Linyi Li 李林翼 First Year PhD Student, CS@UIUC


I am Linyi Li, a first-year PhD Student at CS@UIUC. My advisor is Prof. Tao Xie.

My research interests are in the intersection of software engineering, machine learning, and programming languages.

In 2017, I spent a wonderful summer @ CMU, fortunately advised by Prof. Matt Fredrikson on neural network explaining.

I got bachelor degree from Department of Computer Science and Technology, Tsinghua University, where I did research on Web API Automated Testing, advised by Prof. Xiaoying Bai.

[Curriculum Vitae]



  1. Klas Leino, Shayak Sen, Anupam Datta, Matt Fredrikson, Linyi Li
    Influence-Directed Explanations for Deep Convolutional Networks
    Arxiv Preprint 1802.03788; to appear in International Test Conference 2018
    [ArXiv]   [BibTex]
  2. Junyi Wang, Xiaoying Bai, Linyi Li, Haoran Ma, Zhicheng Ji
    A Model-Based Framework For Cloud API Testing
    Computer Software and Applications Conference (COMSPAC), 2017 IEEE 41st Annual
    [PDF]   [BibTex]
  3. Junyi Wang, Xiaoying Bai, Haoran Ma, Linyi Li, Zhicheng Ji
    Cloud API Testing
    Verification and Validation Workshops (ICSTW), 2017 IEEE International Conference on Software Testing
    [PDF]   [BibTex]



Lapis: Scenario-Based Automatic Web API Testing

Lapis is an automatic scenario-based Web API tester. The tool reads OpenAPI specification script and scenario definition, then generates and executes test cases automatically. Several evaluation experiments reveal its high efficiency and strength in Web API testing. PyLapis, the latest tool written in Python, using specification language extended from OpenAPI 3.0, is about to release.

Neural Network Explanation

Application of Integrated Gradients on Diabetic Retinopathy Detection Network

The project applies integrated gradients, an influence analysis method, to a diabetic retinopathy detection convolutional neural network. The tool and framework supports multiple explaining configurations such as direct attributing and middle-layer filtered attributing. The attribution results can be used directly for lesion detection. The visualization result of each neuron's influence enabled further analysis of the neural network.



A distributed web API testing system. Distributed cluster nodes send test request individually under center control. The distribution property makes it possible to generate heavy test load.

Ray Tracing

Ray Tracing Render Engine

  • A totally independent cross-platform graphics render engine.
  • Supported algorithms: Phong reflection model, ray tracing and photon mapping.
  • Supported light source: point light, and area light.
  • Supported model: DSL-specified model, and ".obj" format model.
  • Supported material: solid, and transparency with refraction.


Last Updated: Dec 7, 2018