Mani Rupak Gurram

About Mani Rupak Gurram

I am a PhD candidate in Electrical & Electronics Engineering with a Master’s in Computer Science, working at the critical intersection of silicon design, hardware security, and machine learning. My passion lies in ensuring that next-generation computer systems and cryptographic hardware are not just high-performing, but fundamentally secure from the ground up.Throughout my academic and research career, I have driven the full lifecycle of hardware development. I specialize in everything from pre-silicon RTL design (Verilog/SystemVerilog) and Formal Verification using SAT Solvers, to post-silicon bring-up and debug using JTAG, oscilloscopes, and high-precision EM probes.Currently, my PhD research at Prairie View A&M University focuses on validating the integrity of Post-Quantum Cryptography (PQC) cores. I leverage EM Side-Channel Analysis (EM-SCA) and custom Python-driven automation to process terabytes of physical trace data, actively hunting for physical leakage points. Previously, during my MS research, I developed ML pipelines capable of detecting hardware Trojans.I am actively looking to connect with teams pushing the boundaries of hardware engineering, ASIC/VLSI design, and silicon validation.

Experience

1. Graduate Research Assistant

   Prairie View A&m University

   Aug 2025 — Present · Prairie View, TX, US

   As a PhD researcher in the Electrical & Electronics Engineering department, my work bridges the gap between theoretical cryptography and physical hardware resilience. I specialize in validating the integrity of Post-Quantum Cryptography (PQC) cores using a combination of formal verification and physical side-channel analysis.Key technical contributions include:Silicon Security Verification: Utilizing Formal Methods and SAT Solvers to mathematically prove hardware security properties and ensure pre-silicon design integrity.Post-Silicon Debug & Validation: Conducting high-precision EM Side-Channel Analysis (EM-SCA) to identify physical data leakage points, leveraging ChipWhisperer for real-time hardware vulnerability testing.Hardware Automation: Engineered custom Python-driven analysis platforms to process terabytes of physical trace data, which improved our vulnerability detection efficiency by 40%

Education

• Prairie View A&M University

  Doctor of Philosophy - PhD, Electrical and Electronics Engineering

• Wright State University

  masters, Computer Science