Junjue Wang

9120 Gates Hillman Center, 4902 Forbes Ave
15213
Pittsburgh
USA

junjuew.github.io

junjuew dot cs dot cmu dot edu
Github / Twitter

Biography

I am a third-year Ph.D. student at Carnegie Mellon University, Computer Science Department. My research interest lies at the intersection of mobile computing, computer vision, and human-computer interaction. Particularly, I’m working on wearable cognitive assistance running on cloudlets under the guidance of Prof. Mahadev Satyanarayanan (Satya). I aim to apply recent advancement in mobile and computer vision technology to blur the boundary between the physical and virtual world, build portable and intelligent cognitive systems, and enhance users’ abilities to interact with the real world.

Research Experience

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

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

Paper Slides Video

  • 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

TPOD: Tools for Painless Object Detection

Graduate Research Assistant, Carnegie Mellon University, Advisor: Prof. Satya
  • Designed a web-based system that enables users to create state-of-art object detectors quickly without computer vision knowledge.
  • Automated creating deep neural network based object detectors using Faster-RCNN and transfer learning.
  • Implemented a proactive object labeling web interface that employs tracking for auto-annotation and effectively reduces manual labeling workload to 10%.

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

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

Paper Slides Video

  • 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

In-Kernel Key-Value Storage System

Undergraduate Research Assistant, University of Wisconsin-Madison, Advisor: Prof. Remzi Arpaci-Dusseau
  • Designed and prototyped an in-kernel hash-based key-value storage system optimized for SSD read/write characteristics
  • Implemented a C benchmark framework to evaluate system performance

Estimate GPU Speedups with Machine Learning

Undergraduate Research Assistant, University of Wisconsin-Madison, Advisor: Prof. Karu Sankaralingam
  • Rewrote 6 UT-Austin LonestarGPU CUDA benchmarks into equivalent C++ and OpenCL programs
  • Collected and examined program features with PIN and MICA binary instrumentation framework

Publications

Google Scholar

Industry Experience

Software Engineering Intern in Research @ 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

Education

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

  • 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

Blogs