Graduating Soon · Open to Opportunities

I am expected to graduate in July 2026 and am actively seeking research and industry opportunities in wireless systems, RF/Wi-Fi sensing, and mobile systems.

About

I am a PhD scholar at IIT Madras working on Wi-Fi sensing, indoor localization, resource-efficient wireless systems, and the implications of sensing on network performance, energy consumption, and privacy.

News

  • Awarded the Malathi Veeraraghavan Fellowship 2025: selected as an MV Scholar (2025).
  • Our paper Less is More: Improving Wi-Fi Localization Accuracy with Fewer Scans has been accepted at COMSNETS 2026.
  • LiteTrack: Power-Efficient Wi-Fi Localization accepted at IEEE ANTS 2025.
  • Completed the PrePARe 2025 program at Samsung Research India–Bangalore.

Awards & Achievements

  • Awarded as Malathi Veeraraghavan (MV Fellowship) Scholar 2025.
  • Selected for the Samsung PrePARe Research Leadership Program, a highly competitive national-level initiative aimed at grooming future research leaders.
  • Awarded “Star TA” for exceptional teaching assistance in CS3205 and CS2300 at IIT Madras.

Research

  • Implications of Wi-Fi sensing on device battery, network scalability, and privacy.
  • Adversarial sensing & defenses for IoT devices.
  • Interference-aware ISAC design.

Publications

Less is More: Improving Wi-Fi Localization Accuracy with Fewer Scans

COMSNETS 2026 (Accepted)

We show that dense Wi-Fi scanning is unnecessary for accurate localization. A small, carefully chosen set of scans provides most of the signal strength diversity while reducing battery drain and channel contention. We propose an adaptive scan-scheduling strategy that achieves high accuracy with minimal overhead.

LiteTrack: Power-Efficient Wi-Fi-Based Indoor Localization for Smartphones

IEEE ANTS 2025

Developed during my internship at Samsung R&D. LiteTrack identifies the optimal number of Wi-Fi scans needed for robust localization and proposes an energy-aware scanning policy that retains accuracy while reducing battery consumption.

Practical Defense Against Adversarial Wi-Fi Sensing

IEEE ANTS 2024

A black-box, low-overhead perturbation defense that preserves communication quality while significantly degrading adversarial sensing models.

Improving Network Resource Utilization in Distributed Wireless Sensing

AIoT @ MobiHoc 2024

We propose selective CSI subsampling and compression to improve end-to-end sensing throughput, latency, and energy efficiency across distributed nodes.

Contact

Email: cs22d002@cse.iitm.ac.in
Lab 423, SSB Block, CSE Dept., IIT Madras
Chennai – 600036, India