Chad DeChant
| c | h | a | d | . | d | e | c | h | a | n | t | @ | c | o | l | u | m | b | i | a | . | e | d | u |
| Twitter: | @chaddechant |
| Google Scholar |
| PhD candidate, Computer Science Department |
| Columbia University |
| MA, Government, Institute for International Law and Politics |
| Georgetown University |
| BA, Philosophy |
| Georgetown University |
| I am a PhD candidate at Columbia working at the intersection of natural language processing and robotics, advised by Daniel Bauer. My goal is to apply cognitive science research to make AI safe, reliable, and interpretable. |
| My thesis work is on enabling robots to remember and report on their past actions in natural language through summaries and question answering. I believe that this ability is an essential one both for training robots and other agents to better understand their environments and for ensuring safe and understandable agent behavior. |
| I have also worked on artificial metacognition (i.e. thinking about thinking, especially knowing what we know), learning word representations in the order a child learns words, improving robotic grasping techniques by learning to 'imagine' parts of objects that are hidden, and detecting crop disease from field imagery. |
| I created and taught a course for Columbia undergraduates on AI Safety, Ethics, and Policy. |
Selected Publications
- Episodic memory in AI agents poses risks that should be studied and mitigated
Chad DeChant
Accepted to the Secure and Trustworthy Machine Learning conference 2025
- Episodic Memory Verbalization using Hierarchical Representations of Life-Long Robot Experience
Leonard Bärmann, Chad DeChant, Joana Plewnia, Fabian Peller-Konrad, Daniel Bauer, Tamim Asfour, and Alex Waibel
arXiv preprint 2024
Paper - In search of the embgram: forming episodic representations in a deep learning model
Chad DeChant, Iretiayo Akinola, and Daniel Bauer
Cognitive Computational Neuroscience 2024
Paper - Learning to Summarize and Answer Questions about a Virtual Robot's Past Actions
Chad DeChant, Iretiayo Akinola, and Daniel Bauer
Autonomous Robots Special issue on LLMs and Robotics, November 2023
Paper Code - On the risks and benefits of episodic memory in AI agents
Chad DeChant
Safe and Trustworthy AI Workshop at ICLP 2023
Paper - Are robots stuck in time? Learning to represent actions in long horizon planning by learning to remember the past
Chad DeChant
Learning, Perception, and Abstraction for Long-Horizon Planning Workshop at the Conference on Robot Learning 2022
Paper - RoboReport: Summarizing a virtual robot's past actions in natural language
Chad DeChant, Iretiayo Akinola, and Daniel Bauer
Language and Robot Learning Workshop at the Conference on Robot Learning 2022 (Spotlight talk)
Paper - Do you see what I see? Using questions and answers to align representations of robotic actions
Chad DeChant, Iretiayo Akinola, and Daniel Bauer
Aligning Robot Representations with Humans Workshop at the Conference on Robot Learning 2022
Paper - Toward robots that learn to summarize their actions in natural language: a set of tasks
Chad DeChant and Daniel Bauer
Conference on Robot Learning 2021
Paper and OpenReview - Learning word representations in a developmentally realistic order
Chad DeChant and Daniel Bauer
2021 Paper - Predicting the accuracy of neural networks from final and intermediate layer outputs
Chad DeChant, Seungwook Han, and Hod Lipson
Identifying and Understanding Deep Learning Phenomena workshop at ICML 2019
Paper - Learning to make accuracy judgments from intermediate and final outputs of a neural network for a question answering task
Chad DeChant, Seungwook Han, and Hod Lipson
BlackboxNLP at ACL 2019 - Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning
Chad DeChant, Tyr Wiesner-Hanks, Siyuan Chen, Ethan Stewart, Jason Yosinski, Michael Gore, Rebecca Nelson, and Hod Lipson
Phytopathology 2017
Paper - Shape completion enabled robotic grasping
Jacob Varley, Chad DeChant, Adam Richardson, Joaquín Ruales, Peter Allen
Intelligent Robots and Systems (IROS) 2017
Paper Video
Other Publications
- Creating a Tool to Reproducibly Estimate the Ethical Impact of Artificial Intelligence
Sara Jordan, Sina Fazelpour, Adriano Koshiyama, Jaky Kueper, Chad DeChant, Brenda Leong, Gary Marchant, Craig Shank
Paper 2019 - Could AI Drive Transformative Social Progress? What Would This Require?
Ted A Parson, Robert Lempert, Ben Armstrong, Evan Crothers, Chad DeChant, Nicholas Novelli
Paper 2019 - *What can Natural Language Processing learn from the brain?
Intended as a quick guide to the brain's processing of language for NLP researchers along with some of my ideas for how such a perspective might influence future NLP research. Originally a paper and annotated bibliography I wrote for a class called Computation and the Brain, it needs more work.
Chad DeChant
DRAFT Paper 2018 *in progress
- AI, modularity, and the global workspace
Suggestions for applying ideas of modularity and the global workspace to AI. Written for a Psychology seminar.
Chad DeChant
Paper 2017 - Autonomous Detection of Plant Disease Symptoms Directly from Aerial Imagery
Harvey Wu, Tyr Wiesner-Hanks, Ethan L Stewart, Chad DeChant, Nicholas Kaczmar, Michael A Gore, Rebecca J Nelson, Hod Lipson
The Plant Phenome Journal 2019
Paper - Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning
Ethan L Stewart, Tyr Wiesner-Hanks, Nicholas Kaczmar, Chad DeChant, Harvey Wu, Hod Lipson, Rebecca J Nelson, Michael A Gore
Remote Sensing 2019
Paper - Millimeter-level plant disease detection from aerial photographs via deep learning and crowdsourced data
Tyr Wiesner-Hanks, Harvey Wu, Ethan Stewart, Chad DeChant, Nicholas Kaczmar, Hod Lipson, Michael A Gore, Rebecca J Nelson
Frontiers in Plant Science 2019
Paper - Image set for deep learning: field images of maize annotated with disease symptoms
Tyr Wiesner-Hanks, Ethan Stewart, Nicholas Kaczmar, Chad DeChant, Harvey Wu, Rebecca Nelson, Hod Lipson, and Michael Gore
BMC Research Notes 2018
Paper - Automated Weed Detection in Aerial Imagery with Context
Delia Bullock, Andrew Mangeni, Tyr Wiesner-Hanks, Chad DeChant, Ethan L Stewart, Nicholas Kaczmar, Rebecca J Nelson, Michael A Gore, Hod Lipson
Paper 2019
Teaching
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AI Safety, Ethics, and Policy
I created and taught this course for Columbia undergraduates.
Deep Learning for Computer Vision, Speech, and Language
I served as a TA for this course after taking it.
Other News
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I received a grant from the Long Term Future Fund to fund the remainder of my PhD beginning in Spring 2022.
My thesis proposal was approved in December 2021.
In the Fall of 2020 I passed my candidacy exam on grounded language learning and meta learning.
In the summer of 2019 I was chosen to participate (expenses paid) in the first Summer Institute on AI and Society cosponsored by CIFAR, AMII, and UCLA Law School.
An article in Columbia Engineering magazine discusses my AI Safety, Ethics, and Policy class.
I gave a very short talk at the Columbia Robotics Showcase on our work detecting plant disease using drones.
Volunteering
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I founded Sanghas Supporting Refugees to support newly arrived refugees in New York City. The Associated Press mentioned our work here.