School of Medicine
Showing 101-148 of 148 Results
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Daniel Rubin
Professor of Biomedical Data Science, of Radiology (Integrative Biomedical Imaging Informatics at Stanford), of Medicine (Biomedical Informatics Research) and, by courtesy, of Ophthalmology
Current Research and Scholarly InterestsMy research interest is imaging informatics--ways computers can work with images to leverage their rich information content and to help physicians use images to guide personalized care. Work in our lab thus lies at the intersection of biomedical informatics and imaging science.
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Mirabela Rusu
Assistant Professor of Radiology (Integrative Biomedical Imaging Informatics) and, by courtesy, of Urology and of Biomedical Data Science
Current Research and Scholarly InterestsDr. Mirabela Rusu focuses on developing analytic methods for biomedical data integration, with a particular interest in radiology-pathology fusion. Such integrative methods may be applied to create comprehensive multi-scale representations of biomedical processes and pathological conditions, thus enabling their in-depth characterization.
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Chiara Sabatti
Professor of Biomedical Data Science and of Statistics
Current Research and Scholarly InterestsStatistical models and reasoning are key to our understanding of the genetic basis of human traits. Modern high-throughput technology presents us with new opportunities and challenges. We develop statistical approaches for high dimensional data in the attempt of improving our understanding of the molecular basis of health related traits.
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Julia Salzman
Associate Professor of Biomedical Data Science, of Biochemistry and, by courtesy, of Statistics and of Biology
Current Research and Scholarly Interestsstatistical computational biology focusing on splicing, cancer and microbes
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Nigam H. Shah, MBBS, PhD
Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsWe analyze multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), to answer clinical questions, generate insights, and build predictive models for the learning health system.
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Laurel Stell
Research Engineer, Biomedical Data Science
Current Role at StanfordInvestigating non-glycemic genetic effects on HbA1c using the Veterans Administration Million Veteran Program (MVP). HbA1c is a widely used test that reflects average blood sugar level for the past two to three months. It is well known that certain genetic blood conditions, such as sickle cell disease, can cause HbA1c to be a misleading indicator of blood glucose levels. I am investigating the extent to which genetic variants can have this effect even without a diagnosis of one of these conditions. I am also investigating whether these effects are impacting clinical diagnosis and treatment of diabetes, and whether such impacts are reflected in health outcomes.
The impact of these variants has potentially been overlooked because they are very rare in populations with European genetic ancestry. As with the variant for sickle cell disease, they only persist when they provide an evolutionary advantage, such as protecting against malaria infection and its symptoms. Consequently, the genetic variants that I am analyzing do not appear in most genetic biobanks frequently enough to enable my analyses. MVP, however, includes genetic data for over 100,000 Veterans with African genetic ancestry, making it an ideal resource for this research. Since these variants are common among individuals with African genetic ancestry but practically non-existent in European genetic ancestry, this research may provide insight into racial health disparities in the US, particularly in T2D prevalence and outcomes.
I've also been a member of the department's JEDI Committee since its inception in 2021, providing assistance wherever I can. -
John S. Tamaresis, PhD, MS
Biostatistician, Biomedical Data Science
BioDr. Tamaresis joined the Stanford University School of Medicine in Summer 2012. He earned the Ph.D. in Applied Mathematics from the University of California, Davis and received the M.S. in Statistics from the California State University, East Bay. He has conducted research in computational biology as a postdoctoral scholar at the University of California, Merced and as a biostatistician at the University of California, San Francisco.
As a statistician, Dr. Tamaresis has developed and validated a highly accurate statistical biomarker classifier for gynecologic disease by applying multivariate techniques to a large genomic data set. His statistical consultations have produced data analyses for published research studies and analysis plans for novel research proposals in grant applications. As an applied mathematician, Dr. Tamaresis has created computational biology models and devised numerical methods for their solution. He devised a probabilistic model to study how the number of binding sites on a novel therapeutic molecule affected contact time with cancer cells to advise medical researchers about its design. For his doctoral dissertation, he created and analyzed the first mathematical system model for a mechanosensory network in vascular endothelial cells to investigate the initial stage of atherosclerotic disease. -
Lu Tian
Professor of Biomedical Data Science and, by courtesy, of Statistics
Current Research and Scholarly InterestsMy research interest includes
(1) Survival Analysis and Semiparametric Modeling;
(2) Resampling Method ;
(3) Meta Analysis ;
(4) High Dimensional Data Analysis;
(5) Precision Medicine for Disease Diagnosis, Prognosis and Treatment. -
Robert Tibshirani
Professor of Biomedical Data Science and of Statistics
Current Research and Scholarly InterestsMy research is in applied statistics and biostatistics. I specialize in computer-intensive methods for regression and classification, bootstrap, cross-validation and statistical inference, and signal and image analysis for medical diagnosis.
