جست‌وجوی استاد و پژوهشگر

Rafet Al-Tobaseiمشاهده پروفایل

دانشیار

• Bioinformatics
• Aquaculture Genetics
• RNA Sequencing
• +۵ مورد دیگر

Dr. Rafet Al-Tobasei serves as an Associate Professor in the Department of Computer Science at Middle Tennessee State University (MTSU), where he applies computational methodologies to solve complex biological problems in aquaculture species. His research bridges computer science and genomics to address critical challenges in fisheries science and genetic improvement programs. His academic credentials include: Ph.D. in Computer Science, Middle Tennessee State University (2017) M.A. in Computer Science, Middle Tennessee State University (2011) M.S. in Computer Science, Middle Tennessee State University (2011) B.S. in Computer Science, Tennessee State University (2007) Dr. Al-Tobasei's research program focuses on developing and implementing bioinformatics tools for aquaculture genomics, with particular emphasis on rainbow trout and Nile tilapia. His expertise spans RNA sequencing analysis, Genome-Wide Association Studies (GWAS), Single Nucleotide Polymorphism (SNP) discovery, Long non-coding RNA (lncRNA) characterization, DNA methylation profiling, and Chromatin Immunoprecipitation Sequencing (ChIP-Seq) data interpretation. He investigates molecular mechanisms underlying economically important traits including muscle growth, fillet quality, disease resistance, and stress response. Analysis of his publication record reveals consistent thematic progression in aquaculture genomics: early work established foundational genomic resources for rainbow trout, followed by sophisticated GWAS applications for trait mapping, and recent integration of multi-omics approaches (transcriptomics, epigenomics, and lipidomics). His research increasingly emphasizes practical breeding applications, with significant contributions to genomic selection methodologies using reduced-density SNP panels and advanced statistical models. While specific awards and grants aren't documented in the source material, his extensive publication record in high-impact journals like BMC Genomics and Scientific Reports demonstrates substantial scholarly impact. His collaborative network spans multiple institutions and includes frequent co-authorship with leading aquaculture geneticists, indicating active participation in the research community and likely involvement in mentoring graduate students through research projects.

Charles Burantمشاهده پروفایل

استاد

• Obesity
• Type 2 Diabetes
• Insulin Resistance
• +۵ مورد دیگر

Charles Burant is Professor of Nutritional Sciences at the University of Michigan School of Public Health and holds a professorship in the Section of Metabolism, Endocrinology, and Diabetes within the Medical School's Department of Internal Medicine. He holds the endowed Dr. Robert C. and Veronica Atkins Professor of Metabolism chair. His clinical practice focuses on obesity, type 2 diabetes, and related metabolic disorders at the WK Kellogg Eye Center, Brehm Tower in Ann Arbor. Dr. Burant's research integrates multi-omic approaches (genomics, transcriptomics, proteomics, metabolomics) with clinical and behavioral phenotypes to unravel mechanisms of obesity, insulin resistance, and diabetes. His laboratory investigates pancreatic beta-cell metabolism, insulin secretion dynamics, and adult pancreatic progenitor cells for beta-cell regeneration. As director of the Michigan Metabolomics and Obesity Center (MMOC), he oversees the NIH-funded Nutrition Obesity Research Center and Michigan Regional Comprehensive Metabolomics Resource Core, providing critical infrastructure for metabolic disease research. The MMOC also coordinates the Investigational Weight Management Clinic, which combines clinical care with phenotypic tracking to study diabetes prevention and weight regain mechanisms in formerly obese patients. Analysis of his recent publications reveals dominant themes in multi-omic exercise physiology, causal metabolite-disease relationships, and tissue-specific metabolic adaptations. His work consistently emphasizes sexual dimorphism, temporal dynamics, and molecular mechanisms in metabolic disorders, particularly through large-scale consortia like MoTrPAC. Key focus areas include lipid metabolism in disease contexts, adipose tissue remodeling, and mitochondrial function across tissues. While no specific scientific awards beyond his endowed chair are listed, his leadership of major NIH-funded centers demonstrates significant recognition. His grant portfolio includes infrastructure support through the MMOC, NORC, and Metabolomics Resource Core, enabling extensive human and animal studies in metabolic diseases. Dr. Burant directs the MMOC's integration of research and clinical care through the Investigational Weight Management Clinic. His laboratory maintains active programs in insulin resistance mechanisms using animal models and explores pancreatic progenitor cell applications for diabetes therapy, representing forward-looking approaches to metabolic disease treatment.

