Data Scientists

Bren completed their MS in Statistical Data Science at San Francisco State University (SFSU). Prior to working at the QSU, they collaborated with faculty at SFSU and UCSF to develop novel feature selection methods for high-dimensional immune repertoire data. Their programming expertise are in R and Python.

Current Research Interests: Health Inequality; Pediatrics; Public Health; Nutrition

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Rafael joined the Quantitative Sciences Unit (QSU) in October 2024, after completing his MS in Statistics from the University of California, Santa Cruz. His research focused on tailoring clustering methods for geo-spatial compositional data. During his graduate studies, Rafael also interned at QSU, where he contributed to the Donor Heart Study group. Prior to his master’s, he worked as a Data Scientist in Brazil’s financial industry, focused on predictive modeling and machine learning. Rafael is passionate about R programming and enjoys building tools and visualizations that uncover important insights from complex data.

Current Research Interests: predictive modeling, machine learning, visualization tools, Bayesian inference

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Kristen received her PhD in Biostatistics from University of Minnesota in 2015. Kristen Cunanan joined the QSU in July 2018. Prior to joining the QSU, she was a Research Scholar at Memorial Sloan Kettering Cancer Center. 

Current Research Interests: Nutrition; Oncology; Clinical Trials

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Himani has graduated from Loyola University Chicago with MPH degree. During medical school, she focused on healthcare data and research. Her previous experiences include collaborating with research professionals in the areas of pediatrics, oncology, cardiology, maternal and child health, infectious disease and many more. Her passion is to blend clinical knowledge into statistical aspects and give meaning inferences to the available data.

Current Research Interests: Oncology; Clinical Trials; Digital Health 

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After completing her M.S. in Biostatistics at the University of Washington, Victoria continued her training at the QSU under a vibrant cohort of senior staff. Since then, she has valued the opportunity to approach research questions through her collaborators’ clinical lens and practice the late musician Charles Mingus’s perspective on creativity as “making the complicated simple” in a team science setting. Besides improving disease management, she is interested in preventive medicine and research that extends health span. Between workdays, she enjoys wandering and wondering across varied terrain.

Current Research Interests: Methods for Longitudinal Data; Risk Prediction; Data Visualization

Ariadna joined the QSU in January 2016. She completed her M.S. in Management Information Systems at the University of Illinois at Chicago. She is a seasoned data scientist with a passion for unraveling complex datasets and transforming them into actionable insights. Ariadna has successfully applied her skills to various research projects. She is also a seasoned programmer in many languages, including Python and R, and has developed many innovative algorithms and predictive models.

Current Research Interests: Data Visualization; Public Health; Cardiology; Medical Informatics

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Anna is a senior research data analyst for the QSU, joining the group after working for 3 years at the Stanford Research Informatics Center, where she gained experience with extracting data from Clarity and the OMOP CDM, working with researchers throughout the Stanford SoM., Anna obtained her PhD in healthcare operations research from the University of Toronto, focusing on Canadian primary care services.

Current Research Interests: AI and Machine Learning Methodologies; Breast and Lung Cancer Research; Delivery of Timely, Equitable and Efficient Healthcare; Bioinformatics and Biomedical Data Analysis

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Suni received her PhD in Biostatistics from the University of Colorado - Anschutz Medical Campus, where her graduate work focused on functional data analysis and specifically on function on scalar regression. Prior to returning to school to continue her education she provided statistical and data management support for multiple public health research studies focusing on physical activity and its benefits for successful aging and quality of life for cancer survivors.

Current Research Interests: Functional Data Analysis; Longitudinal Data Analysis; Machine Learning

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Bo graduated from New York University with a Master’s Degree in Biostatistics. Prior to NYU, he worked as a Data Analyst in heath-related domain. After graduation, Bo continued his research at NYU School of Medicine as a research scientist. 

Current Research Interests: Clinical Trials; Survival Analysis; Longitudinal Analysis; Biomarkers 

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Yuan joined the QSU in December 2023, bringing with her a Ph.D. in Biostatistics earned from George Washington University. Her research interest is centered on machine learning and deep learning, nonparametric modeling, survival analysis, time series analysis, and clinical trials across various medical domains including oncology, cardiology, neurology etc. Her programming expertise is in R and Python. 

