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Paritosh Mittal

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Paritosh Mittal

Sr. MLE @ Tesla Autopilot | MSCV @ CMU RI

Tesla Inc.

Carnegie Mellon University

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.

Interests

Computer Vision
Deep Learning
Photography

Education

M.S. Computer Vision, 2022

Carnegie Mellon University
B.Tech. Computer Science & Technology, 2018

Indian Institute of Technology, Guwahati

Skills

Pytorch

Python

OpenCV

Tensorflow

Scikit-Learn

Pandas

Experience

Sr. Machine Learning Engineer

Tesla

Jun 2022 – Present Palo Alto, CA

Training large vision foundation models for self-driving and deploying them in-car

Research Collaborator (MSCV Capstone)

Verisk Analytics

Sep 2021 – Mar 2022 Pittsburgh, PA

Working on generative 3D models in collaboration with CMU and Verisk

Computer Vision Software Intern

NVIDIA

May 2021 – Aug 2021 Santa Clara, CA

Worked with the Stereo Perception team of Nvidia’s Autonomous Driving Group

Sr. Machine Learning Engineer

Samsung Research

Mar 2020 – Dec 2020 Bangalore, IN

Proposed and Developed a stroke based input modality with Input Method Intelligence team

Machine Learning Engineer

Samsung Research

Jul 2018 – Mar 2020 Bangalore, IN

Innovated [Patented] a stereo-image based pipeline for Obstruction Removal

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

.js-id-Deep-Learning

All Deep Learning Computer Vision Development

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

Video Capstone Page code (Coming Soon)

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.

Poster Project Report code (Coming soon)

Inverting 3D Deep Learning Architectures

Project on inverting models of 3D object recognition and classification in order to analyze interpretability

Project Report Code (Coming Soon)

3D reconstruction using Stereo Correspondence

Course Assignment on Eight-Point Algorithm, finding epipolar correspondence and bundle adjustment for 3D reconstruction from noisy stereo correspondence

Code

Homography and Panaroma

Course assignment to compute homography matrix, develop AR application and create panaromas using multiple images

Code

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.

Code

Video Recommender System

Flask based WebApp to recommend and view videos. Uses MySQL, MongoDB and Neo4j for tracking, storing and recommending videos

Code

Hunger Games

A realtime IoT based outdoor shooting game

Code Project Report

Java Multi-Threading

Implemented distributed merge-sort and android based client server application using the ForkJoin principle

Multi-threaded Search and Sort Android Client Server using multi-threading

Logic Programming

Uses logic programming language SWI-Prolog to develop database retireval and maze solver

Code

Publications

More publications »

Paritosh Mittal, Yen-Chi Cheng, Maneesh Singh, Shubham Tulsiani (2022). Non-sequential Autoregressive Shape Priors for 3D Completion, Reconstruction and Generation. In CVPR 2022.

PDF Code Project Page

Paritosh Mittal, Shankar M Venkatesan, Aloknath De, Himanshu Pandotra, Viswanath Veera, Rohith Aralikkatti (2021). Method and electronic device for obtaining reconstructed image. US Patent.

Paritosh Mittal, Pragya Sahu, Himanshu Pandotra, Viswanath Veera, Shankar M Venkatesan (2020). A method for enhanced recognition of continuous inputs from multiple modalities. US Patent.

Paritosh Mittal, Shankar M Venkatesan, Viswanath Veera, Aloknath De (2020). Deep Fence Estimation using Stereo Guidance and Adversarial Learning. In Arxiv.

PDF Cite

Paritosh Mittal, Kunal Agarwal, Pragya Sahu, Vishal Vatsalya, Soumyajit Mitra, Vikrant Singh, Viswanath Veera, Shankar M Venkatesan (2020). Photo-realistic emoticon generation using multi-modal input. In ACM IUI.

PDF Cite DOI

Pranav Kumar Singh, Manish Kumar Dash, Paritosh Mittal, Sunit Kumar Nandi, Sukumar Nandi (2018). Misbehavior Detection in C-ITS Using Deep Learning Approach. In ISDA.

PDF Cite DOI

Sathisha Basavaraju, Paritosh Mittal, Arijit Sur (2018). Image Memorability: The Role of Depth and Motion. In ICIP.

PDF Cite DOI

Photography

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