Team


Prospective students: I am actively looking for self-motivated students who interested in ubiquitous sensing and applied machine learning. Please drop me an email if you're excited!

I am fortunate to work with the folloiwng talents. Their motivation and curiosity to explore the unknown are the eternal driving forces of our team.

Master Student
Incoming PhD Student
Undergraduate Student
Undergraduate Student
You
Contact us now!

Research


Much of my thesis work focuses on empowering the next-generation transportation ecosystem that is comprised of vehicles, environment, and humans. This research includes a suite of novel and scalable sensing technologies for facilitating safer and more efficient transportation that has a profound impact on people.

The synergy of my thesis works is illustrated in the above figure. My research investigated ubiquitous sensing, vehicular system, and wireless communication. These three elements are cornerstones for tackling real-world challenges on our roads.

Latest News


Nov. 2020
One paper on authenticating drivers using batteries is accepted by ACM UbiComp.
Sept. 2020
I will serve as a publicity chair of MobiHoc 2021. The CFP will be out soon. Stay tuned!
Nov. 2019, CIKM 2019
One (paper) was accepter and presented at CIKM workshop on AI for Transportation
Oct. 2019, CCS 2019
LibreCAN got accepted by CCS'19
Oct. 2019, Talk
CSCI3511 (Hardware-Software Interface) at CU Denver to introduce our recent progress in human mobility interaction.
Sept. 2019, Talk
Gave a talk at UbiComp'19 on TurnsMap.
Jul. 2019, UbiComp 2019
TurnsMap got accepted by UbiComp'19
Read more

Papers


LibreCAN: Automated CAN Message Translator
CCS 2019

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!

Paper | Website

TurnsMap: Enhancing Traffic Safety with Crowdsensing and Deep Learning
UbiComp 2019

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.

Paper | Website | Slides

Mobile IMUs Reveal Driver's Identity from Vehicle Turns
ArXiv Preprint, 2017

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

Paper

Locating and Tracking BLE Beacons with Smartphones
CoNEXT, 2017

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.

Paper | Demo | Slides

Invisible Sensing of Vehicle Steering with Smartphones
MobiSys, 2015

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

Paper | Demo | Slides

Vulnerability and Protection of Channel State Information in Multiuser MIMO Networks
CCS, 2014

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.

Paper

Personal


I enjoy the teamwork and hustle in competitive sports, especially basketball. Read more from my blog post.