Current Research Interests: Machine Learning and Deep Learning; Nonparametric; Survival Analysis; Clinical Trials

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Dr. Le Guen received his PhD in 2018 at the University Paris-Saclay. While at Stanford University, he was awarded a highly selective European fellowship (Marie Skłodowska-Curie Actions, 2019 call. He works on integrating genomics data (whole-genome sequencing, transcriptomics, proteomics and other omics data) with deep phenotyping of Alzheimer' disease cases and cognitively unimpaired individuals (clinical characterization, blood and CSF biomarkers, as well as PET/MRI).

Current Research Interests: Bulk and Single-Cell RNA Sequencing (Transcriptomics); Proteomics; Neurology; Aging 

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Fatma joined the QSU in December of 2022. Prior to joining the QSU, she was a senior biomedical analyst at the University of Tennessee Health Science Center, Center for Biomedical Informatics. She got her PhD in statistics from Dokuz Eylul University, Turkey in 2019.  Her research interests include predictive modeling, signal analysis, medical image analysis, statistical modeling, machine learning, deep learning, and data visualization for clinical decision making.

Current Research Interests: Machine Learning; Medical Image Analysis; Predictive Modeling; Data Visualization

Stanford Who Profile

Dr. Hedlin joined the QSU in 2013 after completing her PhD in Biostatistics from Johns Hopkins in 2011. In her work, she has contributed to medicine and public health research on women's health, cardiovascular and pulmonary diseases, and data safety monitoring. She enjoys mentoring clinicians, researchers, and students who are interested in learning statistics. 

Current Research Interests: Data and Safety Monitoring; Clinical Trials and Data Coordinating Center Implementation; Real World Data; Clustered and Longitudinal Data Analysis for Repeated Measures

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Alex joined the QSU in October 2022. She received her Ph.D. in Biostatistics from the University of Colorado – Anschutz Medical Campus, where her graduate work focused on applications of graph theory and kernel machine methods to neuroimaging data. While a graduate student, she participated in projects ranging from the creation of pre-processing pipelines for brain MRI to using large administrative databases to study health disparities and outcomes. 

Current Research Interests: Medical Image Analysis; Graph Theory/Network Analysis; Agreement Statistics/Interrater Reliability; Health Services Research and the Use of Administrative Databases

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Yinyao joined the QSU in April 2024. She got her master’s degree in Biostatistics from Duke University in 2019. Prior to joining the QSU, she worked as a Statistician Investigator at the University of North Carolina at Chapel Hill where she focused on advancing the understanding of processes mediating trauma recovery.

Current Research Interests: Longitudinal data analysis, predictive modeling, data visualization

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Justin joined the QSU in September 2016 after receiving his MPH degree from the University of Miami. During his graduate studies, he worked as a Graduate Research Assistant for UM’s Clinical and Translational Sciences Institute, providing data management and analysis for a Zika virus research project using SAS, R, and ArcGIS programs. He is interested in applying statistical methods to a wide variety of research topics.

Current Research Interests: Shiny Dashboards; Parallel Computing; Big Data; Public Health

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Su Jin joined the QSU in November 2023. Prior to joining the QSU, she was a biostatistician in the Quantitative Sciences Division at the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, collaborating with investigators to provide statistical and programming support for their early phase clinical trials and cancer research. She received her ScM in Biostatistics from Johns Hopkins University in May 2018.

Current Research Interests: Clinical Trial Design; Machine Learning; Medical Image Processing; Data Visualization

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Yuhan joined the QSU in March 2021. She got her master’s degree in Biostatistics from Duke University. After that and prior to joining the QSU, she worked as a biostatistician at the Vanderbilt University Medical Center, where she worked on ophthalmology and pediatrics projects.

Current Research Interests: Survival Analysis; Claims Data; Longitudinal Analysis; Oncology

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Di joined the QSU in February 2020. She completed her Master in Biostatistics at Duke University. Prior to joining the QSU, she was a biostatistician at the Duke Clinical Research Institute, while working on national, multi-center registry projects. She is interested in searching for the optimal treatment and essential factors associated with improved clinical outcomes.

Current Research Interests: Survival analysis; Longitudinal data analysis; Clinical trials; Propensity score methods

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Rong joined the QSU in 2020. She received her PhD in Biostatistics from The Ohio State University (OSU) in 2016 Rong has experience of analyzing large-scale electronic medical records, National Cancer Database, RNA expression microarray data, GTEx RNA-seq, HuProt protein array, cytokine-chemokine expression, and autoantibody profiles.

Current Research Interests: Random-forest-based Feature Selection Methods; Correlated Feature Selection Methods; Clinical Trial Design; Simulation Based Power Analysis

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Ingrid joined the QSU in April 2023. She completed her M.S. in Biostatistics at the University of Washington. During her graduate studies, she completed two internships in the biotech companies that focused on genomic data analysis. She was also involved in collaborative research projects focused on oncology and animal health. Her programming expertise includes R and SAS.

