Jaydeep Borkar

(he/him/his)

PhD Student

Jaydeep Jitendra Borkar
  • Trustworthy machine learning
  • Data privacy & security
  • BS in Computer Engineering, Savitribai Phule Pune University — India

Jaydeep Borkar is a doctoral student at the Khoury College of Computer Sciences at Northeastern University, advised by Alina Oprea. His doctoral research, which he began in 2021 and expects to complete in 2026, focuses on trustworthy machine learning—how machine learning models can be made fair, safe, robust, and privacy-preserving before they are deployed. This includes elements of security and adversarial machine learning.

To this same end, Borkar is a founding organizer of the Trustworthy ML Initiative, which aims to lower the entry barriers into trustworthy machine learning. He maintains TensVect, a GitHub page with more than 150 linked resources for computer science students. His project COVID Letters—in which he and his colleagues penned anonymous letters to spread positivity and combat anxiety and loneliness during the pandemic—was named as a top submission in a challenged conducted by the Internet and Mobile Association of India and UNICEF India.

Borkar presented his work “Simple Transparent Adversarial Examples” at the ICLR 2021 Workshop on Security and Safety in Machine Learning Systems. Before joining Khoury College, he was an external research student at the MIT-IBM Watson AI Lab, where he researched adversarial robustness of deep learning models.

Outside of computer science, Borkar has performed volunteer work, including chat support to rescue stranded people during the 2018 Kerala floods in southern India. He has won several prizes for garba—a type of Indian folk dance—and speaks four languages: English, Hindi, Marathi, and Gujarati.

  • Simple Transparent Adversarial Examples

    Citation: Jaydeep Borkar, Pin-Yu Chen: Simple Transparent Adversarial Examples. ICLR 2021 Workshop on Security and Safety in Machine Learning Systems.