جست‌وجوی استاد و پژوهشگر

Brian D. Gregoryمشاهده پروفایل

استاد

• RNA Modifications
• Computational Biology
• Plant Genetics
• +۴ مورد دیگر

Brian D. Gregory is a Professor of Biology at the University of Pennsylvania's School of Arts & Sciences. His research focuses on RNA modifications, computational biology, and plant genetics, particularly studying how RNA modifications regulate gene expression in plants and animals. He holds a Ph.D. from Harvard University (2005) and a B.S.A. from the University of Arizona (2000). Research Interests: RNA epitranscriptomics (e.g., m6A, NAD+ caps) RNA secondary structure and protein interactions Genomic approaches to study plant stress responses Development of high-throughput sequencing tools like PIP-seq Recent Work Highlights: Recent studies include analyzing pathogen-induced RNA modifications' role in plant immunity (Plant Cell 2023), global RNA structure/protein interaction mapping, and epitranscriptomic dynamics in drought tolerance. His lab's work bridges computational methods with molecular genetics to uncover post-transcriptional regulatory mechanisms. Lab & Collaborations: The Gregory Lab uses Arabidopsis thaliana as a primary model organism but also explores animal systems. They collaborate with institutions like Cornell University and have developed protocols published in Current Protocols in Molecular Biology. Teaching: BIOL 4231: Genome Sciences and Genomic Medicine BIOL 6010: Communication for Biologists

Joel Blanchardمشاهده پروفایل

دانشیار

• Neuroscience
• Stem Cell Biology
• Neurodegeneration
• +۱۲ مورد دیگر

Joel Blanchard, PhD, is an Associate Professor in the Departments of Neuroscience and Cell, Developmental & Regenerative Biology at the Icahn School of Medicine at Mount Sinai. He is also an Investigator at the Black Family Stem Cell Institute and the Ronald Loeb Center for Alzheimer’s Disease. His research focuses on developing human brain models using induced pluripotent stem cells to study and treat neurodegenerative diseases. Education: BS, St. Lawrence University MLA, Harvard University PhD, The Scripps Research Institute & MIT Research Interests: Dr. Blanchard’s lab is dedicated to understanding the genetic and environmental vulnerabilities that lead to neurodegeneration. His team develops cutting-edge 3D brain organoids and blood-brain barrier models to investigate diseases like Alzheimer’s, Parkinson’s, and ALS. A major focus is on the role of APOE4 in myelination, cholesterol metabolism, and vascular dysfunction. Scientific Awards: Friedman Brain Institute Scholar Award (2021) ISSCR Merit Award (2019) Glenn Foundation Postdoctoral Fellowship (2018) California Institute for Regenerative Medicine Pre-doctoral Fellowship (2011) Funding & Grants: Dr. Blanchard’s lab is currently funded by NASA, NIH, and the Cure Alzheimer’s Fund. Active projects include the development of miBrain (multicellular integrated brain tissue), blood-brain barrier modeling, and APOE4-mediated mechanisms of neurodegeneration. Lab & Team: The Blanchard Lab is a multidisciplinary team of postdocs, graduate students, and research associates. The lab is actively recruiting postdoctoral fellows and is known for its collaborative, innovative environment focused on translational neuroscience.

Kushal Deyمشاهده پروفایل

استادیار

• Statistical Genetics
• Machine Learning in Genomics
• Functional Genomics
• +۵ مورد دیگر