Current Research Interests: Oncology; Aging; Infectious disease; Omics Data

Stanford Who Profile

Andy brings a diverse background of applied science and technology to QSU.  He completed his PHD at UC Berkeley studying protein conformational dynamics and unnatural amino acid expression.  He then moved toward data science and engineering as Head of Informatics at Kalypsys, Inc. and as co-founder and COO of Scientist.com.   

At Stanford, he has more than 14 years experience enabling investigator-led clinical studies and trials, building secure, HIPAA-compliant data collection systems, overseeing REDCap, and leading a large consulting team for study design, software engineering, and cloud infrastructure.

Andy played significant roles in the Apple Heart Study, the CATCH Covid study and VERA platform, CA Facts, SnapDX, the Stanford Research Registry, the LEA&RN Registry, the OurVoice platform, and dozens of other Stanford-led research projects.

Current Research Methods: Electronic data capture, Cloud and systems engineering, Building cool tools to enable great research

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Emily received her Ph.D. in Computational Bioscience from the University of Colorado Anschutz Medical Campus. Her thesis work focused on high dimensional mediation methods and their application to transcriptomic data. She also focused on data visualizations to aid in mediation result interpretation. Additionally, Emily has participated in a variety of projects ranging from COPD biomarker discovery using multi-omic networks to scoring bronchiectasis in lung imaging using machine learning methods.

Current Research Methods: ECausal inference, data visualization, and multi-omic data analysis.

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As a statistical generalist, she has over 20 years’ experience in public health, implementation science, psychometrics, survey construction, and a range of experimental and observational study designs. She is a practitioner and strong proponent of data visualization in the scientific process, and lectures often on the topic, as well as maintaining an artistic data visualization practice in her personal life. Kate loves excellent measurement methods, and infuses her daily work with the joy of discovery. She holds a PhD in demography from the University of Pennsylvania and a Master’s in Public Health from Columbia University.

Current Research Interests: Data Visualization; Oncology; Clinical Trials; Maternal and Child Health

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After completing an internship at QSU, I joined the team full-time in 2024. I hold a master's degree in Statistics from SJSU and an undergraduate degree in Physics. I am proficient in programming languages such as R and Python. My previous projects have been on machine learning, statistical analysis and generative AI.

Current Research Interests: Generative AI, Machine Learning and Deep Learning, Time Series Analysis, Stochastic Modelling, Predictive Modelling, Big Data and Messy Data, Statistical Analysis and Inference, Bioinformatics

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FeiFei joined the QSU in 2014 after completing her M.P.H. in Epidemiology and Biostatistics from UC Berkeley. During her graduate studies, she worked on a data analysis project evaluating provider adherence to tuberculosis treatment guidelines at the California Department of Public Health and is familiar with SAS and Stata.  She has contributed to research in women’s health, cardiovascular diseases, and also has experience with data safety and monitoring in clinical trials. 

Current Research Interests: Cardiovascular health; Longitudinal studies; Data visualization

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Victor joined the QSU in February 2022. He received his Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill in August of 2021. He developed methods for time-to-event analyses under complex sampling designs and has collaborations in cardiology, bone diseases, general pediatrics, pediatric neurology, newborn medicine, developmental and behavioral pediatrics, parenting, and child abuse. He has a particular interest in analyses of national health surveys, newborn medicine, and hospital pediatrics.

Current Research Interests: Pediatrics; Survey Methods; Clinical Trials; Maternal and Child Health

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Nidia is a physician, epidemiologist, and public health advocate. She received her PhD in Epidemiology from the University of North Carolina at Chapel Hill in 2022 and joined the QSU in May 2023. Nidia has comprehensive experience in various healthcare systems and academic settings, including clinical practice in primary care and rural health, teaching, and collaborative research with a public health focus.

Current Research Interests: Epidemiologic Methods; Healthcare Databases; Clinical Trials; Data Visualization & GIS in Public Health

Stanford Who Profile

Keejeong completed M.S. in Biostatistics at the University of Michigan—Ann Arbor, and she also has a PharmD from South Korea. During the graduate studies, her research was focused on developing computational tools, and statistical methods for integrative GWAS analysis as well as visualization methods for genetic data.

