Tuhin Chakrabarty

Assistant Professor, Computer Science, SUNY at Stony Brook (Office: NCS 159)

I am an Assistant Professor at the Computer Science Department in Stony Brook University (SUNY), an Affiliate of the Institute for Advanced Computational Science (IACS), and a Visiting Professor at Salesforce AI Research. My work has been covered by The New York Times, The New Yorker, The Atlantic, The Washington Post, The Telegraph, Bloomberg News and MIT Technology Review

My research interests are broadly in AI, Law and Public Policy and my goal is to design, build and audit responsible AI systems that are aligned with the requirements humans have from technology. I often rely on knowledge, methods, and perspectives from multiple disciplines to address complex problems or questions that cannot be fully understood or solved within the boundaries of Computer Science. I am specifically very interested in Generative AI, Creativity, Copyright and its impact on Labor Markets. Some representative works

PhD students



Media



Recent News


Education

2017-2024
Columbia University

Ph.D. in Computer Science

2017-2019
Columbia University

M.S. in Computer Science

2010-2014
Jadavpur University

Bachelors of Engineering in Computer Science

Professional Experience

Now
Salesforce AI Research

Visiting Professor, Past Research Scientist

Current
Google Deepmind

Research Intern

Host: David Reitter and Hannah Rashkin

2021
NYTimes R&D

NLP Research Fellow

2021
Mosaic (Machine Commonsense Team)

Research Intern (PhD)

Host: Yejin Choi and Vered Shwartz (PhD)

2018
Amazon Alexa

Applied Scientist Intern

2016-2017
UBER (Revenue Team)

Machine Learning Engineer




For more details, please see my full CV (PDF).


See my Full List of Publications here.

Selected Publications




Generative AI, Copyright and Labor Markets


Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Models

Xinyue Liu, Niloofar Mireshghallah, Jane C Ginsburg, Tuhin Chakrabarty
Tags: Generative AI, Copyright Law, Fair Use, Memorization, Misalignment

Readers Prefer Outputs of AI Trained on Copyrighted Books over Expert Human Writers

Tuhin Chakrabarty, Jane C Ginsburg, Paramveer Dhillon
Full Paper Under Submission
Presentation at AI and The Future of Work Conference at Wharton, 2026
Tags: Generative AI, Copyright Law, Fair Use, Future of Work, AI Detection, AI and Society, Behavioral Science, Labor Market Impact

Can Good Writing Be Generative? Expert-Level AI Writing Emerges through Fine-Tuning on High Quality Books

Tuhin Chakrabarty, Paramveer Dhillon
Accepted to CHI 2026
[PDF] [Data]
Tags: Generative AI, Copyright Law, Fair Use, Future of Work, AI and Society, Behavioral Science, Labor Market Impact



AI Safety should prioritize the Future of Work

Sanchaita Hazra, Bodhisattwa Majumder, Tuhin Chakrabarty
Accepted to ICML 2025
🏆 Outstanding Position Paper Award
Oral (Top 1%)
[PDF]
Tags: Generative AI, AI Safety, Economics

AI and Human Behavior


Death of the Novel(ty): Beyond N-Gram Novelty as a Metric for Textual Creativity

Arkadiy Saakyan, Najoung Kim, Smaranda Muresan, Tuhin Chakrabarty
Tags: LLM and Writing, Reward Modeling, Text Edits, Human AI Alignment, Pre-training

AI-Slop to AI-Polish? Aligning Language Models through Edit-Based Writing Rewards and Test-time Computation

Tuhin Chakrabarty* , Philippe Laban*, Chien-Sheng Wu
Tags: LLM and Writing, Reward Modeling, Text Edits, Human AI Alignment, Test Time Compuattion, Calibration

Can AI writing be salvaged? Mitigating Idiosyncrasies and Improving Human-AI Alignment in the Writing Process through Edits

Tuhin Chakrabarty , Philippe Laban, Chien-Sheng Wu
🏆 Best Paper Honorable Mention
Tags: LLM and Writing, Text Edits, Human AI Alignment, Behavioral Science

Art or Artifice? Large Language Models and the False Promise of Creativity

Tuhin Chakrabarty, Philippe Laban, Divyansh Agarwal, Smaranda Muresan, Chien-Sheng Wu
Tags: Creativity Evaluation, Divergent Thinking, Story Generation, HCI, Generative AI

Human AI Interaction


Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writers

Tuhin Chakrabarty*, Vishakh Padmakumar*, Faeze Brahman, Smaranda Muresan
* denotes Co-First Authors
Tags: Co-Creative Generation, Natural Language Instructions, HCI, Generative AI

I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors

Tuhin Chakrabarty*, Arkadiy Saakyan*, Olivia Winn*, Artemis Panagopoulou, Yue Yang, Marianna Apidianaki, Smaranda Muresan
* denotes Co-First Authors
Accepted to ACL 2023 Findings
Tags: Co-Creative Generation, Natural Language Instructions, Vision and Language Models, AI Art, Generative AI


Machine Learning for NLP


Fine-tuned Language Models are Continual Learners

Thomas Scialom*, Tuhin Chakrabarty*, and Smaranda Muresan
* denotes Co-First Authors
Tags: Continual Learning, Instruction Tuning

Natural Language and Ambiguity


FLUTE: Figurative Language Understanding through Textual Explanations

Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, Smaranda Muresan
Tags: Figurative Language, Natural Language Inference, Free Text Explanation

It’s not Rocket Science: Interpreting Figurative Language in Narratives

Tuhin Chakrabarty, Yejin Choi, Vered Shwartz
Tags: Figurative Language, Multiword Expression, Commonsense


Contact


E-mail: <x>@cs.columbia.edu, where x=tuhin.chakr.
The Interchurch Center 61 Claremont Avenue, Data Science Institute , 3rd floor (map).