I am PhD student at the Statistics Department at Columbia University. I am fortunate to be advised by Professor Arian Maleki and Professor Daniel Hsu.
I am interested in understanding information theoretic and computational phenomena that arise in modern statistical inference problems using tools from information theory, applied probability and statistical physics.
You can find more information about me in my CV.
Feel free to email me at: rd2714 (at) columbia (dot) edu.

1.  Learning singleindex models in Gaussian space. Rishabh Dudeja, Daniel Hsu Conference on Learning Theory (COLT), 2018. 
2.  Attributeefficient learning of monomials over highlycorrelated variables. Alexandr Andoni, Rishabh Dudeja, Daniel Hsu, Kiran Vodrahalli. Conference on Algorithmic Learning Theory (ALT), 2019. 
3.  Analysis of Spectral Methods for Phase Retrieval with Random Orthogonal Matrices. Rishabh Dudeja, Milad Bakhshizadeh, Junjie Ma, Arian Maleki. Transactions on Information Theory, 2020. 
4.  Spectral Method for Phase Retrieval: an Expectation Propagation Perspective. Junjie Ma, Rishabh Dudeja, Ji Xu, Arian Maleki, Xiaodong Wang. Preprint, 2019. 
5.  Information Theoretic Limits for Phase Retrieval with Subsampled Haar Sensing Matrices. Rishabh Dudeja, Junjie Ma, Arian Maleki. Transactions on Information Theory, 2020 (Accepted). 
I have served as the TA for the following courses at Columbia:
1.  Applied Linear Regression Analysis ( B.A./M.A. Level) 
Fall 2015 
2.  Introduction to Statistics (B.A. Level) 
Spring 2016 
3.  Linear Regression Models ( B.A./M.A. Level) 
Fall 2016 
4.  Applied Categorical Data Analysis (B.A. Level) 
Spring 2017, Spring 2018 
5.  Probability and Statistical Inference (M.A. Level) 
Fall 2017 
6.  Statistical Computing and Introduction to Data Science ( B.A./M.A. Level) 
Fall 2018 
7.  Multivariate Statistical Inference ( B.A./M.A. Level) 
Spring 2019 
8.  Statistical Inference and Time Series Modelling ( B.A./M.A. Level)  Fall 2019, Spring 2020 