جست‌وجوی استاد و پژوهشگر

Olga G. Troyanskayaمشاهده پروفایل

استاد

• Bioinformatics
• Computational Genomics
• Machine Learning in Biology
• +۶ مورد دیگر

Olga G. Troyanskaya is a Professor of Computer Science and the Lewis-Sigler Institute for Integrative Genomics at Princeton University. She serves as Deputy Director for Genomics at the Simons Center for Data Analysis, Simons Foundation, NYC. Her research focuses on computational biology, integrating diverse high-throughput genomic datasets to model molecular pathways in health and disease. Professor of Computer Science and Lewis-Sigler Institute for Integrative Genomics Deputy Director for Genomics, Simons Center for Data Analysis Research Interests: Troyanskaya’s work addresses challenges in bioinformatics, including algorithm development for gene expression analysis, regulatory network modeling, and disease mechanism interpretation. She combines computational methods with experimental validation using S. cerevisiae as a model organism. Scientific Trends: Recent publications emphasize single-cell multiomics, deep learning for transcriptional regulation, cancer immunotherapy design, and epigenomic analysis of immune responses. Key themes include computational modeling of genetic networks, disease-specific pathway analysis, and high-resolution omics frameworks. Collaborative roles in autism, Alzheimer’s, kidney disease, and cancer research Developed tools like HumanBase for data-driven predictions

Guanghao Qiمشاهده پروفایل

استادیار

• Statistical Genetics
• Genomics
• Single-cell RNA Sequencing Analysis
• +۳ مورد دیگر

Guanghao Qi is an Assistant Professor in the Department of Biostatistics at the University of Washington. His research focuses on developing statistical and machine learning methods for multi-omics approaches in genetic studies, particularly integrating single-cell RNA-seq, GWAS, and functional genomic data. Key areas include single-cell eQTL analysis, Mendelian randomization, and multi-trait genetic association analyses. Education: PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health (2020), BS in Mathematics from Fudan University (2015). Research interests emphasize high-dimensional data analysis, allele-specific expression in single cells, and causal inference using genetic variants. Notable achievements include a 2025 NIH K01 award for developing methods to integrate single-cell eQTL and GWAS data, and the development of the TWiST method for single-cell transcriptome-wide association studies. Recent work highlights advancements in computational tools like SURGE for context-specific genetic regulation analysis, and evaluations of Mendelian randomization methods in studies of type 2 diabetes and cardiovascular disease. His work often bridges computational biology and statistical theory to address challenges in interpreting large-scale genomic datasets. Awards: NIH K01 Award (2025) Key Contributions: TWiST method (2025), SURGE framework (2024), HIPO power optimization (2018) Labs/Teams: Active collaborations in genomic epidemiology and statistical genetics, with a focus on single-cell multi-omics integration and causal inference methodologies.

Martin Peiferمشاهده پروفایل

استاد

• Computational Cancer Genomics
• Tumor Evolution
• Genome Instability
• +۵ مورد دیگر

Professor Martin Peifer is a computational cancer genomics researcher at the University of Cologne, where he leads the Department of Translational Genomics. He serves as Principal Investigator of the Peifer Lab, which focuses on developing computational methods to analyze cancer genome sequencing data. His work is deeply integrated with the Center for Data and Simulation Science and he is an active member of the International Cancer Genome Consortium and the Pan-Cancer Analysis of Whole Genomes project. Peifer's research interests center on computational approaches to understanding cancer biology, with particular emphasis on tumor evolution and genome instability mechanisms. His lab develops methods to analyze somatic genome alterations including point mutations, copy number changes, and rearrangements. They also create computational tools for integrative genome analyses, tumor evolution reconstruction, and single-cell sequencing data analysis (both RNA and DNA). His interdisciplinary team applies high-performance computing and machine learning to interpret complex cancer sequencing data, aiming to better understand tumorigenesis, clonal evolution, and therapy resistance. Analysis of Peifer's extensive publication record reveals a strong focus on neuroblastoma and lung cancer genomics, with particular attention to tumor evolution patterns and genomic instability mechanisms. His work spans multiple cancer types but maintains consistent themes of computational methodology development and application to understand cancer progression and treatment resistance. The publications demonstrate increasing sophistication in analyzing intra-tumor heterogeneity and clonal dynamics over time. Peifer leads an active research group including postdoctoral fellows (Joel Kaufmann, Dr. Stephanie Pabel, Agnieszka Rumińska) and PhD students (Magdalena Seiffert, Justinas Valiulis). His lab is involved in the Collaborative Research Center 1399 focused on Mechanisms of Drug Sensitivity and Resistance in Small Cell Lung Cancer, indicating significant grant funding and collaborative research efforts. The Peifer Lab operates at the intersection of computational biology and cancer research, maintaining an interdisciplinary approach that combines bioinformatics, machine learning, and high-performance computing to address complex questions in cancer genomics. Their work has significant implications for understanding cancer evolution and developing more effective treatment strategies.

