CV
Education
University of Illinois Urbana-Champaign
PhD Candidate in Computer Science
Advised by Prof. Klara Nahrstedt. Previously worked with Prof. Deepak Vasisht and Prof. Matthew Caesar.
Aug 2024 – Dec 2027
University of Illinois Urbana-Champaign
Master of Computer Science
Aug 2022 – May 2024
University of Illinois Urbana-Champaign
B.S. in Statistics & Computer Science, Highest Distinction
Aug 2019 – Aug 2022
Publications
Enguang Fan*, Binh Minh Tran*, Klara Nahrstedt
32nd ACM Annual International Conference on Mobile Computing and Networking (MobiCom '26), short paper. * Equal contribution.
2026
Enguang Fan, Yifan Chen, Zihan Shan, Matthew Caesar, Jae Kim
Accepted to the 2026 IEEE Global Communications Conference (GLOBECOM).
2026
Enguang Fan, Anfeng Peng, Matthew Caesar, Jae H. Kim, Josh Eckhardt, Greg Kimberly, Denis Osipychev
IEEE Military Communications Conference (MILCOM), Boston, USA.
2023
Emerson Sie*, Enguang Fan*, Federico Cifuentes-Urtubey, Deepak Vasisht
arXiv preprint. * Equal contribution.
2025
Enguang Fan*, Emerson Sie*, Federico Cifuentes-Urtubey, Deepak Vasisht
31st ACM Annual International Conference on Mobile Computing and Networking (MobiCom), Hong Kong, China. Best Poster Runner-Up. * Equal contribution.
2025
Enguang Fan
arXiv preprint arXiv:2602.01673.
2026
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.
- Advanced the integration of Wi-Fi Round Trip Time (RTT) indoor localization into Android's Fused Location Provider (FLP) and scaled evaluation across numerous Google offices in Mountain View.
- Evaluated Google Wi-Fi hardware and enterprise-grade HPE Aruba, Cisco, and Arista access points, achieving sub-second localization latency with 90th-percentile errors of 1 m and 2 m, respectively.
- Developed and evaluated tightly coupled particle-filter and Bayesian-grid methods for fusing Wi-Fi RTT measurements with motion sensors across varied motion and ranging conditions.
- Designed and implemented APSelector, a geometry-aware AP selection algorithm that prioritizes access points providing strong geometric constraints to mitigate NLOS and positive ranging biases.
- Incorporated IMDF walkable-path constraints as probabilistic priors to reject infeasible trajectories and further improve localization accuracy.
May – Aug 2026
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 and developed an advanced indoor localization prototype combining Wi-Fi RTT and motion sensor fusion on Pixel phones, achieving substantially lower latency and higher accuracy than Google's production FLP in challenging indoor environments.
- 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 pedestrian dead reckoning module and fused it with Wi-Fi RTT through a multi-state Kalman filter, achieving 0.8 m indoor localization accuracy at 90% CDF using existing commercial Wi-Fi infrastructure.
- Contributed to integrating Wi-Fi RTT capabilities into Google's FLP framework for next-generation indoor localization.
May – Aug 2025
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.
- Developed a per-AP adaptive ranging model for NLOS multipath distortion and hardware-induced RTT slope deviations.
- Demonstrated that PeepLoc outperforms Android FLP by over 40% in mean error (3.41 m vs. 7.71 m) across four real-world campus buildings.
Oct 2024 – May 2025
Deep IMU Bias Inference for Robust Visual-Inertial Odometry
- Designed LSTM and Transformer models to infer time-varying IMU biases from measurement histories and prior bias estimates.
- Integrated learned bias estimates as unary factors in a factor-graph state estimator, improving robustness when visual tracking was degraded or unavailable.
- Evaluated the approach on real-world data from handheld devices, quadruped robots, and drones, demonstrating transfer across locomotion patterns.
Aug – Dec 2023
Swarm-Based GPS Spoofing Detection by Multimodal Sensor Fusion
Supervised by Prof. Matthew Caesar, UIUC
- Contributed to improving GPS spoofing detection on disadvantaged platforms such as lightweight drones.
- Proposed an EKF-based sensor fusion architecture combining observations across multiple sensors to detect GPS spoofing and reconstruct coordinates with confidence levels.
- Demonstrated improved location accuracy and lower error variance over baselines through simulations based on real-world mobility and sensor traces.
May – Aug 2023
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)Skills
* **Programming**: C/C++, Java, Python, Matlab * **Tools**: PyTorch, TensorFlow, NumPy, OpenCV, ROS/ROS2, CMake, Gazebo * **Techniques**: Object-Oriented Design, Unit TestingService
* IEEE Transactions on Multimedia (Reviewer) * IEEE Journal of Selected Topics in Signal Processing (JSTSP) (Reviewer) * IROS 2026 (Reviewer) * OSDI 2025 (Artifact Evaluation Reviewer) * SIGCOMM 2025 (Artifact Evaluation Reviewer) * EuroSys 2026 (Artifact Evaluation Reviewer)