|
jaijborkar at gmail dot com I'm a Computer Science PhD student at Northeastern University advised by David A. Smith and a Visiting Researcher at Meta on GenAI Safety Alignment Team 🦙 in New York City. Previously, I was an external research student at MIT-IBM Watson AI Lab advised by Pin-Yu Chen where I worked on adversarial machine learning. I'm one of the founding organizers of the Trustworthy ML Initiative, which aims to lower the entry barriers into trustworthy machine learning. Twitter / Semantic Scholar / Google Scholar / LinkedIn / GitHub / Listening! |
|
|
I study privacy and safety in language models. I'm currently interested in understanding various questions related to training data memorization, such as (1) different factors that lead to memorization (2) privacy implications of memorization (3) better ways to define and evaluate memorization, and (4) understanding memorization vs generalization. Please get in touch if you'd like to collaborate on research or go mountain biking. |
|
Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training Jaydeep Borkar, Matthew Jagielski, Katherine Lee, Niloofar Mireshghallah, David A. Smith, and Christopher A. Choquette-Choo. Findings of the Association for Computational Linguistics (ACL) 2025 Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon USVSN Sai Prashanth, Alvin Deng, Kyle O’Brien, Jyothir S V, Mohammad Aflah Khan, Jaydeep Borkar, Christopher A. Choquette-Choo, Jacob Ray Fuehne, Stella Biderman, Tracy Ke, Katherine Lee, and Naomi Saphra. International Conference on Learning Representations (ICLR) 2025 Mind the gap: Analyzing lacunae with transformer-based transcription Jaydeep Borkar and David A. Smith Workshop on Computational Paleography, ICDAR 2024 What can we learn from Data Leakage and Unlearning for Law? Jaydeep Borkar Workshop on Generative AI + Law (GenLaw), ICML 2023 [poster] [preprint] Simple Transparent Adversarial Examples Jaydeep Borkar and Pin-Yu Chen Workshop on Security and Safety in Machine Learning Systems, ICLR 2021 [poster] [preprint] |
|
June 2025 I have joined Meta GenAI in NYC as a Visiting Researcher. May 2025 Privacy Ripple Effects has been accepted to the Findings of ACL 2025. See you in Vienna! May 2025 Gave a talk at the Google ML Privacy Seminar on Privacy Ripple Effects. March 2025 Had fun attending ACM CS & Law in Munich. February 2025 My wonderful co-authors and I wrote a new paper on memorization of personal information in language models. Check it out! January 2025 Our paper on Memorization in LMs got accepted at ICLR! April 2024 Giving a guest lecture on privacy and security in LLMs for CS 5100 Foundations of AI class at Northeastern June 2023 Stoked to present my work on memorization + law in LLMS at the first Generative AI + law (GenLaw) workshop at ICML in Honolulu, Hawai'i. |
|
Organizing The Trustworthy ML Initiative (together with Hima Lakkaraju, Sara Hooker, Sarah Tan, Subho Majumdar, Chhavi Yadav, Chirag Agarwal, Haohan Wang, and Marta Lemanczyk).Program Committee Workshop on Privacy in Natural Language Processing, ACL 2024. |
|
I enjoy (mountain) biking in Boston/Cambridge (Fells is my favorite), playing tennis, hanging out at bookstores and libraries, and Bollywood dancing in my free time. |
|
I believe in the power of kind and empathetic listening, and I think that everyone deserves a good listener in their life. However, there are so many of us who don't have good listeners. If you want someone to listen to you, I'm just an email away. I'll try my best to listen to you. |
|
Thanks to Jon Barron for the template! |