CONTACT

zhush@cwu.edu

gh-shangyue

linkedin-shangyue

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.

Dr.Shangyue Zhu

- Assistant Professor

- Department of of Computer Science

- College of Engineering

- Central Washington University at Des Moines

WORK EXPERIENCE

Central Washington University

Assistant Professor of Computer Science | Sep 2023 - present

The main research work included

  • Human Activity Recognition Using Smart Sensors: Designed systems using ultrasonic and RF technologies, combined with signal processing and deep learning models (CNNs, RNNs), to classify indoor human motion for smart home and health monitoring applications.
  • Cross-Modality Signal Embedding and Detection: Explored methods to embed and detect hidden binary signals across audio and video modalities using neural networks, enabling robust information retrieval without access to original content.
  • Short Video QoE Optimization: Developed the KBL framework to improve streaming performance on mobile platforms, introducing a novel efficiency metric and achieving up to 58% data savings while maintaining high playback quality in variable network environments.

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

Epileptic Motion Detection. (2025)

This project develops a sensor-based system for detecting and classifying human motion in indoor environments, with a focus on epilepsy-related pathology. By integrating IMU sensors (accelerometer and gyroscope) and ultrasonic sensing. The goal is to enable real-time health monitoring for elderly individuals or patients prone to epileptic seizures, apply machine learning models like CNNs and RNNs to classify motion types. The project emphasizes robustness in noisy environments and practical deployment in smart home settings.


Short Video wastage. (2023)

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.

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