Tanya P. Garcia, PhD
Excited about innovating practical and theoretically sound methods that address challenges in biomedical fields, Dr. Garcia has built a strong interdisciplinary research agenda. It involves national and international collaborations with neuroscientists and biologists, high-impact learning opportunities for students, and service work that promotes a future of diverse (bio)statisticians. Her research focuses on extracting maximal information from large, highly correlated data structures and has led to scientific discoveries in neurodegenerative diseases and the gut microbiome. Her teaching integrates these research activities with interactive, learner-centered projects that promote critical thinking. Her leadership in service activities involves promoting the success of underrepresented groups in (bio)statistics and providing her professional expertise regionally and internationally.
Dr. Garcia's research has attracted over $900,000 in competitive funding as Principal Investigator including grants from the NIH National Institute for Neurological Disorders and Stroke (NINDS) and Huntington's Disease Society of America. She publishes at a rate of 3 papers per year in top statistics journals including Journal of the American Statistical Association, Annals of Applied Statistics, and Bioinformatics. Her work is cited (inter)nationally by scholars in United States, France, Australia, Hong Kong, Canada, among others. She has earned competitive awards including the 2017-2018 NINDS Mentoring Institute for Neuroscience Diversity Scholars Fellowship; a fully-funded, visiting scholar invitation to the University of Sydney in 2012; and the 2011 American Statistical Association Gertrude M. Cox Award (awarded to two of 1200 applicants in North America). She has presented in over 35 invited department seminars and invited (inter)national conference sessions (rate of 5 per year).
Her research innovates new statistical methods that solve important neuroscience and biomedical problems and advances the underlying theory of those methods. Her work has contributed to four exciting areas: prediction models, model selection for high-dimensional data, regression models with measurement error, mean-covariance modeling for longitudinal data.
Honors and Awards
Plenary Speaker at the 39th International Symposium on Forecasting
Mentoring Institute for Neuroscience Diversity Scholar
Gertrude M Cox Awardee
2011, American Statistical Association
Longitudinal Data Analysis
High-Dimensional Variable Selection,
Longitudinal Data Analysis,
Associate Editor of Biostatistics, 2019-Present
- Huntington Disease Society of America Scientific Advisory Board, 2020-Present
- ENAR Regional Advisory Board, 2019-2021
Chair of ASA Biometrics Section Strategic Initiatives Grant, 2018-Present
ENAR Biometrics Section Representative, 2017-2018
- NSF CAREER Panel Reviewer, 2019
- NSF Statistics Panel Reviewer, 2019
- NSERC Mathematics and Statistics Discovery Program Reviewer, 2018
- E-Rare Transnational Grant Reviewer, 2018
- StatFest 2020
- Invited Session for the ISNPS 2018
- Invited Session for ENAR 2019
- Invited Session for Joint Statistical Meetings 2017
- Invited Session for Brazilian Regional Meeting 2016
- Invited Session for ENAR 2016
- Invited Session for SACNAS 2015
- Invited Session for WNAR 2015
Faculty Mentor for ADVANCE at Texas A&M University, 2019-Present
Dynamic landmark prediction for mixture models. Garcia, T.P. and Parast, L. (1970). Biostatistics.
Time-varying Proportional Odds Model for Mega-analysis of Clustered Event Times. Garcia, T.P., Marder, K. and Wang, Y. (2019). Biostatistics, 20(1), 126-146.
Disease progression in Huntington Disease: an analysis of multiple longitudinal outcomes. Garcia, T.P., Wang, Y., Shoulson, I., Paulsen, J.S., and Marder, K. (2018). Journal of Huntington Disease, 7(4), 337-344.
- PhD, Statistics, Texas A&M University, 2011
- MS, Statistics, University of Western Ontario, 2006
- MS, Industrial Engineering and Operations Research, UC Berkeley, 2005
- BS, Mathematics, UC Irvine, 2003