Kushal Dey serves as an Assistant Professor in the Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center (MSKCC), part of the Graduate School of Medical Sciences in partnership with Weill Cornell Medicine. His research integrates statistical and machine learning approaches with genomic data to understand the regulatory architecture of complex diseases. Dr. Dey's research focuses on developing computational methods that integrate human disease genetics with functional genomics data. His work spans immune-related diseases including Alzheimer's and inflammatory bowel disease, as well as heritable cancers like breast and prostate cancer. His lab develops models to prioritize variants, genes, and cell states for disease using genetic, genomic, and perturbation data, with emphasis on causal directed graphs and benchmarking pipelines informed by disease genetics. His recent publications highlight expertise in GWAS, colocalization, spatial transcriptomics, Perturb-seq, and RNA+ATAC multiome analysis. His work frequently appears in top journals like Nature Genetics, with a focus on single-cell multi-omics approaches to understand disease mechanisms at cellular resolution. Scientific Awards: Josie Robertson Investigator (2023–2028) K99/R00 Pathway to Independence Award (NIH/NHGRI) (2022–2026) NIH/NHGRI Early Stage Investigator R01 (2025-2030) NCI P30 CCSG supplement – 'LLMs in cancer research' (2023-2024) Catalog Working Group Co-chair + Disease Focus Group Lead: IGVF consortium (2023-) Dr. Dey mentors several graduate students through the Weill Cornell Graduate School (WGS), including Thahmina Ali, Pretty Garcia, Karthik Guruvayurappan, Louis Liu, Sarthak Tiwari, Berk Turhan, and Harry Zhang. His lab has received multiple grants including the AWS IMAGINE Grant Children's Health Innovation Award 2024-2025 (as Project Co-lead) and PSRP Developmental Funds Awards (2025: Co-lead). The lab actively collaborates with consortia including ENCODE, ADSP, MorPhiC, and IGVF, maintaining strong ties with Columbia University, Stanford University, and Harvard T.H.Chan School of Public Health. The Kushal Dey Lab is part of the vibrant Tri-Institutional Research campus adjacent to Rockefeller University and Weill Cornell Medical College, offering a collaborative environment focused on computational genomics and disease mechanisms.

Nikolaus Rajewskyمشاهده پروفایل

استاد

• RNA Biology
• Gene Regulation
• Single-Cell Transcriptomics
• +۶ مورد دیگر

Nikolaus Rajewsky is a leading Professor at the Max Delbrück Center for Molecular Medicine (MDC) and Charité – Universitätsmedizin Berlin , where he founded and directs the Berlin Institute for Medical Systems Biology (BIMSB) . His lab integrates experimental (biochemistry, molecular biology) and computational (bioinformatics, physics) approaches to study RNA regulation in gene expression , with applications to developmental biology, regeneration, neurodegenerative diseases, and cancer . Using model systems like C. elegans , planaria, and human brain organoids, his team pioneers cutting-edge methods such as MirDeep , DistMap , and FLAM-seq for RNA analysis. His research focuses on single-cell transcriptomics , spatial RNA sequencing , and circular RNA (circRNA) regulation , revealing novel roles for circRNAs like CDR1as in neuropsychiatric disorders. Recent work includes 3D tumor microenvironment mapping and computational modeling of RNA metabolism in diseases. Scientific Awards : Gottfried Wilhelm Leibniz Prize (2012) EMBO Membership (2010) Honorary PhD, Sapienza University of Rome (2014) Berlin Science Award (2009) His team's recent articles highlight breakthroughs in 3D spatial transcriptomics , circRNA degradation mechanisms , and mitochondrial disease modeling using human brain organoids. The lab actively collaborates with clinical partners across Charité and European institutions, driving the LifeTime initiative for cell-based interceptive medicine.

• Computational Biology
• Single-Cell RNA Sequencing
• Virology
• +۴ مورد دیگر

Fabio Zanini is an Associate Professor at the University of New South Wales (UNSW) , leading a research group focused on computational biology , single-cell approaches , and transcriptomic analysis across diseases like severe dengue , neonatal lung disease , cancer , and marine biology . He previously conducted postdoctoral research at Stanford University (2016-2019) and earned a PhD in Bioinformatics from the Max Planck Institute for Developmental Biology and the University of Tuebingen (2015). Current Affiliation: Group leader, UNSW Previous Training: Postdoc (Stanford), PhD (Max Planck/University of Tuebingen) His research spans single-cell RNA sequencing , computational virology , developmental cell biology , and bioinformatics tool development , with recent work on: Severe dengue progression (viral-host interactions, immune signatures) Lung development (endothelial cell diversity, hyperoxia-induced injury) Cancer genomics (mutant HSC clones, AZA therapy response) Marine biology (plankton transcriptomics, evolutionary analysis) Bioinformatics (HTSeq 2.0, northstar algorithm) Recent scientific awards include grants from the Chan Zuckerberg Initiative ($270,000), NIH R01 (multiple), ARC Discovery Grant , and NHMRC Ideas Grant . Notable contributions include: Northstar - Cell classification algorithm SpectralSeq - Hyperspectral-transcriptomic integration Tabula Muris - Mouse aging atlas He has supervised research into hematopoietic stem cell regulation , lung vascular development , and autophagy in viral infections , with collaborations across Stanford , University of Sydney , and Harvard .

