(the second LinkedIn directs
to our non-profit)
Srikanth Chandar
AI Research
Animal Welfare
Hi there, welcome to my website! I try to include all my projects, research, and animal welfare work here.
I am currently a PhD student at Johns Hopkins Center for Alternatives to Animal Testing (CAAT), focusing on computational biology. Previously, I was an ML Ops Engineer at AT&T Labs, having graduated from Columbia University with an MS in Electrical Engineering. Prior to this, I was an AI Engineer at Intel Corporation, having graduated from PES University. My latest research interests are in Foundation Models and Bio/Chem informatics.
A little about the non-technical side of me- I feel very strongly about animal abuse. I help run Ground Animal Welfare, which promotes animal welfare initiatives through campaigns, volunteering activities, and even technical projects.
Research Interests
Anything ML excites me, and I like taking up a challenge that could shape a new paradigm in the same space, be it application-based or research. My preference in Applied Research is in areas like Neuro-ML, Deep Learning, and Data Science. I am exploring my finer interests in these subjects, one project at a time, each of a different topic from the previous.
From an application perspective, I like Brain Computer Interfaces, and feel that the current way of interacting with gadgets- bent backs and typing into a keyboard- is obsolete. My recent research inclinations have been in Foundation Models and how they may forecast time series data. My work at AT&T Labs involves the same. Federated Learning (FL) has been another focal point of my research lately, and my work at Intel included making FL more communication efficient.
Publications
Chandar, Srikanth, et al. "Communication Optimization in Large Scale Federated Learning using Autoencoder Compressed Weight Updates." arXiv preprint arXiv:2108.05670 (2021).
Chandran, P., Bhat, R., Chakravarthi, A., & Chandar, S. (2021). Weight Divergence Driven Divide-and-Conquer Approach for Optimal Federated Learning from non-IID Data. arXiv preprint arXiv:2106.14503.
Chandar, Srikanth, et al. "Road Accident Proneness Indicator Based On Time, Weather And Location Specificity Using Graph Neural Networks." arXiv preprint arXiv:2010.12953 (2020).
APA