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

Graduate Research Assistant

2022.1 - Present
Texas Advanced Computing Center (TACC)

working on high performance computing in deep learning under the supervision of Dr. Zhao Zhang.

Research Intern

2020.6 - 2020.10
National University of Singapore

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.

R&D Intern

2019.9 - 2020.1
MOMO Inc.

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%.

Summer Research Intern

2019.6 - 2019.8
Penn State University

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.

Research Assistant

2018.5 - 2019.5
Institute of Automation, Chinese Academy of Sciences

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.

ANSI-Art - Generate ANSI-/Ascii-art version images/Gifs in your terminal.
Ascii-live - Capture your live webcam stream in ASCII-art form.
FaceMask - An AR face tracking tool which generates real-time masks over human faces in the video.
SimpleDNS - A simple DNS server written in Python, compatible with Windows, Linux and MacOS.

Publications

The following papers were finished while I was an intern at the National University of Singapore and Chinese Academy of Sicences.

  • Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
  • Fuli Feng, Weiran Huang, Xiangnan He, Xin Xin, Qifan Wang, Tat-Seng Chua
    SIGIR'21
  • GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
  • Fenyu Hu, Yanqiao Zhu, Shu Wu, Weiran Huang, Liang Wang, Tieniu Tan
    Pattern Recognition

    Skills & Proficiency

    C/C++

    Java

    Golang

    Python

    Bash

    Doker

    Kubernetes

    PyTorch

    Tensorflow

    Spark

    CUDA

    Rust

    Git

    MATLAB

    LaTEX

    Javascript

    SML