• Genomic Medicine
• Translational Medicine
• Immunogenetics
• +۴ مورد دیگر

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 .

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.

Doron Betelمشاهده پروفایل

استادیار

• Computational Genomics
• Bioinformatics
• Single-cell RNA-seq Analysis
• +۳ مورد دیگر

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.

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.

Maxim Artyomovمشاهده پروفایل

استاد

• Systems Immunology
• Immunometabolism
• Cancer Immunology
• +۲ مورد دیگر

Maxim Artyomov is an Alumni Endowed Professor and Professor of Pathology and Immunology at Washington University School of Medicine , with affiliations to the Institute of Clinical and Translational Sciences (ICTS), Bursky Center for Human Immunology & Immunotherapy Programs (CHiiPs), and Siteman Cancer Center. His research spans Systems Immunology , Immunometabolism , and Cancer Immunology , focusing on integrating epigenetic, transcriptional, and metabolic regulation in immune cells. 2018 promoted to Associate Professor with Tenure 2017 awarded LEAP Inventor Challenge 2018 Unanue Prize for Innovative Research in Immunology Key research contributions include: Discovery of itaconate's anti-inflammatory role (2016) Metabolic reprogramming in macrophage polarization (2015) TREM2's function in microglial metabolic fitness (2017) Development of CORESH gene signature search engine (2025) His high-throughput omics pipelines enable cross-disciplinary analysis of immune responses in tuberculosis, Alzheimer's, and cancer. Notable collaborations include work with Schreiber's lab on immune checkpoint therapy (2018) and ITMO University on systems biology workshops (2017-2018). Awards and grants include the Unanue Prize (2018), LEAP Inventor Challenge (2017), and R01 grant from NIAID (2017). He mentors PhD/MSTP students and co-organizes international systems biology workshops.

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.

Genevera Allenمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Interpretable Machine Learning
• Data Integration
• +۸ مورد دیگر

Genevera Allen is an Associate Professor of Electrical and Computer Engineering, Statistics, and Computer Science at Rice University. She is also an Investigator at the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital and Baylor College of Medicine, and the Founding Director of the Rice D2K Lab, a hub for data science education and real-world problem-solving. Education: Ph.D. in Statistics from Stanford University (2010), B.A. in Statistics from Rice University (2006). Her research develops statistical machine learning tools for reproducible discoveries in neuroscience and bioinformatics, focusing on interpretable models, graphical networks, and data integration. She pioneers methods for high-dimensional and multi-modal data, including convex clustering, sparse PCA, and fairness-aware algorithms. The 15 most recent publications span statistical machine learning theory, neuroscience applications, and bioinformatics. Key trends include graphical model estimation, convex optimization for clustering, latent variable adjustment, and ethical AI frameworks. Applications emphasize neuroimaging and single-cell genomics. Scientific Awards: NSF Career Award (2016), Duncan Achievement Award (2021), Curriculum Innovation Award (2020), Research and Teaching Excellence Award (2017), Forbes 30 Under 30 (2014), elected Fellow ASA (2022), Member ISI (2021). Dr. Allen leads the Rice D2K Lab, which connects students with industry and academic data science projects. She serves as Editor for the Journal of Machine Learning Research and Springer Texts in Statistics . Her teaching innovations include client-sponsored capstone programs and courses on machine learning.

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.