Hi! I am a 1st year PhD student at the department of Operations Research, Edwardson School of Industrial Engineering at Purdue University. I am working at the Stochastic Systems Lab, under the guidance of Prof. Harsha Honnappa. My research is at the intersection of stochastic processes and machine learning. Currently, I am working on optimizing the noise schedule for annealed Langevin dynamics sampling as a function of the data-generating distribution. Another aspect of my research involves utilizing score-based diffusion models to analyze time series and other complex stochastic data, which I eventually plan to apply to rare event estimation, particularly for efficiently predicting extreme weather events such as storms, hurricanes, and tsunamis. Feel free to reach out if interested in knowing more!
Previously, I was a research assistant at the Oden Institute of Computational Engineering and Sciences at the University of Texas at Austin. I was advised by Prof. Chandrajit Bajaj and had wonderful collaborators at the Computational Visualization Center (CVC). This was also part of my 5th year thesis of my undergraduate in B.E. Computer Science and Integrated MSc. Mathematics from BITS Pilani, Goa.
During my last 2 years at BITS, I was a part of APPCAIR, BITS Pilani, mentored by Prof. Snehanshu Saha. Here I researched on correcting the likelihood misspecification of models via generative adversarial networks, with the aid of approximate Bayesian inference. This made several well known regressors much more robust to noise. I have also spent some wonderful summers interning at Graphics Research Group, IIIT Delhi with Dr. Ojaswa Sharma and at Indian Statistical Institute, Kolkata with Prof. Shubhamoy Maitra.
Apart from spending my time doing research, I hold a keen interest in music, a passion that I got to pursue as part of the Music Society at BITS, where I gave several performances as a vocalist. I'm grateful to be able to continue to pursue music as part of the Purdue Tagore Society. I am also an avid reader, and very fond of cooking and sketching portraits.
Working on min-time mixing in Annealed Langevin dynamics and Score-based Diffusion models, and extending the sampling updates by using different SDE sequences than plain Brownian motion. Extending the work for rare event estimation.
Developed a factored-STVGP (spatio-temporal variational Gaussian Process) framework for directly mapping any readily available RGB image to hyperspectral. Our model surpassed the current state-of-the-art on hyperspectral imaging benchmarks, thus enabling better classification and object detection.
We developed a generative modeling paradigm called skipGAN which combines GANs and Approximate Bayesian Computing (ABC-GAN), with skip connections. The architecture aims at correcting likelihood misspecification in prior models. Our research demonstrated performance improvements in well-known priors like TabNet, CatBoost, and Stats model for regression tasks across various noisy datasets. Preprint
Implementing the genetic algorithm on various setups of the modular synthesizer, called "organisms". Each organism produces a particular kind of sound. The organisms evolve by undergoing the graph-based processes of mutation and crossover, and natural selection, resulting in changes in connections and setups, evolving from fragments of sound to a section of a musical piece. Process Document, Appendix, Team page
IE 336: Operations Research - Stochastic Models, Purdue University
IE 230: Probability and Statistics for Engineers, Purdue University
CS F213: Discrete Mathematics, BITS Goa
CS F111: Computer Programming, BITS Goa
MATH F243: Graphs and Networks, BITS Goa
Advised a group of juniors on a deeper understanding of generative models, in correspondence with Dr. Snehanshu Saha, BITS Goa.
Mentored and taught over 200 students over multiple colleges in India.
Tutorials and doubt-clearing sessions for first year students in MATH F113, BITS Goa.