Jianhua Ruanمشاهده پروفایل

استاد

• Bioinformatics
• Computational Systems Biology
• Machine Learning for Life Sciences
• +۷ مورد دیگر

Dr. Jianhua Ruan is a Professor in the Department of Computer Science at The University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. He holds a Ph.D. in Computer Science from Washington University in St. Louis and has been a faculty member at UTSA since 2007. His research focuses on bioinformatics, computational systems biology, and machine learning applied to life sciences. Specific areas include modeling cis-regulatory networks, analyzing biological networks via graph algorithms, and biomarker discovery using omics data. Dr. Ruan collaborates extensively with biologists at UTSA and the UT Health Science Center on genomic, transcriptomic, proteomic, and epigenomic studies. Key research interests include: Machine learning for cis-regulatory network modeling Graph algorithms for biological network analysis Integrated omics data analysis for disease prediction Epigenetic regulation and cancer biology His work has been supported by NIH, NSF, and the San Antonio Life Sciences Institute. Notable achievements include a Best Performer award in the DREAM Reverse Engineering Challenges (2008) and the UTSA Faculty Research Award (2007). Dr. Ruan organizes the UTSA Computer Science Graduate Research Seminar, fostering interdisciplinary collaboration. He has developed software tools like TSCAN for single-cell RNA-seq analysis and contributes to computational methods for biomarker discovery and network-based classification.

Michael Millerمشاهده پروفایل

استاد

• Medical Imaging
• Computational Anatomy
• Pattern Theory
• +۳ مورد دیگر

Michael I. Miller is the Bessie Darling Massey Professor and director of the Department of Biomedical Engineering at Johns Hopkins University (JHU), where he also leads the Center for Imaging Science and co-directs the Kavli Neuroscience Discovery Institute. His work focuses on computational medicine, medical imaging, and computational anatomy, particularly in neurodegenerative diseases like Alzheimer’s, Huntington’s, and schizophrenia. Miller has pioneered cloud-based methods for brain image libraries and algorithms, co-founded four startups, and holds over 200 publications and two textbooks. He earned his BS from SUNY Stony Brook (1976) and MS/PhD from JHU (1978/1983). Education: Bachelor of Science, State University of New York at Stony Brook, 1976 Master of Science, Johns Hopkins University, 1978 Doctor of Philosophy, Johns Hopkins University, 1983 Research Interests: Miller’s research integrates advanced imaging technologies to study brain structure and function in health/disease. Key areas include: Functional and structural analysis of neurodegenerative diseases Development of computational tools for early disease detection Multiscale data integration (molecular to tissue) Translational applications in clinical diagnostics Awards: IEEE Biomedical Engineering Thesis Award (1982) Johns Hopkins Paul Ehrlich Thesis Award (1983) NSF Presidential Young Investigator Award (1986) Johns Hopkins Gilman Scholar (2011) Fellow, American Institute for Medical and Biological Engineering Labs/Teams: His lab at JHU develops cloud-based imaging tools and collaborates on translational research. Projects include the xIV-LDDMM Toolkit and STalign spatial transcriptomics alignment software.

Evan Biederstedtمشاهده پروفایل

پژوهشگر

• Computational Biology
• Cancer Genomics
• Bioinformatics
• +۳ مورد دیگر

Evan Biederstedt is an Associate in Biomedical Informatics at Harvard Medical School, affiliated with the Gehlenborg Lab. He holds a Master of Science (MSc) and specializes in computational biology with a focus on cancer genomics. His work includes developing the Cell Annotation Platform (CAP) to enhance cellular state interpretation through metadata and molecular signature analysis. Biederstedt’s research spans single-cell analysis, spatial transcriptomics, and genomic tools for structural variant discovery. His contributions include scaling cross-tissue annotation models and haplotype-aware somatic variation analysis. Key areas of focus include bioinformatics tool development, cancer genomics, and genomic data integration. He has collaborated on projects like the Virus Discovery Codeathon and international hackathons for genomic analysis. Biederstedt’s GitHub profile showcases his open-source contributions. His publications highlight advancements in tensor decomposition for patient cohort analysis, spatial transcriptomics, and pangenome strategies. Biederstedt’s work bridges computational methods with biomedical applications, emphasizing scalable solutions for complex genomic data challenges.