Valentina Boevaمشاهده پروفایل

استادیار

• Machine Learning
• Bioinformatics
• Genomics
• +۳ مورد دیگر

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.

Zhandong Liuمشاهده پروفایل

دانشیار

• Bioinformatics
• Big Data analysis
• Machine Learning
• +۵ مورد دیگر

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

Ralf Zimmerمشاهده پروفایل

استاد

• Bioinformatics
• Systems Biology
• Machine Learning
• +۵ مورد دیگر

Ralf Zimmer is a Full Professor for Practical Informatics and Bioinformatics at Ludwig Maximilian University of Munich (LMU) since 2001, affiliated with the Department of Informatics in the Faculty of Mathematica, Informatics and Statistics. He concurrently serves as Head of Section III and Head of the Research Group Network Regulation and Modeling / Machine Learning at the Leibniz Institute for Food Systems Biology at TUM (Leibniz-LSB@TUM) in Freising, Germany. His academic foundation includes a Diploma with distinction in Computer Science, Applied Mathematics and Operations Research from the University of Bonn (1981-1986), followed by a summa cum laude Doctorate in Computer Science and Applied Mathematics from CAU Kiel in 1990, where he received dual honors: the CAU Dissertation Award and Best Dissertation Award. Zimmer's research pioneers the integration of bioinformatics, systems biology, and machine learning to decode molecular food-consumer interactions. His group develops causal system models for biological networks, validated through in silico simulations and multi-omics perturbation experiments (transcriptomics/proteomics). Core methodologies include network regulation modeling, algorithmic bioinformatics, and database construction linking food compounds to biochemical networks and cellular phenotypes, with translational goals for food and biotech innovation. His 14 most recent publications (2012-2024) reveal dominant trends in multi-omics immunology and cardiovascular research, featuring computational innovations for high-throughput data analysis. Key themes include host-pathogen dynamics (viral infections), inflammatory disease mechanisms (atherosclerosis), and methodological advances in proteomics/transcriptomics, consistently bridging fundamental bioinformatics with clinical applications. Major scientific recognitions include: CAU Dissertation Award and Best Dissertation Award (1990) Director of LMU's Informatics Department (2010-2012) DFG Review Board membership for biomedical foundations (2008-2016) Leadership of the DFG Bioinformatics Munich Center (2001-2008) Academic Senate election at LMU München (2011) Zimmer directs LMU/TUM's joint B.Sc./M.Sc. bioinformatics programs since 2001 as founding architect of the DFG-funded Bioinformatics Munich initiative. His educational leadership spans spokesperson roles for international training groups (IRTG RECESS), collaborative research centers (SFB1123 Atherosclerosis), and elite programs (Data Science, Munich Center for Machine Learning). Grant stewardship includes directing the DFG Bioinformatics Munich Center and shaping national funding policy via the DFG review board. At Leibniz-LSB@TUM, his research group pioneers databases connecting food compounds to cellular phenotypes through molecular networks, collaborating with Munich universities, clinics, and biotech partners to develop high-throughput sequencing/proteomics applications for future food and health innovations.

Li Songمشاهده پروفایل

استادیار

• Computational Immunology
• Bioinformatics
• Genomics
• +۳ مورد دیگر

Li Song is an Assistant Professor at the Department of Biomedical Data Science within the Geisel School of Medicine at Dartmouth College . He earned his Ph.D. in Computer Science (2018) and a Master's in Applied Mathematics and Statistics from Johns Hopkins University , advised by Liliana Florea. He completed postdoctoral training in the labs of X. Shirley Liu and Heng Li at the Data Science Department of Dana-Farber Cancer Institute . Education Ph.D. in Computer Science (Johns Hopkins University, 2018) M.S. in Applied Mathematics and Statistics (Johns Hopkins University) Research Focus : Designing algorithms for next-generation sequencing data analysis in immunology and microbiology. Key projects include: TRUST4 - De novo assembly of T-cell/B-cell receptors from RNA-seq T1K - Genotyping of polymorphic HLA/KIR genes Centrifuger - Compressed microbial genome classification Chromap - Ultrafast chromatin profiling data processing Recent Publications demonstrate expertise in computational immunogenetics, metagenomic analysis, and chromatin profiling workflows. The 2024 Genome Biology paper on Centrifuger received a Best Paper Award at RECOMB2024 . Training & Recruitment : The Song Lab actively seeks postdocs and graduate students through the Quantitative Biomedical Sciences program and Department of Computer Science at Dartmouth.

