I am a second year PhD student in Machine Learning Department at Carnegie Mellon University where I am fortunate to be advised by Professor Zico Kolter. Previously, I completed my Master's in Computer Science from Stanford University. During my Master's, I also worked as a Research Assistant at Stanford Vision and Learning Lab (SVL) under supervision of Prof. Silvio Savarese . Prior to joining Stanford, I worked as a Software Development Engineer at Amazon for 2 years where I was a part of the Prime Video team and Fulfillment Center Technologies team.
I received my Bachelor’s in Computer Science from Birla Institute of Technology and Science, Pilani, India, where I worked on writing parallel and distributed versions of dimensionality reduction algorithms. I have spent a summer at USC Melady lab working under guidance of Prof. Yan Liu on studying techniques to improve convergence of stochastic gradient descent algorithm on heavy-tailed distributions. I have also interned at Bhabha Atomic Research Center, Mumbai in 2013.
My primary research interests include Representation Learning, Computer Vision and Reinforcement Learning.
X. Zang*, A. Pokle*, M. Vazquez, K. Chen, J. C. Niebles, A. Soto, and S. Savarese. Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation. In Proceedings of Conference on Empirical Methods in Natural Language Processing, 2018.
[ website ][ code ][ paper ]
A. Pokle, R. Martın-Martın, P. Goebel, V. Chow, H. M. Ewald, J. Yang, Z. Wang, A. Sadeghian, D. Sadigh, S. Savarese, M. Vazquez. Deep Local Trajectory Planning and Control for Robot Navigation. In Proceedings of IEEE International Conference on Robotics and Automation, 2019
Defendr: A robust model for Image Classification
CS 231n Course Project with Apoorva Dornadula and Akhila Yerukola, Stanford University
We implemented an image classification model that is robust to black-box adversarial attacks like FGSM and PGD. The model uses a DUNet to denoise adversarial images. It also uses adversarial logit pairing in the objective to train a robust classifier. The model was able to achieve classification accuracy of 86.6% on tiny ImageNet (12,000 data points, 300 classes). This work was selected as the second best poster.
Visual Question Answering
CS 224n Course Project with Stefanie Anna, Stanford University
We implemented a visual question answering system that can provide single-word answer for a wide-variety of questions about an image. The model used Stacked Attention Network and was able to achieve accuracy of 47.11% on VQA v2.
Automated Question Answering
CS 221 Course Project with Amita Kamath and Akhila Yerukola, Stanford University
Suggested modifications to Dynamic Memory Network (DMN+) that improved convergence rate (upto 20% on 18 QA tasks) and improved accuracy (2% - 6% on 4 QA tasks) on bAbI-10k dataset.
Deep Reinforcement Learning for long term strategy games
CS 229 Course Project with Akhila Yerukola and Megha Jhunjhunwala, Stanford University
We implemented a hierarchical DQN on Atari Montezuma’s Revenge and compared the performance with other algorithms like DQN, A3C and A3C-CTS. The agent learnt a strategy to cross the first room and get a consistent reward.
Analysis of Emergent Behavior in Multi Agent Environments using Deep RL
CS 234 Course Project with Stefanie Anna, Stanford University
Implemented parameter-sharing DQN, DDQN and DRQN for multi-agent environments and analysed the evolution of complex group behaviors on multi-agent environments like Battle, Pursuit and Gathering. We observed emergence of interesting group behaviors in these games. In Battle, the particles learnt to cooperate to success- fully capture and kill the particles of the opponent team. In Pursuit, we observed that the predators learned to cooperate and form enclosures to trap the prey.
Nutanix Women in Tech Scholarship, 2018
Regional Winners (India - Europe region), IC Tech Winter Hackathon, Amazon, 2017
Winner, AFT India Hackathon, Amazon, 2016
Merit Scholarship, BITS Pilani, 2011-2015
GE Foundation Scholar-Leader, GE Foundation, 2013