Thomas M Vondriskaمشاهده پروفایل

استاد

• Cardiovascular Epigenomics
• Chromatin Structure
• Heart Failure
• +۴ مورد دیگر

Thomas M Vondriska is a Professor in the School of Medicine at the University of California Los Angeles, with joint appointments in the Departments of Anesthesiology, Medicine, and Physiology. His research focuses on understanding the epigenomic basis of heart disease, particularly how chromatin structure and regulation contribute to cardiac pathophysiology. Dr. Vondriska leads multiple NIH-funded research projects exploring novel therapeutic targets for heart failure and cardiac remodeling. Dr. Vondriska's research interests center on cardiovascular epigenomics, chromatin structure, and gene regulation in heart disease. His laboratory investigates how epigenetic modifications influence cardiac development, growth, and response to stress. Specific areas of focus include histone modifications, DNA methylation patterns, non-coding RNA regulation, and the three-dimensional organization of chromatin in cardiac cells. His work bridges basic molecular mechanisms with clinical applications in heart failure and cardiac hypertrophy. Analysis of Dr. Vondriska's recent publications reveals a strong emphasis on the intersection of epigenetics and cardiac physiology. His research demonstrates how chromatin architecture regulates cardiac gene expression during development and disease, with particular attention to heart failure with preserved ejection fraction (HFpEF), cardiac fibrosis, and arrhythmias. His work increasingly incorporates single-cell technologies, computational approaches, and translational studies connecting basic mechanisms to clinical applications. Dr. Vondriska has secured substantial NIH funding as Principal Investigator for multiple projects including "Epigenomic basis of resilience to heart failure" (R21HL150667), "Small molecule therapies targeting chromatin architecture in heart failure" (R01HL150225), and "Novel Mechanisms of LncRNA Mediated Epigenetic Regulation in Cardiac Hypertrophy" (R01HL143058). His research program has been continuously funded by NIH since 2006, reflecting the significance and productivity of his work in cardiovascular epigenomics. Dr. Vondriska leads a research laboratory focused on cardiac epigenomics and systems biology. His team employs advanced techniques including chromatin conformation capture, single-cell sequencing, proteomics, and computational modeling to investigate the molecular mechanisms underlying heart disease. The laboratory collaborates extensively with clinical researchers to translate basic findings into potential therapeutic approaches for cardiovascular conditions.

Stephen Ramseyمشاهده پروفایل

دانشیار

• Computational Systems Biology
• Bioinformatics
• Precision Medicine
• +۶ مورد دیگر

Stephen Ramsey, an Associate Professor at Oregon State University, holds dual appointments in the School of Electrical Engineering and Computer Science (College of Engineering) and the Department of Biomedical Sciences (Carlson College of Veterinary Medicine). With a PhD in Physics from the University of Maryland, his postdoctoral training in computational genomics at the University of Washington, and professional experience at the Institute for Systems Biology and Center for Infectious Disease Research, Ramsey bridges computational methods with biomedical applications. Education : Ph.D., Physics, University of Maryland; M.S., Physics, University of Maryland; Sc.B., Mathematical Physics, Brown University Ramsey specializes in computational systems biology , focusing on bioinformatics , biomedical knowledge graphs , and precision medicine . His research integrates machine learning , gene regulatory network modeling , and multi-omics data analysis to address challenges in rare disease diagnostics , drug monitoring , and inflammatory disease mechanisms . Current work includes AI-driven biomedical translation and electrochemical biosensor development for non-invasive diagnostics . Recent publications highlight knowledge graph applications in translational biomedicine , causal network inference in clinical-environmental data integration , and cross-species cancer transcriptomics . His team develops tools like RTX-KG2 and PloverDB to standardize biomedical data sharing and semantic reasoning . Scientific Awards : 2019 Zoetis Award (Carlson College of Veterinary Medicine) 2016 NSF CAREER Award 2016 PhRMA New Investigator Award 2010 NIH K25 Mentored Quantitative Research Award Ramsey advises in computational biology courses (CS 446/546) and contributes to biomedical AI through projects like mediKanren for rare disease diagnostics . His NSF-funded research explores gene expression noise and regulatory network dynamics , while NIH and PhRMA grants support his translational medicine initiatives. He leads the Ramsey Laboratory , which develops graph-based reasoning tools for biomedical data translation and multi-omics integration . The lab's work spans comparative oncology models, electrochemical biosensors , and knowledge graph infrastructure for clinical decision support .

