Junjue Wang


9127 Gates Hillman Center, 4902 Forbes Ave


junjuew dot cs dot cmu dot edu
Github / Twitter


I am a final year CS Ph.D. student at Carnegie Mellon University, graduating in May 2020. My PhD dissertation focuses on system support for machine learning workload. In particular, my dissertation addresses the problem of prototyping and scaling real-time computer vision applications to augment human cognition on wearable devices and edge clouds (demos). I built prototype applications and designed system optimizations to scale video analytics at the edge. I have over ten publications in top-tier computer system conferences and journals. Previously, I interned in research and engineering roles at Google, Qualcomm, and Microsoft.

My PhD advisor is Prof. Mahadev Satyanarayanan (Satya). You can find my PhD thesis proposal here and slides here.

Meet with Me

You can schedule a meeting with me here.

Research Experience

Towards Scalable Edge-Native Applications (SEC'19)

Graduate Research Assistant, Carnegie Mellon University, Advisor: Prof. Satya

Paper Slides

  • Leveraged application characteristics to reduce offered load.
  • Proposed and evaluated a profiling-based resource allocation mechanism for edge servers.
  • Reduced offered load by 40 % and maintained application quality of service in severely loaded edge servers.

Bandwidth-efficient Live Video Analytics for Drones via Edge Computing (SEC'18)

Graduate Research Assistant, Carnegie Mellon University, Advisor: Prof. Satya

Paper Slides Code and Datasets

  • Designed an edge-based architecture enabling live video analytics on small autonomous drones for search and rescue tasks.
  • Proposed and evaluated four different techniques to reduce wireless bandwidth usage when offloading computation, e.g. early discard, just-in-time learning, reachback, and context-awareness.

A Scalable and Privacy-Aware IoT Service for Live Video Analytics (MMSys'17 and TOMM)
Best Paper Award

Graduate Research Assistant, Carnegie Mellon University, Advisor: Prof. Satya

Conference Paper Journal Paper Slides Demo Talk

  • Designed and implemented a real-time IoT privacy mediation system that selectively blurs faces
  • Adopted a cloudlet-based approach to achieve separation of trust and ensure platform integrity
  • Leveraged object tracking to speed-up face detection and DNN-based face recognition

Quantifying the Impact of Edge Computing on Mobile Applications (APSys'16)

Graduate Research Assistant, Carnegie Mellon University, Advisor: Prof. Satya

Paper Slides

  • Measured network latency for cloudlets, small-scale datacenters located at the edge of the Internet, under Wi-Fi and 4G LTE networks
  • Measured energy consumption on mobile devices when offloading heavy computation to cloudlets
  • Analyzed system design trade-off between response latency and energy consumption in edge computing
  • Maintained and debugged in-lab cellular base station

UbiK: Ubiquitous Keyboard for Small Mobile Devices (MobiSys'14)

Undergraduate Research Assistant, University of Wisconsin-Madison, Advisor: Prof. Xinyu Zhang

Paper Slides Demo

  • Designed and implemented the paper keyboard UbiK, leveraging audio and motion signals received by a smartphone to detect and recognize different keys
  • Performed feasibility tests and discovered multipath fading audio signatures of keystrokes on conventional surfaces
  • Modified Android kernel TinyALSA audio driver to enable dual microphone recording and double feature space
  • Benchmarked and evaluated UbiK, demonstrating faster typing experiences than on-screen keyboards in user studies


Google Scholar

Industry Experience

Research Intern @ Google

Mountain View, California

  • Interned at Applied Privacy Research team.
  • Designed an end-to-end machine learning system to automatically understand privacy-related user feedback and assign fine-grained privacy tags to users' text feedback.
  • Implemented and trained a recurrent neural network (RNN) using Tensorflow to capture semantic meaning of feedback text.
  • Achieved ~85% accuracy of the tag assignment.

Digital Hardware Interim Engineering Intern @ Qualcomm

San Diego, California

  • Developed a python framework using OpenOffice API to automatically generate SoC clock synthesis constraint files from clock signal catalogs in Excel
  • Automated SoC clock cross domain checking file generation workflow

Program Manager Intern @ Microsoft

Beijing, China

  • Developed an Android application to lock all UIs in landscape mode for synchronizations of mobile phones and In-Vehicle Infotainment system
  • Researched and compared different methods to achieve audio synchronization between Android phones and In-Vehicle Infotainment system


Ph.D. in Computer Science

Carnegie Mellon University
Pittsburgh, Pennsylvania

B.S. in Computer Science (4.0/4.0)

University of Wisconsin-Madison
Madison, Wisconsin
  • Graduated with the Highest Distinction

B.S. in Computer Engineering (3.99/4.0)

University of Wisconsin-Madison
Madison, Wisconsin
  • Graduated with the Highest Distinction
  • Graduated with Honor in Research

Honors & Awards

  • Winner of 2018 Siemens FutureMaker Challenge (140k Research Grant)
  • SEC’18 Student Travel Grant
  • MMSys’17 Student Travel Grant
  • APSys’16 Student Travel Grant
  • UW-Madison Qualcomm Innovation Competition Best Prototype Award
  • UW-Madison Hilldale Research Fellowship
  • UW-Madison W.G. Kirchoffer Memorial Scholarship Award
  • Rockwell Collins Scholarship
  • UW-Madison Sophomore/Freshman Engineering Award