جست‌وجوی استاد و پژوهشگر

Brandon K. Hadlandمشاهده پروفایل

دانشیار

• Hematopoietic stem cell development
• Notch signaling
• Hematopoietic niche
• +۵ مورد دیگر

Dr. Brandon K. Hadland is an Associate Professor at the University of Washington School of Medicine in Pediatrics and Fred Hutchinson Cancer Center's Translational Science and Therapeutics Division, with memberships in the Immunotherapy and Translational Data Science Integrated Research Centers. He serves as an Attending Physician at Seattle Children's Hospital for Pediatric Hematology/Oncology and Bone Marrow Transplant. Education: BS in Chemistry, Harvey Mudd College (1998) MD and PhD in Molecular Cell Biology, Washington University School of Medicine (2006) Pediatrics Residency and Internship, Seattle Children's/University of Washington (2006-2009) Pediatric Hematology/Oncology Fellowship, Seattle Children's/University of Washington/Fred Hutch (2009-2012) His research centers on embryonic hematopoietic stem cell (HSC) development, investigating Notch signaling pathways, vascular microenvironments in the AGM region and fetal liver, and engineering in vitro platforms to model blood formation. He employs single-cell functional and molecular techniques to characterize niche interactions and transcriptional programs driving HSC emergence. Recent publications demonstrate trends in applying single-cell transcriptomics to define HSC-competent hemogenic endothelium and develop stromal-free engineered niches, bridging developmental biology with therapeutic applications for blood disorders and leukemia. Dr. Hadland leads collaborative research across Fred Hutch and UW, partnering with experts in computational genomics (Dr. Cole Trapnell), stem cell engineering (Drs. Sergei Doulatov and Ying Zheng), and pediatric oncology (Drs. Soheil Meshinchi and Irv Bernstein) to advance cellular therapies and leukemia prevention strategies.

Miler T. Leeمشاهده پروفایل

دانشیار

• Gene Regulation
• Developmental Biology
• Computational Genomics
• +۹ مورد دیگر

Miler T. Lee is an Associate Professor at the University of Pittsburgh , focusing on gene regulation during early embryonic development through high-throughput experimental and computational genomics. He earned his Ph.D. in Genomics and Computational Biology in 2009 from the University of Pennsylvania under Dr. Junhyong Kim, followed by postdoctoral work with Dr. Antonio Giraldez at Yale University. Joining the university in 2016, his research spans maternal-to-zygotic transition (MZT), RNA stability, pluripotency networks, and evolutionary developmental biology, utilizing model organisms like zebrafish, Xenopus, and Hydractinia symbiolongicarpus. Key Research Themes: Maternally inherited RNA dynamics during embryogenesis Mechanisms of RNA degradation and transcriptome remodeling Evolution of pluripotency networks in hybrid species Role of zinc signaling in fertilization barriers Computational tools for RNA regulation and sensing Scientific Awards: Pan-American Society for Evolutionary Developmental Biology Junior Faculty Award (2024) Outstanding New Investigator – International Xenopus Board (2023) Basil O'Connor Scholar – March of Dimes (2017-2019) Recent publications highlight his work on enhancer classification, RNA degradation mechanisms, and cross-species MZT comparisons. His lab develops innovative methods like RESA for regulatory sequence analysis and studies evolutionary divergence in RNA localization patterns. While the articles span computational and experimental approaches, they consistently address RNA's role in cellular identity, developmental timing, and evolutionary adaptation. Applications include understanding pluripotency, designing RNA biosensors, and elucidating fertilization barriers. Prospective Ph.D. students are encouraged to contact him for opportunities in gene regulation, development, evo-devo, and computational genomics.

Michael Economoمشاهده پروفایل

استادیار

• Systems Neuroscience
• Motor Control
• Neural Circuits
• +۳ مورد دیگر

Michael N. Economo, PhD, is an Assistant Professor in the Department of Biomedical Engineering at Boston University. His research focuses on neural circuits controlling movement, leveraging cutting-edge optical, electrophysiological, and genetic tools. He holds affiliations with Neuroscience & Neuroengineering and Photonics & Optical Systems programs. Education: PhD in Biomedical Engineering from Boston University, B.S. Biomedical Engineering and B.A. Mathematics from Duke University. Research Interests: Systems neuroscience, motor control, long-range neural circuits, computational neuroscience, neurotechnology. His lab investigates how neural circuits across brain regions coordinate movement using advanced techniques like optogenetics, in vivo imaging, and transcriptomics. Key Projects: Orofacial motor control, voltage imaging with TICO microscopy, neural circuit dissection. Technologies: Neuropixels probes, fluorescent voltage indicators, single-cell RNA sequencing. Notable Awards: NSF CAREER (2023), Scialog Fellow (2023), Whitehall Foundation Young Investigator Award (2021). Advising & Grants: Supervises graduate students (e.g., Munib Hasnain, Jackie Birnbaum) and postdocs (e.g., Vicky Moya, Yujin Han). Lab members focus on motor planning, neuromodulation, and neural dynamics. Labs/Teams: The Economo Lab collaborates on neurotechnology development and integrates interdisciplinary approaches from engineering and biology. Affiliated with BU’s Graduate Program in Neuroscience.