• Regenerative Medicine
• Cardiovascular Development
• Genomics
• +۵ مورد دیگر

Mirana Ramialison is a Professor at Monash University, affiliated with both the Australian Regenerative Medicine Institute and Victorian Heart Institute (VHI). She maintains an active research program with significant contributions to cardiovascular development and regenerative medicine. Her research interests focus on understanding the role of non-coding DNA regulatory elements in heart development and disease, formation of boundaries in developing embryos, and molecular mechanisms underlying cardiac function. She employs cutting-edge genomic, computational, and imaging approaches to investigate gene regulatory networks that govern cardiovascular development. Her recent publications demonstrate expertise in spatial transcriptomics, machine learning applications in epigenomics, and the use of vertebrate models like the African killifish to study aging processes. Her work spans developmental biology, genomics, and computational approaches to understand heart development and disease mechanisms. Professor Ramialison leads multiple significant research projects including 'Role of human non-coding DNA regulatory elements (REs) in heart development and disease' as Primary Chief Investigator, and collaborates on projects related to chemotherapy-induced heart damage prevention and bone growth mechanisms. She has produced 71 research outputs with notable recent publications in high-impact journals including Nature Cardiovascular Research, Genome Biology, and Communications Biology, demonstrating sustained scholarly productivity and impact in her field.

Susanne Gerberمشاهده پروفایل

استاد

• Bioinformatics
• Computational Genomics
• Systems Biology
• +۲ مورد دیگر

Susanne Gerber is a Professor at iDNA and Adjunct Director at the Institute of Molecular Biology (IMB), Johannes Gutenberg University Mainz (JGU), affiliated with the Faculty of Biology's Bioinformatics department. Her academic journey includes an Assistant Professorship in Bioinformatics at JGU (2015-2020) and postdoctoral research at Università della Svizzera italiana. Her educational background comprises a PhD in Biophysics from Humboldt University of Berlin (2011), an M.Sc. in Bioinformatics from Free University of Berlin and Konrad Zuse Institute (2007), and a B.Sc. in Bioinformatics from Free University of Berlin and Max Planck Institute (2004). Dr. Gerber's research spans Bioinformatics, Computational Genomics, Systems Biology, Molecular Evolution, and Neuroinformatics , focusing on developing computational frameworks for genomic analysis, neurodegenerative disease modeling, and microbiome interactions. Her work integrates machine learning with multi-omics data to address complex biological questions in molecular evolution and neural systems. Analysis of her 15 most recent publications (2024-2025) reveals a strong emphasis on nanopore sequencing applications for RNA modification detection, deep learning frameworks for genomic data enhancement, and neurobehavioral modeling using AI-driven approaches. Key thematic clusters include epitranscriptomics, chromatin dynamics, and computational psychiatry with ethical AI considerations. Her methodological innovations include tools like COMET for network analysis, CCUT for chromatin data enhancement, and ModiDeC for RNA modification classification, demonstrating translational impact across genomics and neuroscience. Dr. Gerber leads research groups at IMB and iDNA focusing on computational genomics, advising students in bioinformatics and securing grants for AI-driven genomic analysis. Her labs develop open-source tools for nanopore data processing and neuroimaging analysis, fostering collaboration between computational and experimental biologists.

Lene Uhrbomمشاهده پروفایل

استاد

• Neuro-oncology
• Glioblastoma research
• Molecular cancer biology
• +۵ مورد دیگر

