About Me
I am a first-year graduate student in the Electrical and Computer Engineering department (Software Engineering & Systems track) at the University of Texas at Austin. I also work on high performance computing in deep learning as a research intern at the Texas Advanced Computing Center under the supervision of Dr. Zhao Zhang.
Before transferring to UT, I had been a PhD student in Computer Science at the Chinese University of Hong Kong, where I worked on applying Persistent Memory to large scale recommendation models. Prior to that, I obtained my bachelor degree in Computer Science at the Beijing University of Posts and Telecommunications.
I have a broad interest in everything about software engineering and have an ambition to make this world better by building useful software applications. I am now actively looking for a software development internship opportunity in Summer 2023.
Experiences
working on high performance computing in deep learning under the supervision of Dr. Zhao Zhang.
Researched the Graph Convolutional Networks from a causal inference perspective under Dr. Fuli Feng’s supervision. Our co-authored paper “Should graph convolution trust neighbors? a simple causal inference method” gained a spot on SIGIR’21.
I worked in the 3D Reconstruction Group, Deep Learning Lab at MOMO Inc., under Dr. Tianxiang Zheng’s mentorship.
- Developed a real-time face tracking tool which introduced dynamic rigidity prior to 3D face reconstruction, increased the stability score by 25.7% under drastic poses and expressions.
- Translated an existing MATLAB project into C++ using OpenCV, and modularized the code for easier maintenance and higher efficiency.
- Replaced the projection method in our face-changing app, “ZAO” with perspective projection, increased the reconstruction accuracy by 11.4%.
I researched on graph adversarial learning, speficically, robust graph neural network models, under the supervision of Prof. Suhang Wang at Penn State University.
- Extensively studied several academic papers published in top conferences on graph adversarial learning.
- Implemented several graph adversarial attack algorithms in Python, achieved comparable results as reported in papers but lower overhead.
I researched on network embedding and graph neural networks under the supervision of Dr. Shu Wu at CASIA. I was also a member of Scientific Innovation Training Program.
- Actively participated in several research projects on recommender systems and graph mining.
- Proposed a novel Graph Convolutional Network variant GraphAIR with cooperators, which is the first to explicitly take into account the non-linear neighborhood interactions. The paper of GraphAIR is published on Pattern Recognition (Impact Factor: 7.740, ranking 20 out of 273 in Engineering, Electrical & Electronic).
- Implemented GraphAIR in Tensorflow, by the time it was published, it outperformed all baselines by significant margins on node classification and link prediction tasks, ranking first, second and fourth on 3 benchmark datasets respectively on paperswithcode.com.
Projects
Here lists some of my individual projects as well as course projects in school.
Publications
The following papers were finished while I was an intern at the National University of Singapore and Chinese Academy of Sicences.