Professional History
A comprehensive timeline of my academic and professional journey in formal verification and security research.
Teaching Assistant for ECE 122.
Teaching Assistant for ECE 122.
Teaching Assistant for ECE 361 (Fundamentals of Electrical Engineering).
Teaching Assistant for ECE 361 (Fundamentals of Electrical Engineering).
Teaching Assistant for ECE 304 (Junior Design Project).
Teaching Assistant for ECE 304 (Junior Design Project).
Started PhD program at UMass Amherst.
Started PhD program at UMass Amherst.
Started as Graduate Research Assistant in Khwarizmi Lab under Dr. Muhammad Taqi Raza, focusing on quantum networking security and formal verification.
Started as Graduate Research Assistant in Khwarizmi Lab under Dr. Muhammad Taqi Raza, focusing on quantum networking security and formal verification.
Awarded Rector’s Gold Medal for best senior project on Logic-Locking Security Evaluation. Developed end-to-end pipeline for security-cost tradeoffs analysis. Applied hardware obfuscation techniques on RISC-V...
Awarded Rector’s Gold Medal for best senior project on Logic-Locking Security Evaluation. Developed end-to-end pipeline for security-cost tradeoffs analysis. Applied hardware obfuscation techniques on RISC-V designs.
Graduated with Bachelor’s degree in Electrical Engineering and Minor in Computer Science.
Graduated with Bachelor’s degree in Electrical Engineering and Minor in Computer Science.
Won 2nd place in the global CSAW’22 Logic Locking Competition, a hardware security hackathon.
Won 2nd place in the global CSAW’22 Logic Locking Competition, a hardware security hackathon.
Led the design of ENIGMA, a Python framework for automated logic-locking insertion in hardware designs. Designed parametrized key-insertion system (64-256 bits).
Led the design of ENIGMA, a Python framework for automated logic-locking insertion in hardware designs. Designed parametrized key-insertion system (64-256 bits).
Implemented automated pipeline for court document processing. Fine-tuned Transformer-based models achieving 83% accuracy on case outcome prediction.
Implemented automated pipeline for court document processing. Fine-tuned Transformer-based models achieving 83% accuracy on case outcome prediction.