Hello! My name is Shijie (Brandon) Bian, a Master's of Computational Data Science student at Carnegie Mellon University's School of Computer Science.
Education:
During my undergraduate study at UCLA, I have gained an overall GPA of 3.86/4.0 and a major GPA of 3.89/4.0. Furthermore, I am proficient in designing complicated programs using Python, R, C, and C++.
I am also skilled at developing machine learning models using PyTorch, TensorFlow, Keras, Scikit-learn, and NetworkX.
Research Interests:
I am interested in research areas related to Machine Learning, Computer Vision, Knowledge Engineering, Data Mining, and their applications.
I wish to further my study and research in academic realms related to Artificial Intelligence and apply my knowledge of Mathematics, Computer Science, and Statistics as well.
Research Experience:
I have conducted numerous research internships and projects related to Machine Learning during my undergraduate studies.
Specifically, I participated as the core researcher in the "Smart Connected Worker" project directed by
UC Irvine, CSU Northridge, and funded by the U.S. Department of Energy; the "AI-assisted Knowledge Graph Design" project directed by NASA Jet Propulsion Laboratory (JPL) with Autodesk Inc;
"An Optimizing Compiler for Deep Learning" project at The Center for Vision, Cognition, Learning, and Autonomy of UCLA.
Curriculum Vitae
A knowledge graph-based framework to assist inexperienced engineers in developing new products by learning best practices from existing Computer-aided Designs (CADs) using Graph Neural Networks (GNNs).
An LSTM-based machine learning module that automatically disaggregates devices' power signatures in real-time and predicts machine states. This module could be combined with HCI for energy and signal analysis of smart manufacturing.
A project sponsored by the California Department of Transportation (Caltrans) to design a computer vision-based monitoring system for real-time traffic control and pedestrian safety analysis. The system utilizes real-time object detection on 3D Velodyne LiDAR point clouds. We are also working on creating and optimizing 3D Velodyne LiDAR dataset (like KITTI) for the proposed system.
An already deployed real-time monitoring system of manufacturing workflow that integrates state-of-the-art machine learning techniques with the workplace scenarios of advanced manufacturing systems.
An application of Computer Vision and Artificial Intelligence (AI) models to replace discrete labor-intensive methods to monitor the machine state and predict the errors and risks for advanced manufacturing.
A CRAFT-based finger detection and text recognition model for the real-time human-machine interaction control of a 3D printer.
A Heterogeneous Graph Transformer (HGT) that combined GNN with transformers to create efficient SAT solvers. I investigated, analyzed and debugged numerous baseline models, and designed a suitable experiment that verified the performance of the proposed Heterogeneous Graph Transformer model.
Li, Chen, Shijie Bian, Tongzi Wu, Richard P. Donovan, and Bingbing Li. 2022. "Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers" Sensors 22, no. 16: 6246. https://doi.org/10.3390/s22166246.
Shijie Bian, Daniele Grandi, Kaveh Hassani, Elliot Sadler, Bodia Borijin, Axel Fernandes, Andrew Wang, Thomas Lu, Richard Otis, Nhut Ho, Bingbing Li*.
Richard P. Donovan, Yoon G. Kim, Anthony Manzo, Yutian Ren, Shijie Bian, Tongzi Wu, Shweta Purawat, Henry Helvajian, Marilee Wheaton, Bingbing Li, Guann-Pyng Li . J. Adv. Manuf. Process. 2022, e10130. URL: https://doi.org/10.1002/amp2.10130.
Yoon G. Kim, Richard P. Donovan, Yutian Ren, Shijie Bian, Tongzi Wu, Shweta Purawat, Anthony J. Manzo, Ilkay Altintas, Bingbing Li, Guann-Pyng Li . J. Adv. Manuf. Process. 2022, e10129 URL: https://doi.org/10.1002/amp2.10129.
Shijie Bian, Chen Li, Yongwei Fu, Yutian Ren, Tongzi Wu, Guann-Pyng Li, Bingbing Li*. “Machine learning-based real-time monitoring system for smart connected worker to improve energy efficiency”. Journal of Manufacturing Systems (JCR Quartile Ranking: Q1, 2020 Impact Factor: 8.633), 2021, Volume 61, Pages 66-76. URL: https://doi.org/10.1016/j.jmsy.2021.08.009.
Shijie Bian, Tiancheng Lin, Chen Li, Yongwei Fu, Mengrui Jiang, Tongzi Wu, Xiyi Hang, Bingbing Li*, “Real-time Object Detection for Smart Connected Worker in 3D printing”, Proceedings of the 2021 International Conference on Computational Science (ICCS-2021, Rank A Conference), Krakow, Poland, June 16-18, 2021. URL: https://doi.org/10.1007/978-3-030-77970-2_42. Publication with an oral presentation of the full paper at the conference.