I am a fourth year Ph.D. candidate at the Department of Computer Science, University of Houston, advised by Guoning Chen.
Ladenburg, Germany | 06/2023 - 09/2023
Research Intern
Beijing, China | 07/2016 - 07/2018
Research Intern
Ladenburg, Germany | 06/2023 - 09/2023
Scholarship Participants
Beijing, China | 07/2015 - 07/2015
Team Leader
Beijing, China | 07/2014 - 07/2015
Team Leader, Director
Lei Si, Haowei Cao, Guoning Chen. Hybrid Base Complex: Extract and Visualize Structure of Hex-dominant Meshes, In IEEE Transactions on Visualization and Computer Graphics, Accepted, 2024
Giulia Toti, Lei Si, David Daniels et al. Students and Instructors Reflections on the Impact of COVID-19 on Computer Science Education after One Year of Remote Teaching, 28 December 2023, PREPRINT (Version 1) available at Research Square
Lei Si and Guoning Chen, A Visualization System for Hexahedral Mesh Quality Study. IEEE Visualization 2023 Short Papers, 5 pages, Melbourne, Australia, October 2023
Muhammad Naeem Akram, Si, Lei, and Guoning Chen. An embedded polygon strategy for quality improvement of 2d quadrilateral meshes with boundaries. In VISIGRAPP (1: GRAPP), pages 177–184, 2021
Yanhui Guo, Thomas A Rothfus, Amira S Ashour, Si, Lei, Chunlai Du, and Tih-Fen Ting. Varied channels region proposal and classification network for wildlife image classification under complex environment. IET Image Process., 14(4):585–591, 2020
Chunlai Du, Shenghui Liu, Si, Lei, Yanhui Guo, and Tong Jin. Using object detection network for malware detection and identification in network traffic packets. CMC-COMPUTERS MATERIALS & CONTINUA,64(3):1785–1796, 2020
Chunlai Du, Shenghui Liu, Yanhui Guo, Si, Lei, and Tong Jin. Detection and information extraction of similar basic blocks used for directed greybox fuzzing. In International Conference on Artificial Intelligence and Security, pages 353–364. Springer, 2020
Yanhui Guo, Amira S Ashour, Si, Lei, and Deep P Mandalaywala. Multiple convolutional neural network for skin dermoscopic image classification. In 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pages 365–369. IEEE, 2018
Quadrilateral (or quad) meshes generated by various remeshing and simplification methods for input models with complex structure and boundary configurations often possess elements with minimal quality, which calls for an optimization approach to improve their individual elements’ quality while preserving the boundary features. Many existing methods either fix boundary vertices during optimization or assume a simple boundary configuration. In this paper, we introduce a new quality improvement framework for 2D quad meshes with open boundaries. Our framework aims to optimize the configuration of an embedded polygon constructed based on the one-ring neighbors of each interior vertex. A feature-preserved boundary optimization is also introduced based on the angle configuration of the individual boundary vertices to further improve the quality of the boundary elements. Our framework has been applied to a number of 2D quad meshes with various boundary configurations and compared with other representative methods to demonstrate its advantages.
we present a new quality visual analysis system for 3D hexahedral (hex) and 2D quadrilateral (quad) meshes. Our system allows users to focus their attention on areas with poor quality elements without being distracted by the large elements in the mesh model through an aggregated quality glyph. Our system also explicitly highlights places with overlapping elements that are typically hard to see with existing visualization tools. In addition, we display boundary error information in three different forms to support its detailed inspection, which is often ignored by existing visualization tools. Furthermore, multiple views are provided to support a multi-level analysis of the local mesh configuration at places with low quality elements. To demonstrate the effectiveness of our system, we have applied it to analyze the quality of different mesh models and compare the performance of a couple of mesh generation and mesh optimization algorithms for quadrilateral and hexahedral meshes, respectively.
Three.js is a JavaScript 3D library, and builds only include a WebGL renderer but WebGPU (experimental).
Reviwer of Frontiers of Information Technology & Electronic Engineering
Reviwer of COMPUTER GRAPHICS INTERNATIONAL, Geneva 2024