جست‌وجوی استاد و پژوهشگر

Alexis Battleمشاهده پروفایل

دانشیار

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

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

• Statistical Genomics
• Computational Immunology
• Human Genetics
• +۲ مورد دیگر

Yang Luo is a Kennedy Trust Senior Research Fellow in Data Science at the University of Oxford's Kennedy Institute of Rheumatology. His research bridges statistical genomics and computational immunology to unravel genetic contributions to immune-mediated traits, with a focus on the major histocompatibility complex (MHC) region. His work leverages large biobank datasets (UK Biobank, Biobank Japan), gene expression resources (GTEx), and proteomic data to decode molecular mechanisms linking genetic variation to disease risk. Specific interests include tuberculosis genetics, multi-ancestry polygenic risk scores, and single-cell eQTL modeling. Recent publications highlight expertise in HLA association studies, evolutionary immunogenetics, and disease-specific cell state dynamics. Key contributions include constructing a global HLA haplotype panel and developing novel statistical methods for admixed population genetics. Scientific Awards: Kennedy Trust Senior Research Fellow in Data Science His lab integrates computational and experimental approaches to translate genetic findings into clinical applications for immune disorders.

Ali Shojaieمشاهده پروفایل

استاد

• Biostatistics
• Statistics
• Machine Learning
• +۴ مورد دیگر

Ali Shojaie is a Professor of Biostatistics and Statistics at the University of Washington, serving as Associate Chair for Strategic Research Affairs in the Department of Biostatistics. He leads the Summer Institute for Statistics in Big Data (SISBID) and the Data Management and Statistics (DMS) Core for the UW Alzheimer's Disease Research Center. His research focuses on developing statistical and machine learning methods for high-dimensional data, with applications in genomics, neuroscience, and public health. Shojaie's work includes advancements in graphical models, Granger causality, and spatial statistics. He has contributed to methodologies for analyzing networks from time series and spatial data, with applications in understanding gene regulatory networks and brain connectivity. His recent projects involve NIH-funded grants exploring gene-phenotype associations using omic data and explainable machine learning for brain stimulation research. Scientific awards include the 2022 Leo Breiman Award from ASA's Statistical Learning and Data Science section, and election as a Fellow of the Institute of Mathematical Statistics (IMS) and American Statistical Association (ASA). He serves on editorial boards for journals like the Journal of the American Statistical Association and Biometrika. Shojaie advises numerous PhD students and postdocs, many of whom have secured academic and industry positions. His lab develops open-source software tools, including the netgsa and ngc packages for network analysis and Granger causality estimation.

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.

• Bioinformatics
• Systems Biology
• Disease Module Identification
• +۶ مورد دیگر

Zelmina Lubovac is a Senior Lecturer in BioInformatics at the School of Bioscience, University of Skövde. She serves as both a Course Coordinator for multiple undergraduate and graduate courses in bioinformatics and a Programme Coordinator for Master's level programs. Her academic work focuses on the intersection of computational methods and biological applications, particularly in disease analysis and biomarker discovery. Dr. Lubovac's research spans several key areas in bioinformatics and systems biology: Disease module identification in complex biological networks Multi-omics integration (genomics, proteomics, metabolomics) for biomarker discovery Machine learning applications in RNA-seq and other high-throughput biological data Development of bioinformatics software tools for network analysis miRNA analysis in cancer and neurological disorders Her recent publications (2022-2024) demonstrate a strong focus on applying computational approaches to understand disease mechanisms, particularly in pancreatic cancer and multiple sclerosis. She has developed several widely-used bioinformatics tools including MODalyseR, MODifieR, and TFTenricher that facilitate disease module analysis and gene network interpretation. Her work often involves collaborative research with clinical teams to translate computational findings into potential diagnostic applications. Dr. Lubovac has been involved in significant research projects including: BIO-AID (Biomedical AI-driven data analytics): Oct 2020 - Sep 2024 Systems Biology DMDPipe: Mar 2018 - Feb 2021 She actively contributes to both undergraduate and graduate education at the University of Skövde, coordinating multiple courses and programs in bioinformatics and bioscience, with a clear emphasis on preparing students for careers at the intersection of biology and computational science.

