School of Medicine
Showing 1-40 of 40 Results
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Corinne Beck
Program Manager, Center for Cancer Systems Biology (CCSB), Biomedical Data Science
Current Role at StanfordProgram Manager
Stanford Center for Cancer Systems Biology (CCSB)
Plevritis Lab
Department of Biomedical Data Science (DBDS)
School of Medicine | Stanford University -
Michelle Whirl-Carrillo
Principal Investigator and Director, PharmGKB, Biomedical Data Science
Current Role at StanfordPrincipal Investigator and Director, PharmGKB
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Francisco M. De La Vega
Adjunct Professor, Biomedical Data Science
BioFrancisco De La Vega is a distinguished geneticist and computational biologist, and an experienced technical executive, widely recognized for his expertise in clinical and population genomics, and bioinformatics. Currently serving as the Vice President of Hereditary Disease at Tempus Labs, Francisco is spearheading the development of comprehensive germline genetic tests and conducting innovative research into racial disparities in cancer leveraging Tempus’ multimodal Real-World Data. His work focuses on uncovering the connections between genetic ancestry and cancer genome mutational profiles that may help explain the differences in cancer incidence and outcomes across races and ethnicities. In addition to his role at Tempus Labs, Francisco is an Adjunct Professor in the Department of Biomedical Data Science at Stanford University School of Medicine and is a member of the Board of Directors of the International Society of Computational Biology, serving from 2022 to 2025.
Francisco teaches BIODS-235: "Best practices for developing data science software for clinical and healthcare applications" every Winter quarter. -
Li Gong
Scientific Data Curator 3, Biomedical Data Science
Current Role at StanfordI am a senior scientific curator at PharmGKB, and also serves as the program manager for the Stanford ClinGen team and coordinator for the ClinGen Pharmacogenomics Working Group.
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Fangqing Gu
Casual - Non-Exempt, Biomedical Data Science
BioFangqing (Fey) Gu is a dedicated and accomplished professional with a Bachelor of Arts in Psychology from the University of California, Davis. Fey's academic pursuits also include minors in Education, Communication, and East Asian Studies. As a member of Psi Chi and Phi Beta Kappa, Fey has consistently demonstrated a commitment to academic excellence and intellectual curiosity.
Fey has a strong research interest in cognitive psychology, particularly in the area of language development. This passion for understanding the intricacies of human cognition and communication has driven Fey to explore various facets of language acquisition, processing, and the cognitive mechanisms that underlie these processes.
Fey's work experience includes serving as a Research Assistant at the Stanford Psychophysiology Lab and the Social Inference Lab at UC Davis. In these roles, Fey excelled in recruiting and interviewing study participants, conducting literature reviews, and collecting data. Their strong analytical skills and attention to detail have contributed to the success of various research projects, particularly those related to language and cognitive development.
In addition to their research roles, Fey gained valuable experience in working with children through several hands-on positions.As a Psychology Assistant at the Educational Institute of Putuo District and Putuo Qixing School, Fey conducted assessments for incoming students with disabilities, evaluated students, and assisted in diagnostic and therapeutic procedures. As a volunteer Elementary Educator at Sunshine Cottage School for Deaf Children, Fey provided one-on-one tutoring for Hispanic children with hearing disabilities and facilitated team-building activities to maintain a comfortable environment. These experiences have honed Fey's ability to work effectively with diverse groups of children, fostering empathy, understanding, and a dedication to creating inclusive learning environments.
With strong interpersonal skills and a passion for understanding human behavior, especially in the realm of language development, Fey is poised for continued success at Stanford University -
Matthew Lungren
Adjunct Professor, Biomedical Data Science
BioDr. Lungren is Chief Data Science Officer for Microsoft Health & Life Sciences where he focuses on translating cutting edge technology, including generative AI and cloud services, into innovative healthcare applications. As a physician and clinical machine learning researcher, he maintains a part-time clinical practice at UCSF while also continuing his research and teaching roles as adjunct professor at Stanford University.
Prior to joining Microsoft, Dr Lungren was a clinical interventional radiologist and research faculty at Stanford University Medical School where he led the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). He later served as Principal for Clinical AI/ML at Amazon Web Services in World Wide Public Sector Healthcare, focusing on business development for clinical machine learning technologies in the public cloud.
His scientific work has led to more than 150 publications, including work on multi-modal data fusion models for healthcare applications, new computer vision and natural language processing approaches for healthcare specific domains, opportunistic screening with machine learning for public health applications, open medical data as public good, prospective clinical trials for clinical AI translation, and application of generative AI in healthcare. He has served as advisor for early stage startups and large fortune-500 companies on healthcare AI technology development and go-to-market strategy. Dr. Lungren's work has been featured in national news outlets such as NPR, Vice News, Scientific American, and he regularly speaks at national and international scientific meetings on the topic of AI in healthcare.
Dr. Lungren is also a top rated instructor on Coursera where his AI in Healthcare course designed especially for learners with non-technical backgrounds has been completed by more than 20k students around the world - enrollment is open now: https://www.coursera.org/learn/fundamental-machine-learning-healthcare -
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. -
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.