Rajeshwari Sundaram, M.S.,Ph.D.

Senior Investigator

Biostatistics & Bioinformatics Branch


6710B Rockledge Drive
Room 3232
Bethesda 20892-7004



Research Topics

Dr Sundaram’s research is at the intersection of statistical methods and public health. Her interests include development of statistical methods for survival outcomes, especially multivariate survival outcomes and recurrent events, joint modeling of longitudinal data and survival data and hierarchical data with applications to reproductive outcomes, pediatrics and obstetrics. She is also interested in developing statistical methods for assessing the effects of chemical mixtures on health outcomes, especially pertaining to reproductive and child development, as well as methods for analyzing exposome type of data. Over past few years, she has worked to develop statistical methods aimed at building biologically meaningful models to better assess fecundity and fertility, with a focus on building individualized risk prediction for conception delay and infertility, issues of considerable interest due to the changing profile of couples attempting pregnancy for the first time. She is also currently interested in contributing to better guidelines for labor management in pregnant women by developing methods to address labor progression and by assessing labor arrests and other factors leading to medical intervention.


Rajeshwari Sundaram, M.Stat., Ph.D., is a senior investigator in the Biostatistics and Bioinformatics Branch since 2014. Dr Sundaram received her bachelor’s degree in Mathematics (Honors) from Calcutta University and a master’s degree in Applied Statistics and Data Analysis from Indian Statistical Institute, Calcutta, India. She received her Ph. D. in Statistics from Michigan State University.

Selected Publications

  1. Sundaram R, Mumford SL, Buck Louis GM. Couples' body composition and time-to-pregnancy. Hum Reprod. 2017;32(3):662-668.

  2. Lum KJ, Sundaram R, Buck Louis GM, Louis TA. A Bayesian joint model of menstrual cycle length and fecundity. Biometrics. 2016;72(1):193-203.

  3. Lum KJ, Sundaram R, Louis TA. Accounting for length-bias and selection effects in estimating the distribution of menstrual cycle length. Biostatistics. 2015;16(1):113-28.

  4. McLain AC, Sundaram R, Buck Louis GM. Joint analysis of longitudinal and survival data measured on nested timescales by using shared parameter models: an application to fecundity data. J R Stat Soc Ser C Appl Stat. 2015;64(2):339-357.

  5. Sundaram R, McLain AC, Buck Louis GM. A survival analysis approach to modeling human fecundity. Biostatistics. 2012;13(1):4-17.

This page was last updated on September 26th, 2018