My research focuses on developing computationally efficient novel statistical methods that are capable of handling structurally complex datasets. The broader theme of my research is a confluence of Bayesian Statistics and Machine Learning.

My research interests include:

  • Latent Factor Models

  • Network analysis

  • Nonparametric Statistics

  • Recommender Systems

  • Reinforcement Learning.


Environmental Impact of the COVID-19 Induced Lockdown Measures on the PM2.5 Concentration in USA

Rahul Ghosal and Enakshi Saha

Atmospheric Environment (2021)

Dynamic Sparse Factor Analysis

Enakshi Saha, Veronika Rockova and Kenichiro McAllin

Journal of Applied Econometrics (in Revision), (2021+)

On Theory for BART

Veronika Rockova and Enakshi Saha

The 22nd International Conference on Artificial Intelligence and Statistics. PMLR (2019)

Some High-dimensional One-sample Tests Based on Functions of Interpoint Distances

Enakshi Saha, Soham Sarkar and Anil K. Ghosh

Journal of Multivariate Analysis 161 (2017): 83-95.

Consulting Experience

Effect of Hunger on Moral Development in Children

with Elizabeth Huppert & Jean Decety, Department of Psychology, The University of Chicago

Role of Gene Tbx5 in Formation of Forelimb in Zebrafish

with Erin Boyle Anderson & Robert K. Ho, Department of Organismal Biology and Anatomy, The University of Chicago

Moderated Mediation Analysis of Moral Communication and Moral Hypocrisy

with Elizabeth Huppert, Emma Levine & Jean Decety, Department of Psychology, The University of Chicago


Human Activity Recognition Using Smartphones (2016)

Data Analysis Project Report (STAT 349), The University of Chicago

Some Multi-sample Run Tests Applicable to High Dimension Small Sample Size Data (2014)

with Anil K. Ghosh, Indian Statistical Institute, Kolkata