Rajita Ramesh Chandak

Rajita Ramesh Chandak serves as a Lecturer and Scientist at the Institute of Mathematics within the School of Basic Sciences at École Polytechnique Fédérale de Lausanne (EPFL). Currently holding a Bernoulli Instructorship, she will transition to an Assistant Professor position in the Department of Statistics at the University of Wisconsin-Madison starting Fall 2026.

Her academic foundation includes a PhD in Operations Research & Financial Engineering from Princeton University (2024), an MA in the same field (2021), and a BSc with Honors in Applied Mathematics and Economics from Brown University (2019). Her dissertation, 'Adaptive Nonparametric Statistical Theory and Implementation,' was supervised by Matias Cattaneo.

Chandak's research centers on mathematical statistics and theoretical machine learning, with emphasis on nonparametric estimation frameworks, statistical inference methodologies, and econometric applications. Her work bridges theoretical rigor with practical algorithm development, particularly in flexible function approximation and regression analysis.

Her 2025 publication on oblique regression trees demonstrates convergence rate analysis for complex function libraries, reflecting her focus on unifying nonparametric theory with machine learning implementation challenges across statistics and computer science domains.

Notable recognitions include the MVPM 2024 Scholar Award, an Annals of Statistics-invited paper at JSM 2025, the EPFL Bernoulli Instructorship, and multiple Princeton engineering awards. Professional grants include the Princeton SEAS Travel Grant and Bernoulli funding supporting her theoretical research.

She actively contributes to EPFL's academic ecosystem through graduate instruction in nonparametric methods and statistical inference, while collaborating with the Bernoulli Center on mathematical statistics initiatives and preparing for expanded research leadership at Wisconsin-Madison.