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Master's in Computer Engineering

New York University

Bachelor's in Information Technology

Netaji Subhas Institute of Technology (Univeristy of Delhi)

Gradutate Research Assistant

CILVR Lab, New York University, New York City, US

• Exploring Reinforcement Learning (RL) and Self Supervised Learning (SSL) methods for Robot Learning under Prof. Lerrel Pinto
• Developed a novel Inverse RL method called Regularised Optimal Transport that adaptively combines an Imitation Learning prior with RL exploration capable of learning many robotics tasks using a single expert demo with a 7.8x speedup to reach a 90% success rate over existing baselines.
• Created a framework for Vision based learning using RL, sim2real and Domain Randomisation methods to adapt a policy trained in Simulation to the real world to complete tasks such as Pick and Place and PegInsertion.

Software Development Engineer

Zomato, Gurgaon, India

• Built Zomato’s in-house Distributed and Highly Scalable Monitoring and Alerting Platform. The platform reduced incident response time by 80% and allowed for Preemptive Incident Detection and Alerting.
• Created the Dynamic Inter-service Discovery and Communication for microservices using Envoy and a Golang microservice for AWS ECS Container discovery enabling migration to a microservice-based architecture.
• Set up Zomato’s Distributed Relational Database. It distributed query load and reduced query failure rates by 87%.

Software Engineer

Hong Kong Shanghai Bank (HSBC), Pune, India

• Led team’s service migration to HSBC’s Internal Cloud Platform.
• Created the Microservice Deployment Service acting as an internal CICD tool in Spring Boot. Reduced service deployment incidents by 53% and decreased the service deployment time to <1 min.

Collab-Editor

• Browser-based Simple Collaborative Text Editor.
• Implemented it using Replicated Growable Array, a type of Conflict-Free Replicated Data Type (CRDT) to achieve eventual consistency even if the user is not connected to the internet.

Sim2Real

• Transfering an RL policy learnt in simulation to a real Xarm7 robot.
• Used Asymmetric Actor Critic model to learn the policy in Mujoco simulation.
• Used Domain Randomisation as means for Domain Adaptation to the real world.
• Used Hindsight Experience Replay (HER) to allow more efficient learning in a Sparse Reward setting.

Distributed Training

• Created a Distributed Training Pipeline for learning Self-Supervised embeddings for various downstream tasks from images using Pytorch Distributed Data-Parallel.

Hydra

• A P2P platform to generate, share, contribute to ML Datasets and train Deep Learning models.
• Implemented a Distributed Hash Table for Node Discovery, Multi-tracker approach using Raft to create a fault-tolerant archive of datasets. Used All-Reduce to run Synchronous Stochastic Gradient Descent for distributed training.

MARL

• Implemented Multi-Agent Reinforcement Learning (MARL) using 2 Independent DQNs with information sharing to master the game of Knights Archers Zombies.