NISS Graduate Student Network Research Conference 2024

Saturday, May 18 & Sunday, May 19, 2024

The NISS Graduate Student Network (GSN) is very excited to once again announce our 4th annual two-day Graduate Student Network Research Conference. Last year's conference in 2023 was a resounding success with many fantastic presentations and selecting winners was quite a challenge! This year's conference will be a two-day event and take place on Saturday, May 18 and Sunday, May 19, 2023, from 12pm - 5 pm ET each day.

Career Panels:

  • Statistical Careers in Academia
  • Statistical Careers in Industry and Government

The GSN Research Conference will feature graduate student presentations and two keynote speakers on Day 1. During the conference, 4 graduate students will be recognized with the best presentation awards based on the content and the quality of their presentation.

Keynote Speakers: Causal Inference 

  • Miguel Hernán, Harvard University
  • Razieh Nabi, Emory University


Register on Eventbrite


Call for Abstracts

Submit an abstract to participate in the graduate student research conference as a presenter! 


Submit Your Abstract


Graduate Student Presentations

Students will present either an oral presentation or a poster presentation at this conference within the following categories:

  • Original Research (their own research work),
  • Literature Research (presentation of a published paper that is not authored by the presenter), or
  • Literature Review (presentation on recent developments in an area -- this would be a chance to present a couple of papers to highlight recent developments in an area of interest.)

Selected oral presentations will involve a 20 minute live presentation including 5 minutes of Q&A.

About Speakers

Miguel Hernán uses health data and causal inference methods to learn what works. As Director of the CAUSALab at Harvard, he and his collaborators repurpose real world data into scientific evidence for the prevention and treatment of infectious diseases, cancer, cardiovascular disease, and mental illness. This work has shaped health policy and research methodology worldwide. Miguel is co-director of the Laboratory for Early Psychosis (LEAP) Center, principal investigator of the HIV-CAUSAL Collaboration, and co-director of the VA-CAUSAL Methods Core, an initiative of the U.S. Veterans Health Administration to integrate high-quality data and explicitly causal methodologies in a nationwide learning health system. As the Kolokotrones Professor of Biostatistics and Epidemiology, he teaches at the Harvard T.H. Chan School of Public Health, where he has mentored dozens of trainees and students, and at the Harvard-MIT Division of Health Sciences and Technology. His free online course “Causal Diagrams” and book “Causal Inference: What If”, co-authored with James Robins, are widely used for the training of researchers. Miguel has received many awards for his work, including the Rousseeuw Prize for Statistics, the Rothman Epidemiology Prize, and a MERIT award from the U.S. National Institutes of Health. He is Fellow of the American Association for the Advancement of Science and the American Statistical Association, Associate Editor of Annals of Internal Medicine, Editor Emeritus of Epidemiology, and past Associate Editor of Biometrics, American Journal of Epidemiology, and Journal of the American Statistical Association. He often serves on committees of the U.S. National Academies.”

Razieh  Nabi is a Rollins Assistant Professor at the  Biostatistics and Bioinformatics at Emory University. Drawing valid causal conclusions from data is impeded by various factors such as the presence of unmeasured confounders, curse of dimensionality, missing and censored values, measurement error, social contagion, network interference, and data that reflect historical patterns of discrimination and inequality. The focus of my research is the development of novel causal methodologies to address these pressing challenges. Her research draws on methodological insights from both machine learning/artificial intelligence, especially using graphical models, and statistical theory, especially semiparametric statistics. My applications of interest include healthcare, social justice, and public policy.

Event Type


NISS Graduate Student Network
National Institute of Statistical Sciences