Scholar
Michele Santacatterina
Google Scholar ID: mZPN7H4AAAAJ
NYU Grossman School of Medicine
Citations & Impact
Citations
809
H-index
15
i10-index
25
Publications
20
Co-authors
1
list available
Publications
Resume (English only)
Academic Achievements
- Published multiple academic papers, including 'Modern causal inference approaches to improve power for subgroup analysis in randomized controlled trials' (Under Review), 'Identification and estimation of causal effects using non-concurrent controls in platform trials' (Statistics in Medicine, 2025), 'Non-parametric efficient estimation of marginal structural models with multi-valued time-varying treatments' (Under Review, 2024), 'Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models' (NeurIPS 2024, 2024), etc.
Research Experience
- Previously, an Assistant Research Professor of Biostatistics at the George Washington University School of Public Health, and a postdoctoral associate at the Cornell TRIPODS Center for Data Science and Cornell Tech, mentored by Dr. Nathan Kallus and Prof. Thorsten Joachims. Currently, a Research Advisor at Google DeepMind, working with Ramin Zabih. Served as a Visiting Researcher at Google from 2022 to 2025.
Education
- PhD in Biostatistics from Karolinska Institutet in 2018; Research assistant at Karolinska Institutet in 2014; MSc in Biostatistics from University of Milano-Bicocca in 2012; BSc in Statistics and Computer Science from University of Padua in 2009.
Background
- Assistant Professor of Biostatistics at the NYU Grossman School of Medicine. Research interests include causal inference, randomized experiments, causal machine learning, and causal AI.
Miscellany
- Other interests not detailed.
