CONTACT

szhu7@nd.edu

gh-shangyue

linkedin-shangyue

EDUCATION

University of Notre Dame

Computer Science Ph.D.

2018-present


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, Univeristy of Notre Dame Computer Science Ph.D. Candidate, under the supervision of Prof. Aaron Striegel.

WORK EXPERIENCE

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.

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