CV

Download PDF Resume

Education
University of Illinois Urbana-Champaign
PhD in Computer Science
Advised by Prof. Klara Nahrstedt. Previously worked with Prof. Deepak Vasisht and Prof. Matthew Caesar.
University of Illinois Urbana-Champaign
Master of Computer Science
University of Illinois Urbana-Champaign
B.S. in Statistics & Computer Science, Highest Distinction
Internship
Google — Android Location Team
Student Researcher  ·  Mountain View, CA
Supervised by Dr. Roy Want. Android Location Team — the team behind indoor positioning in Google Maps and Android Location Services.
  • Designed an advanced indoor localization prototype combining Wi-Fi RTT and IMU sensor fusion on Pixel, achieving substantially lower latency and higher accuracy than Google's production Fused Location Provider (FLP).
  • Implemented a passive Wi-Fi listening framework reusing cached FLP scan results for continuous RTT-based ranging, reducing localization update latency by 57% and eliminating blackout periods.
  • Built a high-precision PDR module leveraging Android's step detector and orientation sensors, fused with Wi-Fi RTT via a multi-state Kalman filter, achieving 0.8 m accuracy at 90% CDF.
  • Contributed to integrating Wi-Fi RTT capabilities into Google's FLP framework for next-generation indoor localization.
Research Experience
Crowdsourcing Ubiquitous Indoor Localization with Wi-Fi Ranging
Supervised by Prof. Deepak Vasisht, UIUC
  • Designed and implemented PeepLoc, a scalable indoor localization system using non-cooperative Wi-Fi Ranging and IMU-based PDR, requiring no infrastructure or PHY-layer access.
  • Proposed a probabilistic backend for geolocating APs using one-way ToF estimates fused with PDR trajectories, solving a joint non-linear least squares problem under clock offset uncertainty.
  • Demonstrated PeepLoc outperforms Android FLP by over 40% in mean error (3.41 m vs. 7.71 m) across four real-world campus buildings.
Swarm-Based GPS Spoofing Detection by Multimodal Sensor Fusion
Supervised by Prof. Matthew Caesar, UIUC
  • Proposed an EKF-based sensor fusion architecture combining IMU, inter-drone communication, and signal strength analysis to detect GPS spoofing on resource-constrained drones.
  • Demonstrated significant improvements in location accuracy and error variance reduction over baselines via simulation on real-world mobility traces.
Probabilistic Programming Robustness
Supervised by Prof. Sasa Misailovic, UIUC
  • Contributed to building a system for automatically transforming probabilistic programs to enhance robustness against data variability.
  • Conducted robustness assessments of probabilistic programs using Stan and R.
Teaching
CS 437 Topics in Internet of Things
Graduate Teaching Assistant, UIUC
Graduate Teaching Assistant, UIUC
Honors & Awards
* **MobiCom 2025 Best Poster Runner-Up** * MobiCom 2025 Student Travel Grant * IEEE MILCOM 2023 Student Travel Grant — [Link](https://milcom2023.ieee-milcom.org/) * UIUC Fall 2022 Teachers (TA) Ranked as Excellent by Their Students — [Link](https://citl.illinois.edu/docs/default-source/teachers-ranked-as-excellent/tre-2022-fall.pdf) * UIUC Dean's List FA19, SP20, FA21, SP22
Service
* ISIBER 2026 (Reviewer) * IROS 2026 (Reviewer) * PRICAI 2025 (Reviewer) * OSDI 2025 (Artifact Evaluation Reviewer) * SIGCOMM 2025 (Artifact Evaluation Reviewer) * EuroSys 2026 (Artifact Evaluation Reviewer)
Skills
* **Programming**: C/C++, Java, Python, Matlab * **Tools & Frameworks**: PyTorch, TensorFlow, NumPy, OpenCV, ROS/ROS2, CMake, Gazebo * **Techniques**: Sensor Fusion, Kalman Filtering, SLAM, Object-Oriented Design, Unit Testing