Stanford CARE Team Science Fellowship 2023
About the Program
The Team Science Fellowship is a one-year fellowship program for doctoral students and post-doctoral researchers who want to develop their skills as program leaders. We select 3-5 Team Science Fellows per year, with backgrounds in biostatistics, computer science, qualitative science, and epidemiology. Team Science Fellows support our Stanford CARE Scholars teams in all aspects of their research projects and help get their work into print. Team Science Fellows have an additional curriculum to prepare them to lead teams, with seminars on teaching, mentorship, team science. Fellows help prepare datasets, learn about specific analytic techniques for national databases, are heavily involved in teaching the CARE Scholars core curricula, and participate in innovation seminars with leaders in business, technology, medicine, health policy, and scientific communications.
Applications for our 2024 Cycle Open: Winter 2023
Interested candidates should apply through our application portal above (currently closed)
Please see our FAQ or contact us at carescholars@stanford.edu
Alice Guan, MPH
University of California, San Francisco
Alice (she/her/hers) is an epidemiology doctoral candidate at the University of California, San Francisco. Her research examines how social and economic policies, neighborhood context, and structural factors shape health inequalities across the life course. Outside of work, she enjoys plants, cats, and live theater.
Alyssa Columbus, MS
Johns Hopkins University
Alyssa Columbus (she/her/hers) earned an MS in Applied and Computational Mathematics from Johns Hopkins University in 2022 and is currently a first-year Biostatistics PhD student and Vivien Thomas Scholar at the Johns Hopkins Bloomberg School of Public Health. Alyssa is the author of many technical guides and tutorials on data science that have been published by Forbes and Microsoft, among others, and she has given 15 invited talks, keynotes, or panels on statistics and careers in this area. She has worked professionally as a data scientist, information security analyst, and consultant, and her article titled "Gender Equity in Digital Health: AI as a Double-Edged Sword" was featured by Significance magazine. As an undergraduate student, Alyssa founded R-Ladies Irvine, and she was selected to become a member of the Spring 2018 Class of NASA Datanauts. She has mentored numerous statistics and data science professionals worldwide.
Miguel Esteban Villarreal Rodriguez, MD
Stanford University
Miguel (he/him/his) is a Physician (MD) with a Minor Degree in Psychology from Universidad de los Andes (Colombia), currently enrolled in the Master in Epidemiology and Clinical Research program at Stanford University. He has specific interests in Mental Health, and use of machine learning techniques in Medical Research. Miguel’s research experience is mostly centered on Mental health in Children, Adolescents and Young Adults which has included developing AI assisted systems for early detection of mental health issues in school children and describing Mental Health Issues prevalence among LGBTQI Medical students. His new research horizons include the development of a Deep Learning Hierarchical Framework for prediction of Burnout in physicians using Raw Audit Log Data.
Xinran Qi
Johns Hopkins University
Xinran (she/her/hers) is a MHS candidate specializing in General Epidemiology and Methodology at Johns Hopkins Bloomberg School of Public Health. She is deeply committed to advocating for the health of Asian populations and addressing the health disparities facing diverse global challenges. Xinran finds inspiration at the intersection of clinical medicine, public health, and technology, which she believes can contribute to translational medicine. She is eager to contribute her skills and knowledge to improve health outcomes for underserved populations with a strong desire to continue expanding her understanding of health equity, clinical research, study design, and their implementation on a global scale. Beyond academics, Xinran enjoys cooking, swimming, and reading poems. She is also crazy about orchestra music.
Tina Cheng, MPH
Duke University
Tina Cheng (she/her/hers) is a second year doctoral student at the Department of Population Health Sciences at Duke University. Trained as a mixed-method epidemiologist, Tina’s research focus on combating health inequities through patient-centered care and shared-decision making with social determinants of health in mind. While she has vast experiences from working with national large health datasets, building statistical models, to conducting qualitative interviews, Tina’s current research focus lies in preference research exploring the trade-offs attached to health decisions and the interface of machine learning and healthcare.
Steve Asch, MD
Vice-Chief for Research
Primary Care and Population Health
Stanford School of Medicine
Ruth O'Hara, PhD
Senior Associate Dean for Research
Stanford School of Medicine
Rita Popat, PhD
Co-Director
Spectrum NIH TL1 Program
Stanford School of Medicine
Eldrin F. Lewis, MD, MPH
Professor, University Med Line
Stanford School of Medicine
P.J. Utz, MD
Program Director
DOM Team Science Initiative
Nigam Shah, MBBS PhD
Cheif Data Scientist
Stanford Healthcare
Joe Wu, MD, PhD
Director
Stanford Cardiovascular Institute
Kevin Grimes, MD PhD
Director, SPARK Translational Research Program,
Stanford School of Medicine