Andrew Godwinمشاهده پروفایل

استاد

• Molecular Oncology
• Precision Medicine
• Biomarker Discovery
• +۳ مورد دیگر

Andrew Godwin is a Professor at the University of Kansas Medical Center , where he serves as the Chancellor’s Distinguished Chair in Biomedical Sciences and Director of Molecular Oncology in the Department of Pathology and Laboratory Medicine. He is also the Deputy Director of the NCI-designated University of Kansas Cancer Center and the Founding Director of the Kansas Institute for Precision Medicine and Biospecimen Shared Resource . Dr. Godwin is a leader in translational research and precision medicine , with a focus on molecular oncology , biomarker discovery , and genomic diagnostics . His work bridges basic and clinical science to improve cancer patient care, particularly in ovarian cancer , Ewing sarcoma , and breast cancer . He has contributed over 230 ovarian cancer-related publications and pioneered studies linking the PI3K/AKT pathway to cancer treatment targets. His research program encompasses liquid biopsies using extracellular vesicles , molecular therapeutics , companion diagnostics , and clinical trial validation . He leads the Biomarker Discovery Laboratory and has secured over $250M in extramural funding , including a $11.4M NIH grant for precision medicine initiatives. His team has developed CELLSEARCH® , the first FDA-cleared test for circulating tumor cells. Notable awards include the Dolph C. Simons, Sr. Higuchi Award (2020), Outstanding Mentorship in Pathology Award (2024), and multiple mentoring accolades from KU. He has mentored over 150 trainees across career stages and leads a multidisciplinary lab with expertise in genomics , proteomics , and bioengineering . Academic Roles: Chancellor’s Distinguished Chair in Biomedical Sciences Director, Molecular Oncology, Pathology and Laboratory Medicine Deputy Director, KU Cancer Center Founding Director, Kansas Institute for Precision Medicine Adjunct Professor, Bioengineering Program, University of Kansas Scientific Awards: KUMC Achievement Award for mentoring postdocs (2014) Chancellor’s Club Award for Research (2018) Dolph C. Simons, Sr. Higuchi Award (2020) KU Excellence in Mentoring Award (2021) Outstanding Mentorship in Pathology (2024) Key Research Themes: Extracellular vesicles as liquid biopsy tools Molecular mechanisms of sarcoma and breast cancer Genomic diagnostics and precision oncology Clinical trial biomarker validation Biospecimen repository leadership

Kirsten Jungمشاهده پروفایل

استاد

• Bacterial Signal Transduction
• Stress Response Mechanisms
• Membrane Protein Structure-Function Analysis
• +۳ مورد دیگر

Prof. Dr. Kirsten Jung is a faculty member at the Department of Microbiology , Faculty of Biology , Ludwig Maximilian University of Munich . Her research focuses on bacterial signal transduction, stress response mechanisms, and systems biology approaches to understand microbial regulatory networks. Key research areas include stress-dependent gene expression in bacterial populations Structural and functional analysis of membrane-integrated receptors Metabolism-based chemical communication in bacteria Integration of experimental and computational systems biology Recent publications highlight her lab's work on Escherichia coli epitranscriptomic modifications under heat stress, m 5 C rRNA dynamics, and the role of RNA methylation in host-pathogen interactions. Collaborative studies address bacterial acid stress responses and their implications for antibiotic tolerance. Her interdisciplinary work bridges microbiology with ecological studies, as evidenced by research on biodiversity conservation in forest and urban ecosystems. Publications also demonstrate expertise in advanced imaging techniques (e.g., arterial spin labeling for glioma analysis) and bioinformatics approaches. Current advisees include Gloria Gessinger and Tania P. Gonzalez-Terrazas . She can be contacted at jung@lmu.de .

Hajk-Georg Drostمشاهده پروفایل

مدرس ارشد

• Computational Biology
• Machine Learning
• Bioinformatics
• +۴ مورد دیگر

Dr. Hajk-Georg Drost is a Senior Lecturer and Principal Investigator in the Division of Computational Biology at the University of Dundee's School of Life Sciences. He leads the Digital Biology Group, focusing on integrating machine learning and high-performance computing with biological research to advance healthcare innovation. Previously, he established a Computational Biology group at the Max Planck Institute for Biology Tübingen (2019-2024) and conducted postdoctoral research at the University of Cambridge's Sainsbury Laboratory. His research explores: Evolutionary transcriptomics and phylotranscriptomic patterns across species Machine learning applications in genomics and proteomics Development of bioinformatics tools (DIAMOND, myTAI) for tree-of-life scale analyses Gene regulatory networks and transposable element dynamics His publications demonstrate a consistent focus on evolutionary constraints in development, with recent work expanding into single-cell resolution analyses of developmental diseases. Awards include: Royal Society Wolfson Fellowship (2024) Fellow, Cambridge Philosophical Society Postdoctoral Affiliate, Trinity College Cambridge He currently supervises PhD students including Stefan Manolache and leads projects funded by the Royal Society and others, focusing on protein alignment infrastructure and developmental disease research. His lab develops open-source software for genomic analyses and maintains active collaborations across Europe.