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Thodsawit Tiyarattanachai
Masters Student in Biomedical Data Science, admitted Autumn 2023
Current Research and Scholarly Interestsartificial intelligence
medical imaging
ultrasound
screening and surveillance of liver cancer
cancer prediction models
cancer biomarkers -
Dennis Wall
Professor of Pediatrics (Clinical Informatics), of Biomedical Data Science and, by courtesy, of Psychiatry and Behavioral Sciences
Current Research and Scholarly InterestsSystems biology for design of clinical solutions that detect and treat disease
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John Witte
Professor of Epidemiology and Population Health, of Biomedical Data Science and, by courtesy, of Genetics
BioDr. Witte joined the Stanford community in July 2021. In addition to serving as Vice Chair and professor in the Department of Epidemiology & Population Health, and as a professor of Biomedical Data Science and, by courtesy, of Genetics, he will also serve as a member of the Stanford Cancer Institute.
Dr. Witte is an internationally recognized expert in genetic epidemiology. His scholarly contributions include deciphering the genetic and environmental basis of prostate cancer and developing widely used methods for the genetic epidemiologic study of disease. His prostate cancer work has used comprehensive genome-wide studies of germline genetics, transcriptomics, and somatic genomics to successfully detect novel variants underlying the risk and aggressiveness of this common disease. A key aspect of this work has been distinguishing genetic factors that may drive increased prostate cancer risk and mortality among African American men. Providing an avenue to determine which men are more likely to be diagnosed with clinically relevant prostate cancer and require additional screening or specific treatment can help reduce disparities in disease prevalence and outcomes across populations. Dr. Witte has also developed novel hierarchical and polygenic risk score modeling for undertaking genetic epidemiology studies. These advances significantly improve our ability to detect disease-causing genes and to translate genetic epidemiologic findings into medical practice.
Dr. Witte has received the Leadership Award from the International Genetic Epidemiology Society (highest award), and the Stephen B. Hulley Award for Excellence in Teaching. His extensive teaching portfolio includes a series of courses in genetic and molecular epidemiology. He has mentored over 50 graduate students and postdoctoral fellows, serves on the executive committees of multiple graduate programs, and has directed a National Institutes of Health funded post-doctoral training program in genetic epidemiology for over 20 years. Recently appointed to the National Cancer Institute Board of Scientific Counselors, Dr. Witte has been continuously supported by the National Institutes of Health. -
Wing Hung Wong
Stephen R. Pierce Family Goldman Sachs Professor of Science and Human Health and Professor of Biomedical Data Science
Current Research and Scholarly InterestsCurrent interest centers on the application of statistics to biology and medicine. We are particularly interested in questions concerning gene regulation, genome interpretation and their applications to precision medicine.
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Xianghao Zhan
Ph.D. Student in Bioengineering, admitted Autumn 2019
Ph.D. Minor, Biomedical Data Science
Student Employee, DASHBioXianghao Zhan is a 5th -year Ph.D. candidate at Stanford Bioengineering. He obtained his M.S in Bioengineering in 2021 and his M.S in Statistics in 2023 both at Stanford. Before that he got B. Eng. in Control Science and Engineering (Automation) and his B. Art in English Language and Literature with Summa Cum Laude at Chu Kochen Honors College, Zhejiang University, China, in 2019.