• Autism Genetics
• Genetic Architecture
• Copy Number Variants
• +۵ مورد دیگر

Lauren Weiss, PhD is a Professor of Psychiatry at the University of California, San Francisco (UCSF) School of Medicine and a faculty member at the UCSF Weill Institute for Neurosciences. Her research focuses on understanding the genetic architecture of autism spectrum disorder through genome-wide genetic data analysis and human induced pluripotent stem cell (iPSC) models. Dr. Weiss's laboratory investigates the genetic mechanisms by which DNA variants influence autism risk, examining questions about copy number vs. SNP variation, rare vs. common variation, gene-sex interaction, gene-gene interaction, and gene-environment interaction. Her team uses rich genetic datasets to identify susceptibility loci and the physiological pathways these risk loci implicate. Additionally, they employ iPSC models to study known mutations or copy number variants predisposing to autism, first identifying the effects of genetic risk variants and then determining whether these effects can be modified at the cellular level by environmental or pharmacological agents. Analysis of Dr. Weiss's recent publications reveals a strong focus on sex differences in autism genetics, the role of specific copy number variants (particularly 16p11.2 and 22q11.2), maternal environmental factors during pregnancy, and the integration of multi-omics data to understand neurodevelopmental pathways. Her work bridges basic genetic research with potential clinical applications for improving understanding, prevention, diagnosis, and treatment of autism and related traits. Dr. Weiss has secured significant research funding as Principal Investigator on multiple NIH grants, including R01MH114924 (Decoding the Genetics of Sexual Dimorphism in Autism Spectrum Disorders), R01MH107467 (Utilizing eQTL networks to gain biological insight into multigenic CNVs), and DP2OD007449 (Dissecting Epistasis and Pleiotropy in Autism towards Personalized Medicine). Her laboratory offers research opportunities for students interested in analytical genetics projects related to gene-environment effects, gene-sex effects, gene-gene effects, and the relationship between ASD and brain size. Dr. Weiss actively collaborates with numerous researchers across institutions, particularly on large-scale genomic studies of autism and other neurodevelopmental disorders. Her work has contributed significantly to our understanding of the complex genetic architecture underlying autism spectrum disorder and related conditions.

Yi Liمشاهده پروفایل

استاد

• Survival Analysis
• Data Science
• High-dimensional Inference
• +۱۱ مورد دیگر

Yi Li is the M. Anthony Schork Collegiate Professor of Biostatistics at the University of Michigan School of Public Health. With a PhD in Biostatistics from the University of Michigan (1999) and postdoctoral training at Harvard (1999-2000), Dr. Li has established himself as a leading researcher in statistical methodology with applications across multiple biomedical domains. Dr. Li's research spans survival analysis, data science, high-dimensional inference, machine learning, deep learning, spatial data analysis, random-effects models, clinical trial design, and infectious disease modeling. His methodological work finds application in cancer genetics/genomics, radiomics, racial disparity analysis, chronic disease research, and opioid overuse studies. With over 230 publications in major statistical journals including JASA, Biometrika, JRSSB, and Biometrics, as well as premier subject matter journals like PNAS, JAMA, and JCO, Dr. Li's work has significantly impacted both statistical theory and biomedical applications. His research portfolio demonstrates consistent evolution from foundational methodological work in survival analysis and spatial statistics to cutting-edge applications in high-dimensional data, machine learning, and deep learning approaches for complex biomedical problems. The recent publications reveal increasing focus on integrating multiple data sources, causal inference in observational studies, and developing interpretable machine learning models for clinical applications. Dr. Li's work has been continuously supported by NIH funding since 2003, including multiple National Cancer Institute grants (R01 CA95747, 1P01CA134294-010002, R21CA157219, R01CA249096, R01CA269398) and a National Institute on Aging grant (R21AG058198). He actively collaborates with researchers from the University of Michigan and Harvard University on clinical and observational studies. As an educator, Dr. Li has taught advanced courses in survival analysis and statistical methods, mentoring the next generation of biostatisticians. His methodological contributions have been widely recognized through invitations to serve on NIH study sections (BMRD 2008-2012, EPIC 2015-2019) and as Associate Editor for leading statistical journals including Journal of the American Statistical Association, Biometrics, and Scandinavian Journal of Statistics.

