About

Hello, I am Nivesh, a UG 4th year at IIT Bombay in the CSE department. I am highly interested in reasearch in CryptoGraphy and Game Theory and enjoy learning new things in these fields. I am currently working with Prof. Chetan Kamath on Timed Cryptographic Primitives and Prof. Swaprava Nath on Intermediary Network Auctions.

You can download my resume/CV here.

I am always eager to learn new things and challenge myself. I love traveling and exploring new places, meeting new people and learning about their cultures. I am a huge nature lover and food enthusiast. My idea of a perfect day is a refreshing morning trek followed by a hearty meal and good music. I believe in living each moment to its fullest and always trying your best, as that’s the best anyone can do.

Experiences

Timed Cryptography

Jan 2025 – Present
IIT Bombay
  • worked under Prof. Chetan Kamath on understanding Timed Cryptographic Primitives like Time Lock Puzzles and Verifiable Delay Functions

Revenue Maximising in Intermediary Network Auctions

Jan 2025 – Present
IIT Bombay
  • Modeled the problem using graphs and Bayesian settings, specifying type distributions and valuation functions and designed a mechanism for simple graphs
  • Proved hardness of general auction design in tree networks by reducing from UDMO

Quantitative Trader

Summer 2025
Graviton Research Capital
  • Worked on improving non linear model training pipeline by normalizing the input features given, optimising loss functions and other paramters achieving an improvement of 2-4 percent
  • Worked on a simulator for testing models, allowing us to compare them faster and provide more flexibility

Positions of Responsibility

Teaching Assitant - Theoretical Foundations of Cryptography

Autumn 2024
IIT Bombay
  • Helped in making solutions for quizzes and grading them. Also conducted paper-solving sessions for all the exams

Mentor – Brain Tumor Detection

Winter 2023
Analytics Club, IIT Bombay
  • Mentored students in CNN architectures and PyTorch implementation, achieving 89% accuracy in brain tumor detection models
  • Designed curriculum covering neural network fundamentals and NumPy implementations to reduce framework dependency