My research interest is in HCI. I study human input performance, design input techniques for essential interaction tasks in emerging environments and realize them with computing methods (probability, applied machine learning, signal processing, etc). I have worked on text entry in AR (CHI'16) & VR (UIST'15) and smartwatch interactions (CHI'17).
I received my Bachelor's degree from the Department of Computer Science and Technology at Tsinghua University in 2014. Here is my resume.
Float is a wrist-to-finger interaction technique that enables one-handed and touch-free input on smartwatches with high efficiency. With Float, a user tilts the wrist to point and performs an in-air finger tap to click. We realize Float using only commercially-available built-in sensors. Particularly, we detect the finger taps based on the photoplethysmogram (PPG) signal acquired from the heart rate monitor sensor (The supplementary file below provides a detailed destripiton of the PPGTap).
CHI 2016 | Honorable Mention Award (Top 5%)
1D-Handwriting is a unistroke gesture technique that enables text entry on a one-dimensional interface. We map two-dimensional handwriting to a reduced one-dimensional space, while achieving a balance between memorability and performance. After an iterative design, we derive a set of ambiguous two-length unistroke gestures, each mapping to 1-3 letters. 1D-Handwriting outperforms a selection-based technique for both letter and word input.
UbiComp 2016 Workshop: UnderWare
SkinMotion reconstructs human motions from skin-stretching movements. We discuss the potential applications of SkinMotion. In addition, we experimentally explore one specific instance ‒ finger motion detection using the skin movement on the dorsum of the hand. Results show that SkinMotion can achieve 5.84° estimate error for proximal phalanx flexion on average. We expect SkinMotion to open new possibilities for skin-based interactions.
Air Typing Keyboard (ATK) enables freehand ten-finger typing in the air based on 3D hand tracking data. We empirically investigate users' mid-air typing behavior, and examine fingertip kinematics, correlated movement among fingers and 3D distribution of tapping endpoints. We propose a probabilistic tap detection algorithm, augmenting Goodman's input correction model to account for the ambiguity in distinguishing tapping finger.
|Tsinghua University||09/2014 - present|
|Ph.D. student in Computer Science and Technology|
|Advisors: Prof. Yuanchun Shi|
|Tsinghua University||09/2010 - 06/2014|
|B.Eng. in Computer Science and Technology|
|GPA rank: 13/129, Graduate with honors in the Department of CS|
|Intern at , a leading company in Augmented Reality in China||10/2016 - 12/2016|
I learned from and worked with experienced colleagues in the SLAM group. After a period of code cleanup work, I have a preliminary understanding of the basic algorithms of Augmented Reality. I also built a pipeline tool which takes image sequence as input and outputs the 3D mesh model reconstructed from them with open source softwares.
|Teaching Assistant, Human-Computer Interaction: Theory and Technology, THU||09/2015 - 01/2016|
|09/2016 - 01/2017|
|National Scholarship by Ministry of Education||2016|
|Honorable Mention Award by ACM CHI 2016||2016|
|Outstanding Graduate by the Department of CS, Tsinghua University||2014|
|Comprehensive Excellence Scholarship by Tsinghua University||2011, 2013|
|Academic Excellence Scholarship by Tsinghua University||2012|
|C++, Java, Python, C#, MATLAB, Cooking|
Updated in January 2017