Hello,
I am Namita! I am currently finishing up my MA in Biostatistics at the University of California, Berkeley School of Public Health. My thesis research is focused on estimation of public health intervention effect on multivariate biomarker outcomes using a targeted learning framework (Van der Laan et al), and I am advised by Dr. Alan Hubbard.
Previously, as a Data Science Intern at Genentech, South San Francisco, I contributed to developing an R Shiny dashboard for interactive exploratory data analysis and modeling of early phase clinical trial data to assist in regulatory decision making. At Berkeley, I have also collaborated on a project with Dr. Elizabeth Purdom in the Department of Statistics, where I analyzed high dimensional RNA-Seq data from field droughted samples of Sorghum bicolor to study effects of drought and watering patterns at the molecular level. In addition to quality assessment and statistical analysis, I also actively maintained an extensive data pipeline using Git, Bash and Makefile.
In my first year at Berkeley, I also served as a student instructor for undergraduate probability and statistics courses with applied work in R software. I thoroughly enjoyed this experience of introducing students to the magical world of stats with a special focus on using statistics to solve problems in medicine and public health! In addition to teaching, I also mentored undergraduate students, especially first generation college students, and guided them in their application to graduate programs in quantitative sciences.
Prior to coming to Berkeley, I was a research scientist in New York City involved in the high-throughput discovery of new molecules for treatment of diseases such as cancer. I am a co-inventor on the patent for a theranostic antibody and a key contributor in a commercially licensed monoclonal antibody. I was fortunate enough to work on many exciting collaborative projects with physicians and scientists from Memorial Sloan Kettering Cancer Center, Weil Cornell Medicine and The Rockefeller University.
I bring the same diligence and meticulous approach to data science that I employed in my work as a bench-side researcher, and examine data with the same attention to detail. I am curiosity driven, and passionate about applied statistics in the field of biology, medicine and healthcare!
Interests
- Semi- and Non-Parametric Statistics
- Causal Inference
- High Dimensional Data
- Data Visualization
- R Software Development
- Data Communication
Connect with me on Linkedin!