I am a roboticist at Tesla Optimus team.
My research interests lie at the intersection of Computer Vision, Graphics, and Robotics. My long-term goal is to build intelligent agents that can see (through vision, audio, and other senses), interact (navigate and act in an environment), and reason (plan long-term actions from sparse rewards).
I got my Ph.D in Artificial Intelligence at University of Maryland College Park. I also interned at Google, NVIDIA, and Amazon AI Labs. Before starting my Ph. D., I spent 5 years prototyping and scaling Vision and ML products in both industry (Netradyne, American Express), and academia (NICTA, ITRI, Carnegie Mellon, IIT Delhi). I also co-founded a startup helping users design their wardrobes by “learning” a fashion knowledge graph from social network data.
Recent News
- May 2024: Optimus goes to the factory
- Sep. 2023: Optimus learns to play with legos
- July 2023: ASIC, SHACIRA, and Chop&Learn accepted to ICCV 2023!
- May 2023: Joined Tesla Optimus as Senior Research Scientist
- Apr. 2023: Defended my Ph.D. Check out my thesis
- Jan. 2023: LilNetX accepted to ICLR 2023
- July 2022: Neural Space-filling Curves accepted to ECCV 2022
- July 2022: Attended International Computer Vision Summer School, ICVSS 2022
- May 2022: Started an internship with Google AR and Google Research
- Apr. 2022: Outstanding Reviewer Award at CVPR 2022
- Jan. 2022: Outstanding Graduate Assistant Award by the Graduate School, UMD
Research Projects
SHACIRA - Scalable HAsh-grid Compression for Implicit Neural Representations
ICCV 2023
ChopNLearn - Generating Object-State Compositions
ICCV 2023
ASIC - Aligning Sparse in-the-wild Image Collections
ICCV 2023
Teaching Matters - Investigating the Role of Supervision in Vision Transformers
CVPR 2023
LilNetX - Lightweight Networks with EXtreme Model Compression and Structured Sparsification
ICLR 2023
Neural Space-filling Curves
ECCV 2022
PatchGame - Learning to Signal in Referential Games
NeurIPS 2021
LayoutTransformer - Layout Generation with Self-attention
ICCV 2021
The Lottery Ticket Hypothesis for Object Recognition
CVPR 2021
Improved Modeling of 3D Shapes with Multi-view Depth Maps
3DV 2020
PatchVAE - Learning Local Latent Codes for Recognition
CVPR 2020
A deep dive into location-based communities in social discovery networks
COMCOM 2017
Global pose estimation with limited gps and long range visual odometry
ICRA 2012
Modeling and Calibration Visual Yield Estimates in Vineyards
FSR 2012, CMU Tech Report
A Compression Scheme for Handwritten Patterns
ICDAR 2011
Blog
Yet Another Machine Learning blog written with an intention of
- learning more by coding and writing. I strongly believe that the best way to learn a concept is to either code it myself or write a tutorial about it. This helps in both understanding various nuances associated with the concept as well as retain the concept for a longer time.
- keeping notes of various lectures/articles/papers/books/ideas I (have) come across.
Check out the blog page for more.
Lernen durch Codierung
is german for Learning by Coding. It’s a play on Learning by Teaching, a strategy for students to learn by teaching their peers popular in Germany.
Disclaimer
Contents of this blog are inference of a biological neural network, trained over a very tiny dataset for a very long time. Any statistically significant correlation to existing literature is not coincidental but an outcome of overfitting.
Website
Finally moving from old page to Jekyll, hoping to do some justice to the new website cum blog. Promising myself to be more regular and more meticulous.