Johannes Soedingمشاهده پروفایل

پژوهشگر

• Computational Biology
• Bioinformatics
• Protein Structure Prediction
• +۹ مورد دیگر

Johannes Soeding is a Research Group Leader in the Computational Biology department at the Max Planck Institute for Multidisciplinary Sciences in Göttingen, Germany. His work bridges physics, bioinformatics, and molecular biology, focusing on computational methods for biological data analysis. His research interests include computational biology, protein structure and function prediction, metagenomics, transcriptional regulation, and statistical genomics. He develops widely used software tools such as HH-suite, HHpred, MMseqs2, and Foldseek for protein sequence and structure analysis. The recent publications demonstrate a strong focus on high-throughput biological data, particularly in protein structure search (e.g., Foldseek), metagenomic gene discovery (e.g., MetaEuk), and regulatory genomics. His work combines algorithm development with deep biological insights, often published in top-tier journals like Nature Biotechnology , Science , and Nature Methods . He has been involved in significant methodological advances in sequence clustering, contact prediction, and eQTL analysis, showing a consistent trend toward scalable, data-driven approaches in genomics and proteomics. Soeding has contributed to major projects in gene regulatory networks and RNA biology, often in collaboration with experimental groups. His leadership in developing open, efficient bioinformatics tools has had a broad impact on the scientific community. He is affiliated with several graduate programs including IMPRS Physics of Biological and Complex Systems, Biomolecules: Structure - Function - Dynamics, and Genome Science, indicating active participation in training the next generation of scientists.

Abigail Bighamمشاهده پروفایل

دانشیار

• Human Population Genetics
• Molecular Evolution
• Natural Selection in Human Genome
• +۴ مورد دیگر

Abigail Bigham is an Associate Professor and Vice Chair for Undergraduate Studies in the Department of Anthropology at the University of California, Los Angeles (UCLA). Her research focuses on human population genetics, molecular evolution, and high-altitude adaptation, particularly in Andean and Tibetan populations. She holds a PhD in Anthropology from Penn State University (2008) and a BA in Anthropology from the University of Arizona (2001). Her team investigates genetic and epigenetic mechanisms underlying adaptation to hypoxia, diet, and infectious diseases. Key research areas include the genetic basis of altitude-related traits like hemoglobin concentration, pulmonary function, and metabolic adaptations. Her lab also explores the evolutionary impact of dietary changes (e.g., potato-rich diets in the Andes) and immune responses to pathogens such as HIV and dengue. Recent work includes epigenetic studies of high-altitude acclimatization and the functional validation of adaptive alleles. Dr. Bigham’s projects often involve interdisciplinary collaborations, integrating genomic data with physiological measurements and anthropological fieldwork. She has conducted field studies in Peru, Tibet, Guatemala, and Nepal, focusing on indigenous populations to understand how genetic variation contributes to survival in extreme environments. Her lab’s website (https://bigham-lab.anthro.ucla.edu/) provides access to datasets and research tools. Her academic contributions span over 50 peer-reviewed articles and grants addressing high-altitude physiology, evolutionary genetics, and global health. She actively advocates for ethical discussions on race and genetics, challenging simplistic biological determinism through public outreach and educational initiatives.

