Biography
I am a Sr. Machine Learning Engineer with Tesla Autopilot currently working on developing large foundation models and solve Autonomy. I have been actively contributing to Full Self Driving (F.S.D.) development since V10 release.
Previously, I was a MS student at the Robotics Institute of Carnegie Mellon University, advised by Professor Shubham Tulsiani. I have a natural affinity towards Computer Vision, Machine Learning and great confidence in their ability to uplift human society.
Before CMU, I worked as a Sr. Machine Learning Engineer with the Advanced Technology Labs in Samsung Research, India. I worked closely with Dr. Shankar Venkatesan towards developing AI systems that can remove obstructions from real-world images. For my undergraduate thesis, I was fortunate to be advised by Professor Arijit Sur on the problem of Image Memorability Prediction.
Download my resumé.
- Computer Vision
- Deep Learning
- Photography
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M.S. Computer Vision, 2022
Carnegie Mellon University
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B.Tech. Computer Science & Technology, 2018
Indian Institute of Technology, Guwahati
Skills
Experience
News
- [03/2024] Promoted to Sr. ML Engineer with Tesla’s Autopilot
- [06/2022] Joined Tesla’s Autopilot as ML Engineer
- [06/2022] AutoSDF: 3D priors for shape generation published in CVPR 2022 paper
- [11/2021] Invited to speak in Verisk AI Frontline (VAI-FI) Seminar Series. Slides
- [08/2021] Joined Dr. Shubham Tulsiani’s group in CMU RI
- [08/2021] Undertook TAship in Computer Vision (16-720 B) with Dr. Kris Kitani
- [05/2021] Interned with Nvidia’s Autonomous Driving team
- [02/2021] Started graduate studeies at CMU RI
- [07/2018] Joined the Advanced Technology Labs in Samsung Research, India
- [07/2014] Started undergraduate studies at IIT-Guwahati
Academic Projects
Non-sequential Autoregressive Shape Priors for 3D Completion, Reconstruction and Generation
Capstone project on exploring autoregressive shape priors for 3D objects and developing a unified framework for shape completion, single-view reconstruction and language guided generation
Multi-Modal Multi-Hop Source Retrieval using Graph Convolutions
A graph convolution based approach to select multiple relevant sources (text or images) of information for multi-hop question answering.
Inverting 3D Deep Learning Architectures
Project on inverting models of 3D object recognition and classification in order to analyze interpretability
3D reconstruction using Stereo Correspondence
Course Assignment on Eight-Point Algorithm, finding epipolar correspondence and bundle adjustment for 3D reconstruction from noisy stereo correspondence
Homography and Panaroma
Course assignment to compute homography matrix, develop AR application and create panaromas using multiple images
Scene Classification using Visual Words
Course assignment to use conventional filter responses (Gaussian, Laplacian of Gaussian etc.) to represent images and develop spatial pyramid based approach to classify scenes.
Video Recommender System
Flask based WebApp to recommend and view videos. Uses MySQL, MongoDB and Neo4j for tracking, storing and recommending videos
Java Multi-Threading
Implemented distributed merge-sort and android based client server application using the ForkJoin principle
Logic Programming
Uses logic programming language SWI-Prolog to develop database retireval and maze solver
Publications
Photography
