ANR JCJC Grant Awarded to Tat-Thang Vo
A young researcher grant obtained on the very first submission
We are delighted to announce that Tat-Thang Vo, researcher in the Epilogy team, has been awarded an ANR JCJC grant from the French National Research Agency, on his very first submission.
The JCJC instrument (Jeunes Chercheuses et Jeunes Chercheurs) of the ANR Generic Call for Proposals supports young researchers who defended their PhD less than ten years ago in leading their own project. It aims to help them gain scientific autonomy, develop their own research theme and build a team around it, and it is often a springboard towards a European Research Council (ERC) application. Selection is highly competitive and takes place in two stages: in the 2026 call, only 898 of the 2,271 eligible JCJC pre-proposals were invited to submit a full proposal.
The SDID project aims to develop novel causal inference methods combining survival analysis and the Difference-in-Differences approach, in order to evaluate health policies and treatments from observational health data such as the French National Health Data System (SNDS), particularly in the presence of unmeasured confounding.
About Tat-Thang Vo
Tat-Thang Vo is a junior professor of biostatistics at the Mondor Institute of Biomedical Research (INSERM U955), University Paris-Est Créteil. Previously, he obtained his PhD in biostatistics jointly from Ghent University (Belgium) and Université Paris Cité (France). From 2020 to 2023, he held a postdoctoral fellowship in the Wharton Statistics & Data Science Department, University of Pennsylvania (USA). Dr. Vo is the recipient of the 2019 Norman Breslow Award for Young Investigators from the American Statistical Association, and the 2018 Young Investigator Award from the International Society for Clinical Biostatistics.
His research focuses on the development of novel causal inference methods for data fusion, mediation analysis, evidence synthesis, and health technology assessment. A key feature of his work is the use of semiparametric theory to develop flexible, data-adaptive estimation strategies for causal parameters, with applications to clinical questions in medicine, psychology, and pharmacoepidemiology.
Congratulations Tat-Thang!