Grant Montgomeryمشاهده پروفایل

استاد

• Genetics
• Genomics
• Reproductive Disorders
• +۴ مورد دیگر

Professor Grant Montgomery is a leading academic at the University of Queensland (UQ), serving as Director of the UQ Genome Innovation Hub and Group Leader of the Genomics of Reproductive Disorders at the Institute for Molecular Bioscience (IMB). His primary focus is on understanding the genetic and epigenetic basis of reproductive disorders, particularly endometriosis, through large-scale genomic studies and functional analyses. His work integrates multi-omics data to identify disease mechanisms and develop translational solutions. Research Interests: Genetic and epigenetic regulation of endometrial function Systems genetics approaches to study complex diseases Functional validation of genomic risk variants Development of clinical tools like eQTL/mQTL browsers Multi-omics integration for disease biomarker discovery Recent Articles: Highlighting endometriosis genetics, comorbidity mechanisms, and novel diagnostic approaches. Key themes include genetic risk stratification, epigenetic dysregulation, and translational applications of genomic data. Collaborations: Active partnerships with institutions globally, including University of California, San Francisco, Oxford Endometriosis CaRe Centre, and Monash University. Major consortia include International Endometriosis Genetics Consortium and Reprogen Consortium. Advising & Training: Supervises PhD students focusing on endometrial cell biology, epigenetic regulation, and computational genomics. Provides training opportunities in functional genomics and systems biology. Labs/Teams: Leads a multidisciplinary team with expertise in molecular biology, bioinformatics, and clinical research. Maintains cell culture facilities and single-cell sequencing capabilities.

Dongwon Leeمشاهده پروفایل

استادیار

• Computational Biology
• Genomics
• Machine Learning
• +۷ مورد دیگر

Dongwon Lee is an Assistant Professor of Pediatrics in the Division of Nephrology at Boston Children's Hospital and Harvard Medical School. He is affiliated with the Manton Center for Orphan Disease Research and serves as an Associate Member of the Broad Institute of MIT and Harvard. Additionally, he is a faculty member of the Harvard Bioinformatics and Integrative Genomics (BIG) PhD Program, where he contributes to training the next generation of computational biologists. Dr. Lee received his PhD in Biomedical Engineering from Johns Hopkins University in 2013 and completed postdoctoral research at the Center for Human Genetics and Genomics at NYU School of Medicine. His educational background has provided a strong foundation for his interdisciplinary research at the intersection of computational biology, genomics, and pediatric kidney diseases. Dr. Lee's research focuses on understanding how gene regulation contributes to the development and progression of human diseases, specifically pediatric kidney diseases. His laboratory employs a combination of single-cell multiomics data, genetic data from disease cohorts, functional assays, and machine-learning approaches to address complex biological problems. Key research areas include building cell-type-specific gene regulatory networks, developing machine-learning models to identify regulatory variants, and validating predictions using high-throughput sequencing technologies in collaboration with experimental biologists. His work has significant implications for understanding the molecular basis of kidney diseases and developing new genomic computational tools. Analysis of Dr. Lee's recent publications reveals a strong trend toward increasingly sophisticated integration of single-cell multiomic data with machine learning techniques to understand transcriptional regulation in kidney disease. His research spans computational method development, regulatory genomics, and translational applications in nephrology, with a growing emphasis on cell-type-specific resolution and clinical correlations. Dr. Lee leads an active research laboratory with multiple computational biologists and research assistants. His team includes current members Anya Greenberg, Jeerthi Kannan, Yangyang Lin, Daniel Nguyen, and Eric Sakkas, as well as notable alumni including Seong Kyu Han (now Assistant Professor in South Korea), Ana Onuchic-Whitford (now Instructor at Harvard Medical School), and Jihoon Yoon (now Clinical Fellow in South Korea). His laboratory regularly recruits postdoctoral fellows and research assistants with computational backgrounds to advance their research on genomic regulation of kidney diseases. The Lee Laboratory has developed several important computational tools including LS-GKM (a scalable gkm-SVM for large-scale datasets), gkmQC (for quality assessment of chromatin accessibility data), and MTSA (for MPRA tag sequence analysis). These resources are publicly available on GitHub and have been widely adopted by the genomics research community. The laboratory maintains strong collaborative relationships with clinicians and experimental biologists to ensure their computational approaches have meaningful biological and clinical relevance.

