EDUCATION
University of Notre Dame
Computer Science Ph.D.
2018-2023
Ball State University
Computer Science M.S.
2015-2017
Ball State University
Computer Science B.S.
2012-2014
Xi'An University of Post and Telecommunication
Computer Science B.S.
2011-2015
RESEARCH
Short Video, Smart Home, Activities Recognition, Machine learning.
ABOUT
Shangyue Zhu, Assistant Professor of Computer Science at Central Washington University.
Univeristy of Notre Dame Computer Science Ph.D..
WORK EXPERIENCE
Central Washington University
Assistant Professor of Computer Science | Sep 2023 - present
The main work included
- Machine Learning.
- Data Mining.
- Research directions: Motion Recognition, Gesture recognition, Short Videos.
AT&T Labs Research
Technical Services Research Internship | Jun - Aug 2021
The main work included
- Test the performance of short videos on various platforms.
- Build algorithms to analyze short video pre-loading mechanisms.
- Short video quality, playing duration, and data consumption analysis from encrypted streaming traffic.
FIGUR8. Inc
Software Development Engineer Internship | Feb - Jul 2018
The main work included
- iOS App development. Developed a video player based on Swift.
- Network interface. Developed an API to connect the sensors database with website.
- Data Science & Machine learning.
Use RNN to recognize jumping features from the wearable sensors.
RESEARCH EXPERIENCE
Short Video wastage. (2022)
The project aims to understand the causes of short video data wastage in terms of viewing time and buffering.
Analyzed the reasons for the waste of different short video servers at the pre-load level.
Designed an algorithm to reduce the short video wastage from pre-loading and buffer level.
Measurement Study across Short Video Services. (2021)
This work provided a comprehensive comparison of four popular
short video services. In particular, the work focus on exploring content characteristics and evaluating
the video quality across resolutions for each service.
Congestion Prediction in WiFi Video Streaming. (2020)
This research aims to detect the network changes to deliver a better rate adaptation mechanism for
adaptive video streaming. This research proposes an algorithm, Congestion Prediction-DASH (CP-
DASH), which is designed to prevent stalling and maximize video quality.
Jump Activities Recognition. (2018)
The purpose of this research is to extract the motion information when jumping, such as jumping
up and down. Through Recurrent Neural Network modeling (RNN), the movement characteristics of the jump
are separated, and extracted the jump information to recognize the specific activities.
Activity Recognition based on radar (Walabot) sensor. (2017)
The research proposes an ambient radar sensor-based solution to recognize the activities
that humans normally perform in indoor environments.
The radar sensors detect abnormal and instantaneous motion situations by high-frequency radio signal.
Tracing location based on Ultrasonic sensor. (2016)
This research work designs and develops a noninvasive distance-based user localization and tracking solution, DiLT, for smart systems.
DiLT consists of mechanical ultrasonic beam-forming designs to track the motion of the user.