Bo Hui
Bo Hui is an assistant professor at the University of Tulsa. He received the Ph.D. degree at Auburn University, under the supervision of Dr. Ku. He earned his B.S. Degree in Computer Science from Xi'an Jiaotong University in 2013. He has worked as an embedded software engineer and a senior software engineer from 2013 to 2018.
His research interests include:
Data mining Machine learning Databases
Useful links: Google scholar   Lab page
I am actively recruiting highly-motivated PhDs / masters / interns to work in the area of data mining and machine learning. Interested candidates are strongly encouraged to send me your CV via email.
Contact
bo-hui at utulsa dot edu
2095, Rayzor Hall at the University of Tulsa
News
Our machine unlearning project is funded by NSF. Thanks NSF!
April, 2024
One research paper is accepted by Neurips 2023 workshop on diffusion model
Oct, 2023
One research paper is accepted by ACM Sigspatial 2023
Oct, 2023
Bo Hui will serve as PC for AAAI 2024
Aug, 2023
One research paper is accepted by GECCO 2023
May, 2023
One research paper is accepted by ICLR 2023
Jan, 2023
Bo Hui is awarded AAAI-23 student scholarship
Dec, 2022
One research paper is accepted by AAAI 2023
Nov, 2022
One research paper is accepted by EMNLP 2022
Oct, 2022
One research paper is accepted by KDD 2022
May, 2022
One research paper is accepted by ICDE 2022
Nov, 2021
One research paper is accepted by ICDM 2021
Aug, 2021
One research paper is accepted by KDD 2021
May, 2021
One research paper is accepted by ICDE 2021
Oct, 2020
One research paper is accepted by CIKM 2020
July, 2020
Industry Experience
2013-2016
Embedded software engineer at Xi'an Microelectronics Research Institution
Develop embedded software utilizing C, MCS-51, CAN Bus, 1553 Bus
2016-2018
Senior software engineer at GrapeCity
Develop website and mobile apps of product leyserkids
Selected Publication
[23’ICLR] Bo Hui, Da Yan, Xiaolong Ma, and Wei-Shinn Ku, “Rethinking Graph Lottery Tickets: Graph Sparsity Matters,” in the 11th International Conference on Learning Representations (ICLR, 2023).
[23’AAAI] Bo Hui, Yuchen Fang, Tian Xia, Sarp Aykent, and Wei-Shinn Ku, “Constrained Market Share Maximization by Signal-guided Optimization,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI, 2023).
[22’KDD] Bo Hui and Wei-Shinn Ku, “Low-rank Nonnegative Tensor Decomposition in Hyperbolic Space,” in Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD, 2022).
[22’ICDE] Bo Hui, Da Yan, Haiquan Chen, and Wei-Shinn Ku, “Time-sensitive POI Recommendation by Tensor Completion with Side Information,” in Proceedings of the 38th IEEE International Conference on Data Engineering (ICDE, 2022).
[22’EMNLP] Bo Hui, Tian Xia, and Wei-Shinn Ku, “A Localized Geometric Method to Match Knowledge in Low-dimensional Hyperbolic Space,” in Conference on Empirical Methods in Natural Language Processing (EMNLP, 2022).
[21’KDD] Bo Hui, Da Yan, Haiquan Chen, and Wei-Shinn Ku, “TrajNet: A Trajectory-Based Deep Learning Model for Traffic Prediction,” in Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD, 2021).
[21’ICDE] Bo Hui, Haiquan Chen, Da Yan, and Wei-Shinn Ku, “EDGE: Entity-Diffusion Gaussian Ensemble for Interpretable Tweet Geolocation Prediction,” in Proceedings of the 37th IEEE International Conference on Data Engineering (ICDE, 2021).
[21’ICDM] Bo Hui, Da Yan, Haiquan Chen, and Wei-Shinn Ku, “Trajectory WaveNet: A Trajectory-Based Model for Traffic Forecasting,” in Proceedings of the 21st IEEE International Conference on Data Mining (ICDM, 2021).
[20’CIKM] Bo Hui, Da Yan, Wei-Shinn Ku, and Wenlu Wang, “Predicting Economic Growth by Region Embedding: A Multigraph Convolutional Network Approach,” in Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM, 2020).
© 2021 Bo Hui.