Guanghan Wang

University of Toronto

GPA: 3.93

Software Engineer - PEY Intern

• Learned the breadth of technical activities that are required for a modern HLD program
• Enabled Intel® FPGA AI Suite customers to use Python API from OpenVINO
• Designed and implemented an automatic regression test triager from scratch to minimize human efforts
• Completed and improved a refreshed Schedule Viewer as a part of the Intel® oneAPI FPGA Reports Tool
• Porting typed pointers to opaque pointers in Intel® LLVM FPGA compiler code

Teaching Assistantship

Summer Research on Security and Machine Learning

The objective of the research is to determine if log files can predict the paths executed in an application. To begin, I collected logs and code coverage data using a fuzzer based on AFL. While collecting data, I constructed an LSTM neural network to predict the code region coverage of a log segment. By the end of the summer, the model had achieved an accuracy of approximately 90% on the openssh/wolfssh pair. Later on, I developed a decision tree to address the same problem and improved the accuracy to 99.7%.

Summer Research on Audio Adversarial Machine Learning

Under the supervision of Prof. Nicolas Papaernot in the ClearHans Lab, I worked on audio adversarial machine learning and implemented a genetic algorithm to tackle the black box setting of speaker verification. During this process, I self-learned NumPy and TensorFlow. In the end, I achieved the goal of reducing the model's accuracy to below 1% through imperceptible genetic mutations of the input signal.

The result is part of the paper On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples