We proposed MagX, the first untethered and mobile magnetic tracking platform for fine-grained hand tracking. Exemplary applications includes AR/VR interaction and face-touching detection.

This work presented B-Auth, a vehicle battery-based driver authentication platform. We discovered the connection between battery voltage with the driver's behavior, e.g., braking, acceleration, using wipers, etc.

LibreCAN can be used for automated translation of in-vehicle data, thus making the data flow in cars "transparent" to developers. In the future, we may be able to build apps on cars as easy as developing apps on smartphones!

This work presents TurnsMap, an IoT + AI framework for automatically detecting if a left turn is safe, e.g., whether it has protection for left-turning cars. Democratizing this information can help assure traffic safety for various transportation applications, e.g., navigation and ride-sharing apps.

This work presents Dri-Fi, a solution that enables automotive apps to identify the person behind-the-wheel by only using mobile sensors. The capability of identifying driver is essential for personalized service/assistance for the driver and his/her designated parties, thus can benefit various automotive apps

LocBLE is able to locate specific location of any surrounding Bluetooth low energy (BLE) beacons by justing using your smartphone. Comparing to existing coarse-grained BLE ranging applications, LocBLE is capable of enabling various use cases in Internet-of-Things. This is a collaborative work with Hewlett Packard Labs during my internship.

This work introduced a vehicle steering detection middleware called V-Sense which can run on commodity smartphones without additional sensors or infrastructure support.

This work investigated vulnerability in MU-MIMO. Focus on plaintext feedback of estimated channel state information (CSI) from clients to the APs. We have found a malicious user could use sniff attack power attack by utilizing the vulnerability in existing CSI feed scheme.