Audrey Fuمشاهده پروفایل

دانشیار

• Statistical methods for genomics
• Machine learning algorithms
• Causal network inference
• +۷ مورد دیگر

Audrey Fu is an Associate Professor of Family Medicine and Public Health Sciences and of Molecular Medicine and Genetics at Wayne State University School of Medicine, located at Scott Hall, 540 E. Canfield Street, Detroit, MI 48201. Her interdisciplinary work bridges computational methods with biomedical applications, particularly in genomics and medical imaging fields. Her educational background includes a PhD from the University of Washington (2008), followed by postdoctoral training at the University of Chicago (2008-2014) and a Visiting Postdoctoral Scholar position at Stanford University (2014-2015). This strong foundation in statistics and computational biology has enabled her to develop innovative approaches to complex biomedical data analysis. Dr. Fu specializes in developing statistical methods and algorithms for analyzing high-dimensional biomedical data, with particular expertise in causal network inference using Mendelian randomization, deep learning for single-cell RNA-sequencing data, and methods for identifying disease-relevant cell types through integration of genomic data. Her research group actively practices open science, distributing open-source software packages in R and Python through GitHub and CRAN. Her publication record reveals a clear progression from fundamental statistical methodology development toward increasingly translational biomedical applications. While her earlier work focused on DNA methylation patterns, statistical inference of gene expression noise, and Bayesian clustering methods, her recent publications demonstrate expansion into medical imaging analysis, particularly in space medicine applications and neurological conditions like Chiari malformation. This evolution shows her ability to adapt statistical frameworks to address diverse biomedical challenges. NIH Pathway to Independence Award (K99/R00; 2014-2019) International Society for Bayesian Analysis Travel Award (2010) Dorothy and Leon Gilford Fellowship, Department of Statistics, University of Washington (2003) Dr. Fu is currently accepting new M.S. students for 2025-2026 but not new Ph.D. students. Her NIH K99/R00 award indicates successful transition from postdoctoral research to independent investigator status. Her lab develops multiple open-source software packages including MethylHMM, MRPC, LATE, and rolypoly, reflecting her commitment to making computational tools accessible to the broader research community. Her collaborative work spans multiple institutions and disciplines, from basic molecular biology to clinical applications in ophthalmology and neurosurgery.

C.J. Tsaiمشاهده پروفایل

استاد

• Plant Biology
• Genetics
• Biotechnology
• +۱۰ مورد دیگر

Dr. C.J. Tsai is the Winfred N. 'Hank' Haynes Professor and Georgia Research Alliance Eminent Scholar at the University of Georgia. His research focuses on plant biology, particularly the molecular mechanisms underlying tree longevity, perennial growth, and interactions with pathogens. He employs advanced techniques including CRISPR genome editing, transcriptomics, and metabolomics. His lab hosts visiting scholars for technical training in areas like in situ hybridization and metabolomic analysis. Research interests include sucrose transport regulation, lignin biosynthesis, and salicylic acid signaling in trees. He has pioneered tools for editing woody perennials, with industrial relevance for biomass production and ecological insights into tree-pathogen interactions. His lab is affiliated with The Plant Center and operates the Forest Functional Genomics Lab. Recent publications highlight breakthroughs in Populus genome editing, CRISPR promoter development, and discoveries like the BOOSTER gene enhancing photosynthesis. Major awards include the 2024 IUFRO Scientific Achievement Award and AAAS Fellowship. He oversees a team of 14 researchers and 3 Ph.D. students, focusing on translating fundamental discoveries into sustainable forestry applications. Awards: IUFRO Excellence Awards (2024), AAAS Fellowship (2017), IAWS Fellowship (2013) Advising: 3 current Ph.D. students and 14 research staff Labs: Forest Functional Genomics Lab (specializing in tree genomics and biotechnology)