Lene Uhrbom is Professor of Molecular Brain Tumor Research at Uppsala University's Department of Immunology, Genetics and Pathology within the Faculty of Medicine. She leads the Neuro-oncology and Neurodegeneration Research Programme and maintains an active research group focused on glioblastoma mechanisms. Her team includes PhD students Veera Jokinen and Ines Neves, Master's student Ellen Rahmqvist, doctoral assistant Xi Lu, and research assistant Rebeca-Noemi Imbria. Her research centers on neuro-oncology with emphasis on glioblastoma stem cells, molecular mechanisms of tumorigenesis, and cell-of-origin studies. She investigates how epigenetic regulation, signaling pathways, and tumor microenvironment interactions drive glioma development and therapeutic resistance. Her work bridges basic molecular biology with translational applications for improved diagnostics and treatments. Analysis of her 15 most recent publications reveals a strong focus on molecular subtyping of glioblastoma, stem cell biology, and therapeutic targeting. Her research consistently explores how cellular lineage, epigenetic regulation, and microenvironmental factors create distinct tumor subgroups with different clinical outcomes. Recent work has expanded into vascular biology, immunotherapy, and advanced imaging techniques for tumor detection. Professor Uhrbom has received significant recognition for her work, with publications in high-impact journals including Nature Communications, Cancer Cell, and Journal of Clinical Investigation. Her research has been featured in multiple news outlets, cited in patents, and referenced in Wikipedia, demonstrating its broad scientific impact. Her laboratory maintains an extensive collection of patient-derived glioblastoma models and employs advanced techniques including single-cell sequencing, epigenetic profiling, and sophisticated mouse models. Current projects focus on developing targeted therapies based on molecular subtyping and improving drug delivery across the blood-brain barrier. She actively collaborates with clinical neuro-oncology teams to translate basic findings into potential therapeutic strategies.

Fatemeh Vafaeeمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Systems Biology
• +۵ مورد دیگر

Associate Professor Fatemeh Vafaee is a leading researcher at the University of New South Wales (UNSW) , holding appointments as Associate Professor in the School of Biotechnology and Biomolecular Sciences (BABS) and Deputy Director (Science) of the UNSW AI Institute . She previously served as Deputy Director of the UNSW Data Science Hub (uDASH) and has held academic positions at the University of Toronto and the University of Sydney. PhD in Artificial Intelligence from University of Illinois at Chicago Postdoctoral Fellowships at University of Toronto and University of Sydney Founded the AI-Enhanced Biomedicine Laboratory in 2017 Her research focuses on deploying advanced AI techniques to address biomedical challenges through: Biomarker Discovery for cancer and neurodegenerative diseases Single-Cell Multi-Omics data integration and analysis Computational Drug Repositioning and network pharmacology Multi-Omics Data Fusion and temporal network modeling Recent publications demonstrate expertise in liquid biopsy development , single-cell imaging , and AI-driven cancer diagnostics . Her methodological contributions include novel deep learning architectures for omics data analysis and graph neural networks for drug synergy prediction. Scientific accolades include: Winner, Women in AI Asia-Pacific Health Award (2023) Runner-Up, WAI-APAC Innovator of the Year (2023) Top 10 Women in AI in Asia-Pacific (2023) Australian Bioinformatics and Computational Biology Society Research Excellence Award (2023) She supervises PhD candidates across computational biomedicine and AI in healthcare , with significant grant achievements exceeding $17M in competitive funding, including schemes from ARC Discovery , NHMRC , and Medical Research Future Fund .

David Monkمشاهده پروفایل

استاد

• Developmental Epigenetics
• Genomic Imprinting
• DNA Methylation
• +۷ مورد دیگر

David Monk is Professor of Developmental Epigenetics at the University of East Anglia, School of Biological Sciences, where he also serves as the Cells & Tissues Theme Leader. His research focuses on epigenetic mechanisms in human development and disease, with emphasis on genomic imprinting, DNA methylation, and placental biology. Research Interests: His work investigates how epigenetic regulation influences fetal growth, imprinting disorders, and cancer. He employs high-throughput genomic technologies, single-cell epigenomics, and genome editing to dissect the interplay between genetic variation and epigenetic marks, especially in early development and placental function. Recent Research Trends: His most recent publications (2023–2025) highlight a strong focus on placenta-specific imprinting, non-canonical methylation patterns, transcriptomic alterations in complicated pregnancies, and epigenetic defects in early embryonic development. The integration of single-cell multi-omics and low-input profiling is a recurring methodological theme. Scientific Awards: Ramon y Cajal Career Development Fellowship Grants and Advising: He leads multiple active research projects funded by the BBSRC, MRC, and NHS-affiliated trusts. While specific students are not listed, his publications indicate active mentorship of researchers in epigenetics and developmental biology. Labs and Teams: He leads a research team at UEA focused on developmental epigenetics, and collaborates with international institutions, including IDIBELL (Barcelona), and clinical partners such as Norfolk & Norwich University Hospitals NHS Foundation Trust.

