mechanisms of GABAergic inhibitory circuits, with particular emphasis on interneuron biology and its implications in neurological disorders. Dr. Paul's research spans multiple neuroscience domains, with primary focus on GABAergic inhibitory circuits and interneuron biology. His work investigates how specific neuron subtypes, particularly Chandelier cells and cortical interneurons, contribute to brain function and dysfunction. He has made significant contributions to understanding the role of these cells in schizophrenia, Alzheimer's disease, and other neurological conditions. His research integrates molecular, cellular, and systems-level approaches to uncover fundamental mechanisms of neural circuit assembly, plasticity, and function. Key areas include RNA regulation in neuronal development, transcriptomic subtypes of inhibitory neurons, and cell-type specific vulnerabilities in neurodegenerative diseases. His research portfolio demonstrates consistent productivity with publications spanning from 2003 to 2025, showing an evolving focus from basic molecular neuroscience to translational research in neurological disorders. Recent work emphasizes single-cell analysis techniques and the role of specific interneuron populations in disease mechanisms, particularly in schizophrenia and Alzheimer's disease. His publications appear in high-impact neuroscience journals including Neuron, BMC Biology, and Frontiers in Cellular Neuroscience. Dr. Paul has received the NARSAD Young Investigator Award (2018), recognizing his promising research in neuroscience. His scientific contributions have been supported by multiple competitive grants from prestigious organizations including the National Institute on Aging (NIA) and the Brain and Behavior Research Foundation. He serves as Principal Investigator on multiple active research projects, including two major grants from the National Institute on Aging focused on cell-type specific risk and resilience in Alzheimer's disease and aging (2021-2024 and 2024-2026), as well as previous projects from the Brain and Behavior Research Foundation investigating Chandelier cells in schizophrenia. His research program demonstrates sustained funding and scientific leadership in the field of interneuron biology and its clinical implications.

John Paisleyمشاهده پروفایل

دانشیار

• Machine Learning
• Bayesian Statistics
• Data Science
• +۳ مورد دیگر

John Paisley is an Associate Professor of Electrical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science, and a member of Columbia's Data Science Institute (DSI). He holds a B.S., M.S., and Ph.D. in Electrical and Computer Engineering from Duke University (2004-2010), followed by postdoctoral research in Computer Science at Princeton University and UC Berkeley. His research focuses on Bayesian models, posterior inference techniques for Big Data, and applications in data analysis, recommendation systems, information retrieval, and compressed sensing. He has pioneered methods like Bayesian Gaussian Process ODEs and Double Normalizing Flows, with recent work emphasizing uncertainty quantification in environmental modeling and neuroimaging analysis. His collaborative workflows (e.g., bneR ) address air pollution exposure and PM2.5 concentration uncertainties, combining Bayesian nonparametric ensembles with geospatial data. He has also developed frameworks for neural network interpretability, image denoising, and compressed sensing MRI. Paisley's work bridges statistical theory and applied machine learning, with applications in healthcare, environmental science, and geophysics. His academic contributions include over 50 publications since 2016, spanning topics like deep metric learning, adversarial learning, and variational inference optimization. He maintains an active research group and serves on editorial boards for machine learning and signal processing journals.

Hyun Kooمشاهده پروفایل

استاد

• Biofilms
• Oral Infectious Diseases
• Nanotechnology
• +۳ مورد دیگر

Hyun (Michel) Koo is a Professor at the University of Pennsylvania School of Dental Medicine , with affiliations in the Department of Orthodontics , Division of Community Oral Health , and Division of Pediatric Dentistry . As Co-Founder and Co-Director of the Center for Innovation & Precision Dentistry (CiPD) , he leads interdisciplinary efforts merging bioengineering, nanotechnology, and oral health research. Education : DDS and PhD Research Focus : Biofilms, bacterial-fungal interactions, and nanotechnology for oral disease prevention Leadership : Co-Director of CiPD; key roles in training programs like NIDCR-sponsored R90 and T90/R90 Dr. Koo’s research explores biofilm mechanisms in oral infectious diseases, particularly childhood caries, through engineering methods and microrobotics . His team developed micron-scale robots for automated biofilm eradication and FDA-approved nanoparticles for caries prevention. Collaborations with Penn Engineering, including Dr. Daeyeon Lee and Dr. Kacy Cullen, emphasize translational approaches. The 15 most recent publications highlight his work in nanorobotics , interkingdom biofilms , and precision diagnostics . Articles span 2025–2024 and address topics like adaptive micromotors , biofilm matrix degradation , and single-cell microbial interactions . These emphasize his focus on targeted therapies and biofilm microenvironment engineering . Key Awards : Elected Fellow, American Association for the Advancement of Science (AAAS) IADR Distinguished Scientist Award for innovative dental research Dr. Koo trains next-generation researchers through the CiPD NIDCR T90/R90 Postdoctoral Training Program , mentoring fellows like Smruti Nair (ACE2 Chewing Gum development) and Zhi Ren (K99 awardee). His work intersects with Penn Health-Tech, CT3N , and Penn Institute for Biomedical Informatics , fostering transdisciplinary innovation.