• Statistical genomics
• Big omics data analysis
• Multivariate analysis
• +۵ مورد دیگر

Dr. Lin Chen is a tenured Professor of Biostatistics in the Department of Public Health Sciences at The University of Chicago, where they have been faculty since 2010, progressing from Assistant Professor (2010-2017) to Associate Professor (2017-2024) and now Professor (2024-present). Previously, they completed postdoctoral research at Fred Hutchinson Cancer Research Center (2008-2010) under Drs. Ross L. Prentice and Li Hsu, and at Princeton University (2008) under Dr. John D. Storey. Dr. Chen earned their Ph.D. in Biostatistics from the University of Washington in 2008 under Dr. John D. Storey, following a B.S. in Economics from Peking University (2002). Their research focuses on developing advanced statistical methods for statistical genomics , big omics data analysis , multivariate analysis , missing data analysis , and mixed-effects models . Their work has significantly advanced methodologies in Mendelian randomization, multi-omics integration, and genetic association studies. Dr. Chen's recent publications reveal a strong emphasis on integrative approaches that connect multiple layers of genomic data across different biological contexts. Their research demonstrates particular expertise in developing statistical frameworks that bridge genetic variation with molecular phenotypes and complex traits, often addressing critical challenges in causal inference and mediation analysis within genomic studies. The work consistently appears in top-tier journals including Nature Communications, Nature Genetics, and The American Journal of Human Genetics. The Best Paper in Genetic Epidemiology Award for the Year 2021 The Departmental Best Dissertation Award 2017-2018 (awarded to Jiebiao Wang) Dr. Chen actively mentors graduate students and postdoctoral fellows, with several former lab members now in faculty positions or industry roles at major pharmaceutical companies. Their lab currently has ongoing postdoctoral openings. They have developed multiple widely-used software packages including GMAC, Primo, mvMISE, ofGEM, MixRF, mixEMM, PEMM, Trigger, RHT, SNPath, and EigenR2, available through CRAN, GitHub, and Bioconductor. Dr. Chen teaches courses including Applied Regression Analysis, Introduction to Biostatistics, Statistical Analysis with Missing Data, and Introductory Statistical Genetics at the University of Chicago.

• Genetic Kidney Diseases
• Chronic Kidney Disease
• Autosomal Dominant Polycystic Kidney Disease (ADPKD)
• +۳ مورد دیگر

Gabriel Loeb, MD, PhD is an Assistant Professor at the University of California, San Francisco (UCSF) School of Medicine , where he operates as a physician-scientist specializing in genetic kidney diseases . His research integrates human genetics , genomics , and novel kidney disease models to identify molecular mechanisms in chronic kidney disease and Autosomal Dominant Polycystic Kidney Disease (ADPKD). Clinically, he focuses on familial and genetic kidney disease care at the UCSF Nephrology Faculty Practice . Education BS in Biology (2005), Stanford University MD (2015), Cornell PhD in Immunology (2015), Cornell/Rockefeller/Sloan Kettering Internal Medicine Residency (2018), Brigham and Women's Hospital/Harvard Medical School Nephrology Fellowship (2021), UCSF Research Interests center on leveraging human genetic variation to decode kidney disease mechanisms, with a focus on ADPKD and tubule epithelial regulatory elements . His work explores cell type-specific genomics , polycystin channel function , and urine multiomics for non-invasive diagnostics. Recent Publications highlight advancements in ADPKD mechanistic understanding , urine-based liquid biopsies , and genomic deep learning model limitations . Key journals include Nature Genetics , Nature Communications , and bioRxiv . Grants & Programs Laboratory for Genomics Research Innovation Award (2024–2025) Physician Scientist Scholars Program, UCSF (2021–2026)