Current Research Interests: Longitudinal Data Analysis; Bioinformatics; Clinical Trials; Data Visualization

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Blake joined the QSU in August 2022. He completed his MS in Biostatistics at UC Davis. Prior to his graduate studies, he worked as the data manger for a human development research project that studied how stress effects the development of emotion regulation in Mexican origin children. His master’s thesis focused on using blood biomarkers and PET scans to develop statistical models that predicted subject’s likelihood of having fatty liver disease.

Current Research Interests: Public Health; Nutrition; Biomarkers; Survival Analys

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Sa Shen received her Ph.D in statistics from the University of Pittsburgh. She has more than 20 years’ collaborating experience in working with researchers, scientists, physicians and clinicians on grant proposals and funded projects. Sa has worked in a wide range of fields, such as chronic disease, mental health, psychology, neuroscience, epidemiology, nursing, pediatrics, health technology, and social work. She served as a co-investigator or a lead biostatistician on several federal and foundation funded observational studies and randomized controlled trials. In addition, she has coauthored more than 60 peer-reviewed journal articles.

Current Research Interests: Longitudinal Data Analysis; Randomized Controlled Trials; Propensity Score Matching; Causal Inference

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Chloe Su earned her PhD in Epidemiology & Clinical Research at the Stanford University School of Medicine, and received her B.S. in Molecular, Cell and Developmental Biology with a Minor in Biomedical Research from UCLA. As someone with a family history of cancer, Chloe is passionate about improving early cancer detection and accelerating development of cancer treatments to improve the lives of cancer patients and their families, specifically through appropriate utilization of real-world data to complement randomized controlled trials. During her graduate training, she has worked with a variety of data types (clinical trial, EHR, registry and claims) to study the epidemiology of advanced lung cancer and evaluate surveillance strategies for second primary lung cancer. Prior to Stanford, Chloe was a clinical trial project and data manager at UCLA Health, where she managed studies in early lung cancer detection and led a team to curate data for an integrated EHR database for lung cancer. Chloe has also previously worked at ZS Associates and Genentech, Inc., using innovative trial designs and real-world evidence to accelerate clinical development in oncology.

Current Research Interests: Causal inference, real-world evidence, integrating real world evidence with randomized controlled trials

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Megha joined the QSU in January 2022. She completed her MPH in Epidemiology and Biostatistics at the University of Southern California (USC). Prior to joining the QSU, Megha was a biostatistician at the University of California, Los Angeles (UCLA) working on various research projects in the Department of Urology and OBGYN. Her programming expertise includes SAS and STATA.

Current Research Interests: Public Health; Maternal and Child Health; Nutrition; Sports Medicine

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Isabel joined the QSU in 2017 after working at Stanford Surgery Policy Improvement Research & Education center to develop prediction models for surgery-related outcomes using data from the Electronic Health Records. She has been actively collaborating with clinical researchers and local public health agencies to deliver best statistical practices that facilitates the integration of knowledge from real-world evidence in clinical and public health researches. 

Current Research Interests: Quasi-experimental Design and Causal Inference; Pragmatic Clinical Trials; Real-world Evidence Analysis Using Electronic Health Records, Registry and Claimed-Based Databases; Artificial Intelligence in Learning Health Systems

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Amy joined the QSU in April 2022. She completed her MPH in Global Epidemiology at Emory University, during which she supported malaria prevention efforts as a Graduate Research Assistant at the Centers for Disease Control and Prevention. Prior to joining the QSU, Amy continued on at the CDC as an ORISE Fellow, collaborating with clinicians on spina bifida research in the Division of Birth Defects and Infant Disorders.

Current Research Interests: Pediatrics; Ob/Gyn; Survival analysis; Data visualization

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Kenny Zhang joined the QSU in November 2020. He graduated from Duke University with a master’s degree in Biostatistics.  At Duke, he worked as an intern biostatistician at Duke Cancer Institute on two large multi-center cancer trials. His graduate work focused on statistical analysis of biomarker and validation of prognostic and predictive models. His programming expertise includes: R, SAS, SQL and Python. 

Current Research Interests: Pediatrics; Cardiovascular; Renal; Palliative Care

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Mengrui joined the QSU in 2024. He received his Ph.D. degree in Statistics from the University of Georgia in May 2022. Mengrui’s research focuses on leveraging advanced statistical and machine-learning techniques to extract meaningful insights from complex biological datasets. He is interested in developing new methods, tools, and pipelines for various kinds of biological datasets, especially in single cell, RNA-Seq, metagenomics, and proteomics to support drug discovery and development.

Current Research Methods: Bioinformatics, Microbiome & Metagenomics, Deep Learning/Machine Learning

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