• Developmental Biology
• Neurosciences
• Dopamine Neurons
• +۴ مورد دیگر

Thomas Perlmann is a Professor in Molecular Developmental Biology at the Karolinska Institutet , leading research at the Department of Cell and Molecular Biology and serving as Director of the Stockholm Branch of the Ludwig Institute for Cancer Research. He also holds the position of Secretary General of the Nobel Assembly and Nobel Committee for Physiology or Medicine since 2016. Ph.D. , Karolinska Institutet, 1991 M.Sc. , Stockholm University, 1987 Research Interests : The Perlmann lab investigates the specification and maintenance of dopamine neurons in the central nervous system, with a focus on transcriptional regulation , signaling pathways , and regenerative medicine applications for Parkinson’s disease and other neurodegenerative disorders. His work bridges developmental biology and neuroscience , emphasizing the role of transcription factors in neuronal identity and function. Recent Research Trends : Perlmann’s recent publications highlight the use of single-cell RNA sequencing to dissect dopamine neuron heterogeneity , epigenetic regulation during development, and transcriptomic changes in Parkinson’s disease models. His studies increasingly leverage multiomics and bioinformatics to map neuronal lineage trajectories and gene expression dynamics. Scientific Awards : Royal Medal by HM the King (2025) Nicholson Lecturer, Rockefeller University (2011) Göran Gustafsson Prize in Molecular Biology (1999) Eric K. Fernström Young Investigator Prize (1997) Advising & Collaborations : While no student names are explicitly listed, Perlmann collaborates extensively with researchers such as Malin Parmar , Agnete Kirkeby , and Per Svenningsson on projects related to neuronal development and cell therapy . His lab receives funding from institutions like the Ludwig Institute for Cancer Research . Labs & Teams : The Perlmann Lab at Karolinska Institutet includes researchers like Linda Gillberg , Laura Lahti , and Behzad Yaghmaeian Salmani , who work on mouse models , single-cell transcriptomics , and bioinformatics to study dopamine neuron biology.

Itsik Pe'erمشاهده پروفایل

استاد

• Human Genetics
• Computational Biology
• Genomics
• +۳ مورد دیگر

Itsik Pe'er is a Full Professor and Vice-Chair in the Department of Computer Science at Columbia University's Fu Foundation School of Engineering & Applied Science, and holds a joint appointment as Professor of Systems Biology at the Vagelos College of Physicians and Surgeons. His research focuses on computational methods in human genetics, including genetic variation analysis, disease association studies, and algorithm development for genomic data. He leads the Itsik Pe'er Lab of Computational Genomics, which develops tools like Xplorigin, Germline, and SEACells to address challenges in genomics and medical research. His work spans machine learning applications in healthcare, microbiome analysis, and cancer genomics. Notable contributions include studies on hypertensive disorders in pregnancy, bias correction in predictive models, and the development of non-Euclidean learning libraries like Manify. Pe'er has advised students including Vladimir Vacic, Anat Kreimer, and Arthi Ramachandran, and collaborates on grants addressing genetic epidemiology and computational biology. His lab's location is in the Computer Science Building at Columbia's Morningside Campus.

• Immunology
• Genomics
• Multiomics Analysis
• +۳ مورد دیگر

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

• Bioinformatics
• Genomics
• Transcriptomics
• +۶ مورد دیگر

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.

Matthias Mannمشاهده پروفایل

استاد

• Mass Spectrometry
• Proteomics
• Systems Biology
• +۶ مورد دیگر

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.