Under the guidance of Prof. Oliver Gevaert and Prof. David B. Camarillo, he mainly focuses on the optimization of computational modeling of traumatic brain injury with machine learning and animal modeling based on biomechanical and radiological data. His research interests and projects also extend to the data mining of free-text clinical notes with natural language processing, biomedical data fusion for COVID-19 patient outcome prediction, machine learning reliability quantification with conformal prediction, reliability-based semi-supervised learning, and domain adaptation for biomedical sensory systems (with artificial olfaction systems and surface electromyography systems). He has published 18 peer-reviewed articles as a first/co-first author (IF 136.1) in such journals as NPJ Digital Medicine, IEEE Transactions on Biomedical and Health Informatics, IEEE Transactions on Biomedical Engineering, Journal of Sport and Health Science, with 4 first-author journal articles under review. He has been a peer reviewer for 16 journals including Annals of Biomedical Engineering, Journal of Neurotrauma, Computer methods in biomechanics and biomedical engineering.
In addition to his research, he has two master degrees while pursuing his Ph.D. degree: BIOE 2021 and STATS 2023. He has taken more than 10 data science and machine learning courses at Stanford with course project experiences and technical background with UNet-based image segmentation, BERT, Transformer-XL, DeepSEA, BPNet, VAE/SSVAE, flow model, energy-based model cycle-GAN, CNN-based image classification, LSTM-based clinical event prediction, Bi-LSTM-based neural machine translation, BERT, DCT/DWT/STFT, PCA, DRCA, NFL, convex optimization.
His research is recognized by the field and he was awarded with IET Postgraduate Research Award for an Outstanding Researcher (one awardee across the globe, first Chinese), Siebel Scholar Class of 2024, IET Healthcare Technology William James Award (one awardee across the globe), Stanford Interdisciplinary Graduate Fellowship (highest honor for interdisciplinary Stanford graduates), Pfeiffer Research Foundation Fellow, AMIA Trainee Award (six awardees, the only Chinese), American Society of Neurotrauma Trainee Award (20 awardees, the only Chinese), Chu Kochen Scholarship (12/23,000), Ten most Preeminent Students of Zhejiang University (10/36,000), Chinese National Scholarship (Top 0.2%).
He is dedicated to support underrepresented minorities. He has been a program leader for Stanford Summer Research Program and mentored 3 undergrads from the underrepresented minorities. He has been a research mentor at Foothill College for two years and mentored latino students from local community college. Additionally, he is a sports fan with 13 Stanford Intramural champions (10 volleyball, 3 tennis) and two medals from regional volleyball tournaments. He enjoys the sport passion and team spirits as a captain. -
Weiruo Zhang
Research Engineer, Biomedical Data Science
BioDr. Zhang is currently a Research Engineer at the Department of Biomedical Data Science and the Center for Cancer Systems Biology, Stanford School of Medicine. Dr. Zhang obtained her M.S. and Ph.D. in Electrical Engineering, both from Stanford University. Her Ph.D. studies focused on developing computational algorithms for metabolomics data analysis, in which she received Young Scientist Award from the Metabolomics Society for her algorithm on metabolic network analysis delineating the effects of genetic mutants and drug treatment on the metabolome. Her postdoctoral studies at the Department of Radiology, Stanford School of Medicine, integrated radiomic data and genomic data that identified a prognostic metabolic regulation biomarker for non-small cell lung cancer. Her current research primarily focuses on developing and implementing novel computational methods to integrate and analyze single-cell multi-omics data, such as single-cell RNA sequencing, spatial proteomics and spatial transcriptomics. She has developed algorithms to solve computational challenges of spatial omics data and to identify mediators for cell-cell interactions associated with metastasis that was featured in Stanford Medicine Magazine. Dr. Zhang has authored and co-authored publications including Nature, Cell, Nature Methods etc. Her research aims at bridging multi-omics, imaging, machine learning, artificial intelligence to better understand biology for cancer progression and immunosuppression.
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James Zou
Associate Professor of Biomedical Data Science and, by courtesy, of Computer Science
Current Research and Scholarly InterestsMy group works on both foundations of statistical machine learning and applications in biomedicine and healthcare. We develop new technologies that make ML more accountable to humans, more reliable/robust and reveals core scientific insights.
We want our ML to be impactful and beneficial, and as such, we are deeply motivated by transformative applications in biotech and health. We collaborate with and advise many academic and industry groups.
