Hi, I'm Miheer Dewaskar
I am a postdoc in the Department of Statistical Science at Duke University, working with David Dunson on developing methods for robust inference in parametric models and non-parametric Bayesian clustering. I obtained my PhD in Statistics and Operations Research from UNC Chapel Hill in 2021, under the direction of Shankar Bhamidi, Amarjit Budhiraja, and Andrew Nobel in which we developed a method for groupwise correlation mining and applied it to the data-integration problem of eQTL analysis in genomics, and asymptotically studied the power-of-choice load-balancing scheme.
Research in Data and Systems scienceMy research spans a wide range of mathematical topics that include: formal methods in computer science for system verification, queuing theory and stochastic processes, and design of statistical theory and methodology for data analysis. I take a principled approach to solving problems, leveraging mathematical tools like:
- Game Theory, Fixed point procedures, and Convex Analysis.
- Concentration inequalities, Stochastic Calculus, and Large Deviation theory.
- Multiple testing, Covariance estimation, and Bayesian asymptotics.
Publications* indicates joint first authors.
= indicates primary contribution and alphabetical author order.
- Near Equilibrium Fluctuations for Supermarket Models with Growing Choices (2022). Bhamidi S, Budhiraja A, and Dewaskar M=. Annals of Applied Probability, VOL. 32 (NO. 3), 2083-2138.
- NExG: Provable and Guided State Space Exploration of Neural Network Control Systems using Sensitivity Approximation (2022) Goyal M, Dewaskar M, Duggirala PS. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022, doi: 10.1109/TCAD.2022.3197524.
- Controlling a population (2019). Bertrand N, Dewaskar M=, Genest B=, Gimbert H, and Godbole A. Logical Methods in Computer Science, Vol. 15, Issue 3.
- Controlling a population (2017). Bertrand N, Dewaskar M=, Genest B=, Gimbert H. 28th International Conference on Concurrency Theory (CONCUR 2017).
- Finding Groups of Cross-Correlated Features in Multi-View data. Dewaskar M , Palowitch J, He M, Love M.I., Nobel A.B. Under revision for Journal of Machine Learning Research
- Robustifying likelihoods by optimistically re-weighting data. Dewaskar M *, Tosh C*, Knoblauch J, Dunson D.B. Submitted to the Journal of American Statistical Association
- Bayesian Level-set Clustering. Buch D*, Dewaskar M*, Dunson D.B. (In preparation).