PhD in ECE at UCSD
I am a PhD student at University of California San Diego (UCSD) working in the innovative Wireless Communication, Sensing and Networking Group (WCSNG) advised by Prof Dinesh Bharadia.
My research interest lies in developing scalable deep learning systems that perceive and reason about the physical world, with a focus on autonomous driving. Currently I'm building radar foundation models and sensor-fusion frameworks that unify simulation with real-world data for robust scene understanding. A core theme is for a modular architecture that integrates diverse sensors (radar, LiDAR, cameras) while improving interpretability and generalization.
I completed my undergraduate studies at Indian Institute of Technology Hyderabad, where I earned my B.Tech in Electrical Engineering with a minor in Computer Science. During my undergraduate years, I was fortunate to be advised by Prof. Aditya Siripuram and Prof. Ayon Borthakur on projects spanning graph learning, neural networks, and signal processing.
Besides work, I enjoy playing a lot of sports such as ultimate frisbee, swimming and badminton. I love listening to music and my favourite activity to unwind is to drive! I also represented my squash team at multiple competitions such as Milan and Diesta during my undergraduate.
I am broadly interested in the intersection of sensing, machine learning, real-world perception systems and engineering. I am very passionate about developing scalable deep learning systems that have real-world impact and drive innovation.
We present C-Shenron, a radar simulation framework integrated into CARLA that generates realistic radar measurements by fusing LiDAR and camera data. Our framework supports configurable radar parameters and demonstrates that radar-camera fusion models achieve performance equivalent to LiDAR-camera baselines on CARLA leaderboard metrics.
C-Shenron is the first realistic radar simulator that utilizes LiDAR and camera sensors to generate high-fidelity radar ADC measurements from physics-based modeling. We demonstrate improved performance in end-to-end driving scenarios and aim to rekindle interest in radar-based self-driving research.
We propose SPELA, a learning algorithm that operates with local loss functions using a single forward pass, significantly reducing memory and computational requirements. SPELA eliminates gradient propagation while demonstrating competitive performance with backpropagation on standard benchmarks, making it ideal for resource-constrained edge AI applications.
We propose an algorithm for simultaneous data restoration and graph structure learning using an interpretable neural network designed from unrolling framework. Our closed-loop feedback mechanism guides graph learning based on inpainting performance, achieving robust results under noisy conditions on sensor network datasets.
Throughout my academic journey, I have gained valuable industry and research experience through internships and leadership roles.
Designed and implemented a cross-talk cancellation algorithm for spatial audio playback. Developed an efficient low-order filter approximation using bi-quad and all-pass filters.
With Prof. Aditya Siripuram
Assistant for Linear Systems & Signal Processing and Probability for AI. Conducted tutorial sessions and formulated questions for exams.