I am a senior year undergraduate pursuing a major in Computer Science and a minor in Data Science at BITS Pilani, India.
My research interests lie in Artificial Intelligence specifically in Machine Learning, Deep Learning, and their applications in Computer Vision. I try to understand
human learning and take insights from it to make incremental progress toward building a more generalizable and efficient learning algorithm.
Currently, I am a researcher in Serre Lab at Brown University. At BITS, I am affiliated with APPCAIR
where I have worked in collaboration with TCS Research. I also spent a summer at IBM,
where I worked in collaboration with IBM Research to extend Watson OpenScale.
As a core member of the Society for Artificial Intelligence and Deep Learning, I have been involved in various projects and conducted and taught
some student run courses related to AI.
DetAIL : A Tool to Automatically Detect and Analyze Drift In Language Accepted at the Annual Conference on Innovative Applications of Artificial Intelligence, Collocated with AAAI '23
Supervisors:Nishtha Madaan,Harivansh Kumar [ Preprint ]
We propose to measure the data drift that takes place when new data kicks in so that one can adaptively
re-train the models whenever re-training is actually required. In addition to that, we generate
various explanations at sentence level and dataset level to capture why a given payload text has drifted.
The Abstraction and Reasoning Corpus (ARC) was introduced by François Chollet
as a benchmark to measure AI skill-acquisition on unknown tasks, with the constraint that only a handful of demonstrations are
shown to learn a complex task. We show how mordern neural networks fail ARC and test the idea of using meta learning to learn
priors useful to solve ARC.
This work explores the contexts associated with errors in the automatic transcription of spontaneous speech and its effects
on current State-of-the-art methods for dementia detection. We attempt to build a purely acoustic solution based on Vision Transformers
for dementia detection.
We develop a model which mimics the Visual Hierarchy observed in animals for decoding fMRI to classify images. We show that
hierarchical processing of fMRI data from different parts of the animal visual system aids classification.
Out-of-Distribution Detection for Skin Lesion Classification [ Code ]
In this work, we study the problems dealing with OOD data for skin cancer classification. We compare the performance of Virtual Outlier Synthesis,
a recent work published at ICLR 2022, with other State-of-the-art OOD detection methods. We further show how using another inference method help achieve better results than VOS.
We also study the effects of different types of OOD data on our method.