I'm an associate professor in Computer Science & Engineering
at Indian Institute of Technology, Kanpur.
Before this (reverse chronologically), I was a research faculty in
Electrical and Computer Engineering,
at Duke University,
a post-doctoral fellow in Statistics
and Computer Science
at UT Austin,
and a Ph.D. student in School of Computing
at University of Utah. I work in the
area of machine learning and Bayesian statistics. My research is primarily on probabilistic/generative
modeling and understanding of massive and complex data.
My students
and I focus on designing probabilistic generative models
and efficient optimization/inference algorithms that can learn compact and interpretable latent structures
and feature representations from such data, and leverage these to better understand/summarize the data,
generate data with controllable properties, and make better predictions/decisions, especially in the face of
data/model uncertainty. We also work on designing
machine learning models and algorithms that can work well in resource-constrained settings (e.g., scarce training data
or under limited compute/storage).
VERSE: Virtual-Gradient Aware Streaming Lifelong Learning with Anytime Inference
With:Soumya Banerjee, Vinay Verma, Avideep Mukherjee, Deepak Gupta, and Vinay Namboodiri
ICRA 2024
RISSOLE: Parameter-efficient Diffusion Models via Block-wise Generation and Retrieval-Guidance
With:Avideep Mukherjee, Soumya Banerjee, and Vinay Namboodiri
BMVC 2024
Integrating Micro-satellite Images and Sensor Network-based Ground
Measurements using Deep Learning for High-resolution Prediction of Fine Particulate Matter
With:Vaishal Jain, Avideep Mukherjee, Soumya Banerjee, Sandeep Madhwal, Michael H Bergin, Prakash Bhave, David Carlson, Ziyang Jiang, Tongshu Zheng, and Sachchida Nand Tripathi
Atmospheric Environment, 2024
A Probabilistic Framework for Lifelong Test-Time Adaptation
With:Dhanajit Brahma
CVPR 2023
SITA: Single Image Test-time Adaptation
With:Ansh Khurana, Sujoy Paul, Gaurav Aggarwal, and Soma Biswas
CVPR 2023 workshop on Computer Vision in the Wild
Federated Learning with Uncertainty via Distilled Predictive Distributions
With:Shrey Bhatt and Aishwarya Gupta
ACML 2023
Gradient Perturbation-based Efficient Deep Ensembles
With:Amit Chandak and Purushottam Kar
IKDD-CODS 2023
DiffuseVAE: Efficient, Controllable and High-fidelity Generation from Low-dimensional Latents
With:Kushagra Pandey, Avideep Mukherjee, and Abhishek Kumar
TMLR 2022
Novel Class Discovery without Forgetting
With:K.J. Joseph, Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Kai Han, Vineeth Balasubramanian
ECCV 2022
Rectification-based Knowledge Retention for Task Incremental Learning
With:Pratik Mazumder, Pravendra, Singh, and Vinay P. Namboodiri
IEEE TPAMI 2022
CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks
With:Sakshi Varshney, Vinay Verma, Srijith P.K., and Lawrence Carin
NeurIPS 2021
Bayesian Structural Adaptation for Continual Learning
With: Abhishek Kumar and Sunabha Chatterjee
ICML 2021