• Neonatology
• Immunology
• Pediatric Gastroenterology
• +۳ مورد دیگر

Dr. Oluwabunmi (Bunmi) Olaloye is an Assistant Professor of Pediatrics in the Division of Neonatology at Yale School of Medicine. She holds appointments in Neonatal-Perinatal Medicine and is affiliated with the Janeway Society. Her academic background includes an MD from Rutgers New Jersey Medical School, pediatrics residency at University of Texas Medical Branch, and neonatology fellowship at University of Pittsburgh Medical Center. Dr. Olaloye's research focuses on immune dysfunction underlying neonatal intestinal diseases such as necrotizing enterocolitis (NEC) and spontaneous intestinal perforation (SIP). Using cutting-edge techniques like single-cell RNA sequencing and mass cytometry, her work identifies biomarkers and therapeutic targets to improve outcomes for premature infants. Key research areas include fetal immune system maturation, placental immune interactions, and gestational age-specific inflammatory responses. Her publication record spans 12 peer-reviewed articles between 2019-2025, emphasizing translational immunology and neonatal pathophysiology. Notable contributions include defining immune cell trajectories in preterm infants and developing gating guidelines for high-dimensional cytometry data. Current projects involve constructing immune cell atlases across human lifespans and investigating nutritional interventions for intestinal disorders. Laboratory affiliations include the Laboratory for Surgery, Obstetrics & Gynecology where she explores neonatal mucosal immunity. Her work integrates clinical observations with systems immunology approaches to address critical gaps in understanding prematurity-associated gastrointestinal pathologies.

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

Ben Raphaelمشاهده پروفایل

استاد

• Bioinformatics
• Computational Biology
• Cancer Genomics
• +۶ مورد دیگر

Ben Raphael is a Professor in the Department of Computer Science at Princeton University, with affiliations at the Lewis-Sigler Institute for Integrative Genomics, Omenn-Darling Bioengineering Institute, and Center for Statistics and Machine Learning. He is also an Affiliate Faculty member at the Rutgers Cancer Institute of New Jersey, Irving Institute for Cancer Dynamics at Columbia University, and New York Genome Center. His research focuses on computational methods for analyzing large-scale biological data, emphasizing cancer evolution, network/pathway analysis, and structural variation in genomes. Research Trends: His recent work spans cancer lineage trees, spatial transcriptomics, optimal transport for developmental models, and network analysis of mutations. Articles highlight applications in prostate cancer, pancreatic cancer, and single-cell genomics. Scientific Awards: 2024 ACM Fellow 2023 RECOMB Test of Time Award 2022 RECOMB Test of Time Runner-Up 2021 ISCB Innovator Award 2021 RECOMB Best Paper Runner-Up 2020 ISCB Fellow 2020 AACR Team Science Award 2011 NSF CAREER Award 2013 RECOMB Best Paper 2010-2012 Sloan Research Fellowship Advising: He has mentored numerous Ph.D. students and postdoctoral fellows, many of whom have transitioned to academic and industry roles. Current advisees include Uthsav Chitra, Gillian Chu, and Alexander Strzalkowski. Labs & Teams: Raphael leads the Raphael Lab at Princeton, developing tools like HotNet2, CHISEL, and HATCHet for cancer genomics and network analysis.

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.

Jenn Brophyمشاهده پروفایل

استادیار

• Genetic Engineering
• Plant Science
• Synthetic Biology
• +۳ مورد دیگر

Jenn Brophy is an Assistant Professor of Bioengineering at Stanford University, developing technologies for genetic engineering of plants and microbes to address environmental stress resilience and agricultural sustainability. Her lab focuses on synthetic genetic circuits for plant root reprogramming and stress response optimization. B.S. in Bioengineering, UC Berkeley (2010) Ph.D. in Biological Engineering, MIT (2016) Postdoctoral Fellow, Stanford University (Biology) Research spans synthetic biology, plant genetics, and microbiome engineering, emphasizing climate adaptation and sustainable biotechnology. Current projects include: Plant-microbe interaction engineering Stress-responsive biosensors High-throughput genetic tool development Plant cell atlas integration Sustainable laboratory practices Her recent publications highlight advances in recombinase circuits, root architecture engineering, and plant cell mapping, with applications in climate resilience and microbiome design. Collaborators include José Dinneny (Stanford) in plant synthetic biology research.

Gerald Quonمشاهده پروفایل

دانشیار

• Computational Biology
• Single Cell Genomics
• Epigenomics
• +۵ مورد دیگر

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

• Computational Systems Biology
• Multi-omics Data Analysis
• Single-cell Sequencing
• +۵ مورد دیگر

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

• Bayesian Statistics
• Machine Learning
• Health Informatics
• +۳ مورد دیگر

Sara Wade is a Lecturer in Statistics and Machine Learning at the University of Edinburgh , within the School of Mathematics . Her research focuses on Bayesian statistics, machine learning, and their applications in health sciences, particularly in dementia diagnosis and predictive modeling. She holds a PhD from the University of Milan and has held academic positions at the University of Cambridge and University of Warwick before joining Edinburgh. She teaches a popular Machine Learning and Python course for Master’s and final-year undergraduate students, attracting nearly 200 enrollments annually. Her work integrates Bayesian methods with modern machine learning, emphasizing interdisciplinary applications such as scalar-on-image regression and biomarker analysis. Notable contributions include developing hierarchical Dirichlet processes for clustering and uncertainty quantification in RNA velocity studies. She secured a Royal Society of Edinburgh grant for her dementia research project, which aims to improve early diagnosis through statistical modeling. Education: PhD in Statistics, University of Milan Bachelor’s in Mathematics, University of Maryland Wade advocates for diversity in STEM, actively participating in the Women in Machine Learning community. Her research bridges statistical rigor and computational tools, fostering collaborations across academia and healthcare sectors.

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.

• 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 .

Emre Seferمشاهده پروفایل

دانشیار

• Bioinformatics
• Machine Learning
• Financial Networks
• +۳ مورد دیگر

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

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