Understanding the effects of environmental exposures on child health and development is crucial to promote positive health outcomes in adulthood. To advance knowledge in this area, our lab is part of the NIH-funded Environmental influences on Child Health Outcomes (ECHO) Program. As of April 2021, the cohorts have collected data from over 90,000 participants which includes over 57,000 children. The selected student(s) will be involved in projects investigating the relationship between a variety of prenatal and postnatal exposures and physiological variables. Specifically, we aim to build multidimensional associative and predictive models to investigate the effects of prenatal maternal drinking and smoking on development of cardiac and neural systems non-invasively assessed at 4-, 5-, 7-, 9- and 11-years of age. We will advance prior work on the relationship of maternal depression and child development by including depression measures at 1 and 4 years post-delivery and prospective assessment of multiple domains of child development and applying machine learning methods for trajectory identification.

To achieve this goal, the student(s) will be involved in the activities required to design models, e.g., data cleaning and preprocessing, dataset dimensionality reduction, identification of exposure, outcomes, and covariates of interest and interpretation of results.

This project is eligible for a matching fund stipend from the Data Science Institute. This is not a guarantee of payment, and the total amount is subject to available funding.

Faculty Advisor

  • Professor: William P. Fifer, Michael M. Myers
  • Center/Lab: Fifer Lab
  • Location: Pardes Building, 4th floor, NYSPI
  • Dr. Fifer’s research interests focus on fetal and neonatal behavioral, physiological and central nervous system development. Current investigations in his laboratory include studies of fetal, newborn and premature infant neurobehavioral responses to environmental stimulation during sleep and the effects of prenatal exposures on later neurodevelopment.

Project Timeline

  • Earliest starting date: 9/6/21
  • End date:
  • Number of hours per week of research expected during Fall 2021: ~15
  • Number of hours per week of research expected during Summer 2022: ~15

Candidate requirements

  • Skill sets: basics of coding (any coding language, R, SAS and python for example), basics of statistical methods, basics of machine learning
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes