I am building AI agents to fast-track financial decision making at Finster AI.
Previously, I did a DPhil (PhD) in Machine Learning in the University of Oxford, advised by Professor Philip Torr in Torr Vision Group, Professor Yarin Gal in OATML and Dr. Puneet Dokania. I obtained my Bachelor of Engineering degree in Computer Science and Engineering from Jadavpur University, Kolkata, India and completed my Master of Science (MSc) in Computer Science from the University of Oxford.
My research mainly focuses on methods to safely scale modern neural networks. I am also interested in AI agents and foundation models.
DPhil (PhD) in Machine Learning, 2019-2024
University of Oxford
MSc in Computer Science, 2017-2018
University of Oxford
BE in Computer Science and Engineering, 2012-2016
Jadavpur University
We analyze concept forgetting while fine-tuning foundation models and propose a simple fix to this phenomenon.
We propose a new benchmark for generating and evaluating different types of out-of-distribution samples given an in-distribution dataset.
A deterministic deep neural network with sensitivity and smoothness (bi-Lipschitz) constraints on its feature space can be used to quantify epistemic uncertainty from an estimate of density in feature space and aleatoric uncertainty from the entropy of its softmax distribution.
We propose a modified contrastive loss function which allows training an alignment between patch tokens of a vision encoder and text CLS token of CLIP like models. This loss allows for easy seamless transfer to semantic segmentation without requiring additional annotations.
Responsibilities included: