Ashwini Pokle

ashwinipokle [at] stanford [dot] edu

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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.
[poster] [report]

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.
[poster] [report]


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