Pei Fen Kuanمشاهده پروفایل

استاد

• Statistical Genomics
• Computational Biology
• Bioinformatics
• +۳ مورد دیگر

Pei Fen Kuan is a Professor in the Department of Applied Mathematics and Statistics at Stony Brook University. She holds a Ph.D. in Statistical Genomics from the University of Wisconsin-Madison. Her research focuses on developing statistical methodologies for analyzing high-throughput omics data, particularly in genome biology and psychiatric/cancer-related studies. She leads projects on ChIP-Seq, RNA-Seq, and DNA methylation analysis, with significant contributions to the WTC Wellness Program studying trauma-related disorders. Education: Ph.D. in Statistics (2009), University of Wisconsin-Madison; B.S. (details not specified). Research interests include computational biology, epigenetics, and integrative omics, with a focus on PTSD, cognitive aging, and melanoma genetics. She has developed R packages like methylGSA and RNAAgeCalc for bioinformatics analysis. Current grants include NIH/NIA and CDC/NIOSH funding for PTSD and aging studies. Lab involvement: Directs the WTC Wellness Program research team, focusing on gene-environment interactions in responders' health. Collaborates internationally via the InterMEL consortium for melanoma studies. Teaching: Courses in computational statistics and statistical genomics (details inferred from departmental role).

Yiqiang Zhangمشاهده پروفایل

استادیار

• Cardiovascular Biology
• Stem Cell Biology
• Regenerative Medicine
• +۶ مورد دیگر

Yiqiang Zhang, PhD is an Assistant Professor in the Department of Anatomy, Biochemistry & Physiology at the University of Hawaii John A. Burns School of Medicine, affiliated with the Center for Cardiovascular Research. He holds graduate faculty roles in Developmental and Reproductive Biology (DRB) and Cell and Molecular Biology (CMB). Dr. Zhang earned his B.Sc. in Biology (Zoology) and M.Sc. in Physiology from Sun Yat-sen University (China), followed by a PhD in Biomedical Sciences from the University of Montreal (2006). His postdoctoral training included Johns Hopkins Medicine’s Cardiology Fellowship Program and Cedars-Sinai Medical Center, focusing on developmental cell biology and cardiac electrophysiology. He has held prior roles as a Staff Scientist at City of Hope National Medical Center and Acting Assistant Professor at the University of Washington School of Medicine. His research integrates multi-omics, bioinformatics, and advanced cellular/molecular techniques to study cardiac development, disease mechanisms, and regenerative therapies. Key projects include: epigenetic regulation of cardiomyocyte cell cycle re-entry, single-cell analysis of heart failure mechanisms, and leveraging stem cell models to understand congenital heart defects. His work addresses diabetic cardiomyopathy, arrhythmias, and heart failure through translational approaches. Supported by NIH, American Heart Association, and other funding agencies, his lab seeks to identify novel therapeutic targets for cardiovascular diseases. Dr. Zhang teaches across undergraduate, graduate, and medical programs, emphasizing translational research and clinical education. His contributions span over 60 peer-reviewed publications, including high-impact studies in Cell , Circulation , and Nature journals. Current research emphasizes computational genomics, epigenetic modifiers, and inflammatory pathways in cardiac disorders.

Kadir C. Akdemirمشاهده پروفایل

استادیار

• Cancer Genomics
• Computational Biology
• Chromatin Organization
• +۴ مورد دیگر

Dr. Kadir C. Akdemir is an Assistant Professor in the Department of Neurosurgery at The University of Texas MD Anderson Cancer Center. His research focuses on integrating computational analyses and multi-omics data to understand chromatin organization and genomic alterations in brain tumors. He leads the Akdemir Lab, which collaborates with global initiatives like the NIH’s 4Dnucleome project and the Break Through Cancer Foundation’s GBM program. Education: Ph.D. in Genetics and Epigenetics from the UTHealth Houston Graduate School (2013), postdoctoral training at MD Anderson’s Genomic Medicine Department. His work combines computer science and genetics to develop tools for analyzing cancer genomes. Research interests include chromatin folding dynamics, somatic rearrangements, and computational methods for genomic data integration. His lab studies glioblastoma and chordoma, focusing on tumor evolution and microenvironment interactions. Notable achievements include identifying ERBB2 alterations in esophageal adenocarcinoma brain metastases and elucidating the role of KDM5D in colon cancer sex differences. He holds grants from NCI, Break Through Cancer, and the UT Rising Stars Award. Lab membership includes PhD/Master’s students and postdocs working on projects like spatial transcriptomics and ecDNA dynamics. Collaborations span institutions like the Brain Cancer SPORE team and the GBM Moonshot initiative.