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

Paper Demo

• Assisted user study on the latency requirements of wearable cognitive assistance

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

• 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%.

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

• 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

• 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

• 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.

• 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

• 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

• Graduated with the Highest Distinction

• Graduated with the Highest Distinction
• Graduated with Honor in Research