I am a passionate AI researcher with a strong background in computer vision and robotics. I am currently pursuing my PhD in Electrical and Computer Engineering at the University of Southern California. My research interests include deep reinforcement learning, machine learning, and robotics. I am a strong believer in the power of AI to improve the quality of life of people around the world.
Sept 2021 - Present, Los Angeles, CA
RESL is a research lab at the University of Southern California. The lab is headed by Prof. Gaurav Sukhatme. The lab is focused on robotics and AI research.
May 2021 - August 2021, Los Angeles, CA
This company investigates the application of AI in the field of sales
May 2020 - May 2021, Los Angeles, CA
SSLL is a research lab at the University of Southern California. The lab is headed by Prof. Rahul Jain. The lab is focused on theoretical reinforcement learning .
November 2019 - October 2020, Los Angeles, CA
DRCL is a research lab at the University of Southern California. The lab is headed by Prof. Quan Nguyen. The lab is focused on robotic control.
June 2016 - July 2019, Bangalore, India
Fidelity Investments is renowned financial institution that specializes in investment management, retirement planning, portfolio guidance, brokerage, benefits outsourcing, and many other financial products and services.
July 2017 - July 2019
June 2016 - August 2016
May 2014 - June 2015, Mangalore, India
Laboratory of Applied Biology, Kuppers Biotech Unit.
We propose HyperPPO, an on-policy reinforcement learning algorithm that utilizes graph hypernetworks to estimate the weights of multiple neural architectures simultaneously. Our method estimates weights for networks that are much smaller than those in common-use networks yet encode highly performant policies. We obtain multiple trained policies at the same time while maintaining sample efficiency and provide the user the choice of picking a network architecture that satisfies their computational constraints.
In this work, we propose using diffusion models to distill a dataset of policies into a single generative model over policy parameters. We show that our method achieves a compression ratio of 13x while recovering 98% of the original rewards and 89% of the original coverage. Furthermore, the conditioning mechanism of diffusion models allows for flexibly selecting and sequencing behaviors using language.
We leverage graph hyper networks to learn graph hyper policies trained with off-policy reinforcement learning resulting in networks that are two orders of magnitude smaller than commonly used networks yet encode policies comparable to those encoded by much larger networks trained on the same task.
In this work, we focus on the combination of analytical and learning-based techniques to help researchers solve challenging robot locomotion problems. Specifically, we explore the combination of curricular trajectory optimization (CTO) and deep reinforcement learning (RL) for quadruped hurdling tasks.
We introduce a new version of VizDoom simulator to create a highly efficient learning environment that provides raw audio observations. We study the performance of different model architectures in a series of tasks that require the agent to recognize sounds and execute instructions given in natural language. Finally, we train our agent to play the full game of Doom and find that it can consistently defeat a traditional vision-based adversary.
We present RANDPOL, a generalized policy iteration algorithm for MDPs with continuous state and action spaces. Both the policy and value functions are represented with randomized networks. We also give finite time guarantees on the performance of the algorithm.
This paper evaluates the use of DDPG to solve the problem of risk aware portfolio construction. Simulations are done on a portfolio of twenty stocks and the use of both Rate of Return and Sortino ratio as a measure of portfolio performance are evaluated. Results are presented that demonstrate the effectiveness of DDPG for risk aware portfolio construction.
Apply BERT sentence transformer to encode abstracts of hundreds of papers, and then find cosine similarity of the encoding with that of topic definitions to rank and tag them
As a part of the Autonomous Vehicle lab, I worked on navigation, path planning and simulation of an autonomous car to take part in IGVC 2021. I used Gazebo to build an accurate simulation of the track, and implement path finding algorithms such as A star.
As a part of my directed research with the Hardware Accelerated Learning group, I’m experimented with various multi agent reinforcement learning algorithms. The goal of this project is to understand the state of the art RL algorithms that work well in both competitive and cooperative environments.
Use reinforcement learning and transfer learning to create robust AI agents. The AI agent should generalize to a variety of open world self driving simulations. After training an AI for a self driving car simulation using Imitation learning and reinforcement learning, the learnt policy was used as a pre trained network for an AI agent in another self driving simulation. The pretrained model showed faster learning in the new simulation.
Use a conditional Generative Adversarial Neural Network to generate images on spectrograms of speech signals. By using cycle GANs we use style transfer on spectrograms of speech signals to embed emotion in them. The generated spectrogram is reconstructed back to speech using the Griffin-Lim algorithm.
Use a Siamese Convolutional Neural Network to classify if two fashion objects are compatible with each other. Then using the pair-wise similarity scores predicted to see if an outfit is compatible. To do this Google Tensorflow 2.0 was used and the models were trained on AWS p3.2xlarge instances (Tesla V100 GPUs).
Implement a Gated Recurrent Unit based Neural Network to classify the extracted MFCC features from speech audio. A streaming model classifies the language being spoken in real time. Using this streaming model, we could analyse the probability of miss-classification at every instant of speech.
Undergraduate Thesis, sEMG signal controlled speech production aid for speech challenged individuals using Machine Learning. The signals were collected, filtered, pre-processed and then fed to a classifier that would predict the hand action performed. The action would then be translated to speech.
I was part of a three member team that built a Machine Learning driven emotion detector using variations in speech signals. Using MFCC feature extraction and PCA on other features, we built a classifier.
2021-Present Ph.D in Electrical and Computer EngineeringCGPA: 3.94 out of 4 | ||
2019-2021 M.S. in Electrical and Computer EngineeringCGPA: 3.94 out of 4 | ||
B.Tech. in Electrical and Electronics EngineeringCGPA: 8.17 out of 10 |
Awarded a 1 year Fellowship for my PhD.
Winner of image classification contest by Deep Cognition.link
EE541 - A Computational Introduction to Deep Learning
EE641 - Deep Learning Systems
CSCI567 - Machine Learning
Delivered a company wide talk on SOTA applied Deep Reinforcement Learning. pdf
Presented a talk on Generative models for robotics
