Siddharth Karamcheti

skaramcheti@cs.stanford.edu
Stanford, CA

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I am a second-year PhD Student in Computer Science at Stanford University where I’m grateful to be co-advised by Percy Liang and Dorsa Sadigh. I am honored to be supported by the Open Philanthropy Project AI Fellowship.

I focus on Natural Language Processing and Human-Robot Interaction, specifically developing robust models for grounded language understanding. My goal is to build adaptive robot agents that can learn from natural language interactions to collaborate effectively with humans.

Before starting at Stanford, I was a Resident at Facebook AI Research in New York, where I was lucky to get to work with Rob Fergus, Arthur Szlam, Douwe Kiela, and Jason Weston on problems in grounded language understanding and reinforcement learning.

I received my undergraduate degree from Brown University in May 2018, where I concentrated in Computer Science and Literary Arts. While at Brown, I did research in natural language processing and human-robot interaction advised by Eugene Charniak and Stefanie Tellex.

News


Publications

2020

Generating Interactive Worlds with Text
Angela Fan*, Jack Urbanek*, Pratik Ringshia, Emily Dinan, Emma Qian, Siddharth Karamcheti, Shrimai Prabhumoye, Douwe Kiela, Tim Rocktäschel, Arthur Szlam, and Jason Weston.
Association for the Advancement of Artificial Intelligence (AAAI), February 2020
[pdf] [dataset]

2019

Finding Generalizable Evidence by Learning to Convince Q&A Models
Ethan Perez, Siddharth Karamcheti, Rob Fergus, Jason Weston, Douwe Kiela, and Kyunghyun Cho.
Empirical Methods in Natural Language Processing (EMNLP), November 2019
[pdf] [blog post] [code]

Learning to Speak and Act in a Fantasy Text Adventure Game
Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, and Jason Weston.
Empirical Methods in Natural Language Processing (EMNLP), November 2019
[pdf] [dataset]

Improving Grey-Box Fuzzing by Modeling Program Control Flow
Siddharth Karamcheti, Gideon Mann, and David Rosenberg
Workshop on Machine Learning for Software Engineering (ML4SE), June 2019
[pdf]

Grounding Natural Language Instructions to Semantic Goal Representations for Abstraction and Generalization
Dilip Arumugam*, Siddharth Karamcheti*, Nakul Gopalan, Edward C. Williams, Mina Rhee, Lawson L.S. Wong, and Stefanie Tellex
Autonomous Robots (AuRO), February 2019
[free-to-read pdf]

2018

Adaptive Grey-Box Fuzz Testing with Thompson Sampling
Siddharth Karamcheti, Gideon Mann, and David Rosenberg
11th ACM Workshop on Artificial Intelligence and Security (AISEC), October 2018
[pdf] [talk]

2017

Modeling Latent Attention within Neural Networks
Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L.S. Wong, and Michael Littman
Preprint
[pdf]

A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions
Siddharth Karamcheti, Edward C. Williams, Dilip Arumugam, Mina Rhee, Nakul Gopalan, Lawson L.S. Wong, and Stefanie Tellex
1st Workshop in Language Grounding for Robotics (RoboNLP) @ ACL, August 2017
Best Paper Award
[pdf]

Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities
Dilip Arumugam*, Siddharth Karamcheti*, Nakul Gopalan, Lawson L.S. Wong, and Stefanie Tellex
Robotics: Science and Systems (RSS), June 2017
[pdf]