BBB trainees contribute to all aspects of our research. Our fellows, many of them Intramural Research Training Award (IRTA) recipients, have experience and interests in various biostatistical and bioinformatic areas of study, from diagnostic methods to analysis tools for high-dimensional data.
Soutik Ghosal, Ph.D., joined BBB as a visiting postdoctoral fellow in November 2018. He currently works with Zhen Chen, Ph.D., on diagnostic accuracy modeling and biomarker analysis to develop parametric and semiparametric models to assess the diagnostic capacity of biomarkers to predict abnormal birth outcomes. He earned his Ph.D. in biostatistics from the University of Louisville in 2018. His doctoral dissertation focused on multiple research areas such as spatiotemporal modeling and causal inference.
Stephanie Guang is a Medical Research Scholars Program fellow working with Rajeshwari Sundaram, M.Stat., Ph.D., on using deep learning to model labor progression. Her research focus spans the fields of obstetrics and bioinformatics, specifically using electronic health record data to predict clinical outcomes in high-risk pregnancies. She is a fourth-year medical student at Brown University where, in 2016, she earned her bachelor’s degree in engineering and public health. She plans to train as an obstetrician/gynecologist and become a clinician scientist to use informatics to meet the growing needs and complexities of perinatal medicine.
Ruijin Lu, Ph.D., is a postdoctoral visiting fellow working with Zhen Chen, Ph.D., on developing Bayesian analyzing tools for high-dimensional and/or complex-structured data. Her research focus is hidden Markov models for learning latent classes or identifying subregions that drive the data. The models are used to understand how family relationship will affect the management of a child’s type 1 diabetes and will be applied on next-generation sequencing data. She completed her Ph.D. in statistics at Virginia Polytechnic Institute and State University in 2019.
Abhisek Saha, Ph.D., is a postdoctoral visiting fellow working primarily with Rajeshwari Sundaram, M.Stat., Ph.D. His research focuses on building novel statistical methodologies, modeling strategies, and algorithms, using penalized estimations for survival analysis with frailty, to study how toxic chemical exposure affects time to pregnancy (as in the Longitudinal Investigation of Fertility and the Environment study). He is involved in developing machine-learning techniques for studying data from continuous monitoring devices like actigraphy. He is also working with reproductive and perinatal epidemiologists, using collaborative data to implement causal models for identifying placental measurements related to long-term mortality and assessing the effect of term deliveries on children’s neurodevelopment. Prior to joining NICHD, he was a postdoctoral fellow with the University of Texas MD Anderson Cancer Center, where he developed Bayesian models for genomics data. These models are used to build precision medicine framework that combines -omics information across various model platforms to predict therapeutic effects on patients using underlying similarity of -omics structures. He received his Ph.D. in statistics from the University of Connecticut. His thesis focused on Bayesian joint modeling of Item Response Theory and response time in longitudinal education data.