Hello! I'm a fourth-year Electronics and Communication Engineering student at NIT Warangal, drawn to the intersection of hardware and artificial intelligence.
My work spans across designing IC's, writing verilog codes and implementing on FPGA. Outside the lab, I lead technical initiatives, stargaze through telescopes, and keep learning because the curiosity never stops.
Skills
Languages
C++
Python
Verilog
MATLAB
Tools & Platforms
Xilinx Vivado
LTSPICE
Cadence
Advanced Design System
Coursework
Low Power VLSI
RFIC
CMOS VLSI
Digital Circuits
DSP
Embedded and Real Time
Operating Systems
Data Networks
Experience
Summer Research Intern
May – Jul 2025
IITM C-DOT Samgnya Technologies Foundation · Chennai
- Worked at IIT Madras's Centre for Quantum Information, Communication and Computing (CQuICC) under Prof. Anil Prabhakar, on characterising entanglement in fibre-based photon sources.
- Set up and tested a polarisation beam splitter (PBS) end-to-end, then built an On–Off Keying optical link to evaluate its performance under real signal conditions.
- Used motorised polarisation controllers to automate alignment, reducing manual calibration overhead significantly.
Team Leader — Smart India Hackathon (Hardware)
Sep 2024
Team Drona · NIT Warangal
- Led the design of a drone system for locating survivors in collapsed-structure rescue scenarios — a problem with few good existing solutions.
- Spec'd the airframe for 40 kg payload and harsh field conditions: sub-zero temperatures, high-altitude low-pressure air, and strong crosswinds along the Indo–China border.
Projects
Verilog · FPGA
Fast HUB Floating-Point Adder
Designed a Half-Unit-Biased double-path floating-point adder with configurable mantissa (10–60 bits) and exponent (6–12 bits). Achieved 34% average speedup over single-path designs on Xilinx Virtex-6, with only 26% area and 13% energy overhead.
Machine Learning · KNIME
Grocery Sales Forecasting
Built a regression pipeline on KNIME to forecast retail sales from product and store attributes, reaching 89% accuracy. Identified product category and store location as the two strongest predictors.