Jonathan Weissmanمشاهده پروفایل

استاد

• Protein Folding
• Systems Biology
• Genomics
• +۶ مورد دیگر

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.

Yuri Pritykinمشاهده پروفایل

استادیار

• Computational Biology
• Genomics
• Machine Learning
• +۵ مورد دیگر

Yuri Pritykin is an Assistant Professor at Princeton University, affiliated with the Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science. He also holds cross-appointments in Molecular Biology, the Omenn-Darling Bioengineering Institute, and the Center for Statistics and Machine Learning. Pritykin earned his Ph.D. in Computer Science from Princeton University (2014), alongside MSc and Ph.D. in Mathematics from Lomonosov Moscow State University. His research lies at the intersection of applied statistics, machine learning, and functional genomics, focusing on integrative analysis of multi-dimensional biological data. Research Interests: Decoding regulatory genomics in immune cells, CRISPR tool development (GuideScan2), single-cell and spatial multi-omics for immunology and cancer, post-transcriptional regulation, and cell-cell interaction profiling (uLIPSTIC technology). Awards: NSF CAREER Award (2023) NIH New Innovator Award (2022) Recognized by Princeton Ludwig Institute, Rutgers Cancer Institute, and AACR Teaching: Courses in computational biology, genomics, and machine learning applications in life sciences. His lab actively collaborates with immunologists and genomicists, seeking interdisciplinary scientists at all career stages.

Mandi de Mestreمشاهده پروفایل

استاد

• Reproductive Biology
• Placental Development
• Equine Medicine
• +۵ مورد دیگر

Dr. Mandi de Mestre is a Professor in the Department of Biomedical Sciences at Cornell University's College of Veterinary Medicine. As Director of the Baker Institute for Animal Health and holder of the Dorothy Havemeyer McConville Professorship of Equine Medicine, her research focuses on equine pregnancy biology, placental development, and fetal health. Director, Baker Institute for Animal Health Dorothy Havemeyer McConville Professor of Equine Medicine Affiliated with Cornell's College of Veterinary Medicine Her laboratory investigates: Molecular mechanisms of placental development Genetic causes of early pregnancy loss Congenital musculoskeletal disorders in Thoroughbreds Non-invasive diagnostics for fetal health Comparative studies linking equine and human pregnancy pathologies Recent research trends show strong emphasis on genomic stability, immune interactions during implantation, and epidemiological factors affecting equine pregnancies. Collaborations with institutions like the University of Nottingham and Royal Veterinary College UK highlight her interdisciplinary approach. The Equine Pregnancy Laboratory employs molecular biology, immunology, and genetics to address critical gaps in placental biology, with implications for both veterinary and human medicine.

Michael Skinniderمشاهده پروفایل

استادیار

• Computational Biology
• Machine Learning
• Metabolomics
• +۴ مورد دیگر

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.

Lara Urbanمشاهده پروفایل

استاد پژوهشی

• Molecular Biology
• Artificial Intelligence
• Environmental Health
• +۳ مورد دیگر

Dr. Lara Urban is a Principal Investigator at Helmholtz Munich and Helmholtz AI, and a TUM Junior Fellow at the Technical University of Munich's Life Sciences School. Her research integrates genomics and artificial intelligence to address One Health challenges, focusing on environmental health, biodiversity conservation, and pathogen surveillance. She holds a PhD from EMBL-EBI and the University of Cambridge (2019) and Master's degrees from Julius-Maximilians-University of Würzburg (2015). Her work leverages portable genomic technologies for real-time data analysis in clinical, environmental, and conservation contexts. Key areas include studying bioaerosols, pathogen detection, and genomic diversity in endangered species like the kākāpō. Awards include the Young Scientist of the Year 2022 and Humboldt Research Fellowship. Her team has published on nanopore sequencing applications, antibiotic resistance, and conservation genomics. Collaborations span institutions like the University of Zurich and ETH Zurich, funded by EU Horizon Europe, BMBF, and Helmholtz grants. Current initiatives include fieldwork in Chile and advancing genomic equity through democratized tools.