I graduated in 2025 from BITS Pilani, with a dual degree: a Bachelor of Engineering in Electrical and Electronics, and a Master of Science in Physics.
During undergrad, I worked on atmospheric water vapor retrieval from satellite occultation data at the Space Applications Centre (ISRO), finger flexion estimation from neural recordings at CSIR-CEERI, and peri-urban area estimation and arrhythmia classification at BITS Pilani. The work was guided by Prof. Manik Gupta, Prof. Rajesh Tripathy, and Dr. Satya Prakash Ojha.
As part of my undergrad I interned at Texas Instruments and Google. At Texas Instruments, I worked on developing an assertion-based formal verification IP for a real-time microcontroller platform, with a focus on reusable, property-driven verification infrastructure. Alongside this, I spent some time exploring whether graph-based representations of finite-state machines could be fed into GNNs to reason about protocol behavior, more out of curiosity than conviction. At Google, I worked on infrastructure for generating and integrating subsystem-level RTL based on architectural configuration. The work touched the full spectrum of auxiliary logic bring-up, covering power intent, clock/reset domain crossing, linting, and register collateral, and gave me a systems-level view of how large SoC blocks are stitched together.
I now work full-time at Google as a Silicon Engineer, as part of a first-party cache-coherent interconnect design team. At its core, this is the hardware that keeps a chip's processors, memory controllers, and accelerators in agreement about what is in memory. My work spans microarchitecture, RTL implementation, and power estimation alongside maintaining general design plumbing flows.
IEEE Access ยท 2025
SERN-AwGOP detects atrial fibrillation from ECG with high accuracy, robustness to noise, and strong cross-dataset generalization.
IEEE Geoscience and Remote Sensing Letters ยท 2024
RBF-SVM model maps peri-urban expansion near Hyderabad, revealing 108% growth.
IEEE Transactions on Geoscience and Remote Sensing ยท 2023
ANN model retrieves atmospheric water vapor from COSMIC-2 data, outperforming existing methods.