About Me

I am a research assistant professor and the IEMS alumni fellow at McCormick School of Engineering at Northwestern University. Here is my CV.

Before joining Northwestern, I was a PhD student under Vinayak Rao and Harsha Honnappa at Purdue University. Here is a link to my thesis.

Prior to joining Purdue, I completed my Bachelors and Masters in Statistics from Indian Statistical Institute. My Master’s specialization was in Mathematical Statistics.

My Erdős number is 4. Imon Banerjee —> Jean Honorio —> Tommi Jakkola —> Noga Alon —> Paul Erdős.

I am on the 2025 academic job market.

On Research: I love learning about new topics, and find research extremely fun. Recently, I explored an interesting phenomenon for the Edgeworth expansion of Bootstrapped Studentized sample quantiles. This was first documented by Hall and Marting (paper). This is unique to the Studentized case and does occur for the un-Studentized estimates. We showed that this phenomenon persists with an appropriate rate for the m-out-of-n Bootstrap estimator (see this).

Currently, I am reading about geometrical interpretations of statistics, pioneered by Efron in papers 1, 2, and further developed more recently in 3. The book by Kass and Vos (link) provides a good introduction. I am also generally interested in geometry, and find it very intuitive. So if you have ideas and think I can help, shoot me an email. I would be most happy to discuss.

Prior Work: The main thrust of my research during my PhD was in Markov chains, with both Bayesian and Frequentist flavors. In a trio of papers, one of which is published, and two of which are upcoming, I rigorously developed statistics on controlled Markov chains under both finite and continuous state spaces. I am currently collecting these three works into a monograph, which I plan to make available on my website.

I also recently worked on a non-Gaussian extension of the Kalman filter (see this) with Itai Gurvich where we showed the the Kalman filter (which is the best linear unbiased estimator) is actually sub-optimal in estimating the state when the noise is non-Gaussian. We then propose a modified esimator based on a prescription by Eimear Goggin which performs an order of magnitude better than the Kalman filter.

I also collaborated with Jean Honorio on sparse principal component analysis (see this).

Contact

imon dot banerjee dot northwestern dot edu