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. Wei-Shinn (Jeff) Ku in 2023. He earned his B.S. Degree in Computer Science from Xi'an Jiaotong University.
His research interests include:
Data mining, machine learning
Useful links: Google scholar   DBLP
Contact
bo-hui at utulsa dot edu
2095, Rayzor Hall at the University of Tulsa
Award
NSF award #2348177
AAAI student scholarship award
News
Three research papers are accepted by ICLR 2026
Jan, 2026
One research papers are accepted by CIKM 2025
Aug, 2025
Two research papers are accepted by ICCV 2025
June, 2025
One research paper is accepted by ECAI 2025
June, 2025
Two research papers are accepted by GECCO 2025 and IJCNN 2025
Mar, 2025
One research paper is accepted by COLING 2025
Dec, 2024
One research paper is accepted by KDD 2025
Nov, 2024
One research paper is accepted by CIKM 2024
July, 2024
Our machine unlearning project is funded by NSF. Thanks NSF!
April, 2024
One research paper is accepted by Diffusion Models@Neurips 2023
Oct, 2023
One research paper is accepted by ICLR 2023
Jan, 2023
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
Selected Publication
  • [26’ICLR] Ruimeng Ye, Zihan Wang, Zinan Ling, Yang Xiao, Manling Li, Xiaolong Ma, Bo Hui, “Your Language Model Secretly Contains Personality Subnetworks”, in the Fourteenth International Conference on Learning Representations (ICLR 2026).
  • [26’ICLR] Ruimeng Ye, Zihan Wang, Yang Xiao, Zinan Ling, Manling Li, Bo Hui, “Weak-to-Strong Generalization with Failure Trajectories”, in the Fourteenth International Conference on Learning Representations (ICLR 2026).
  • [26’ICLR] Kaiyuan Deng, Gen Li, Yang Xiao, Bo Hui, Xiaolong Ma, “Forget Many, Forget Right: Scalable and Precise Concept Unlearning in Diffusion Models”, in the Fourteenth International Conference on Learning Representations (ICLR 2026).
  • [25’ICCV] Yang Xiao, Wang Lu, Jie Ji, Ruimeng Ye, Gen Li, Xiaolong Ma, Bo Hui, “Optimal Transport for Brain-Image Alignment: Unveiling Redundancy and Synergy in Neural Information Processing”, in Proceedings of the International Conference on Computer Vision (ICCV 2025).
  • [25’ICCV] Gen Li, Yang Xiao, Jie Ji, Kaiyuan Deng, Bo Hui, Linke Guo, Xiaolong Ma, “Sculpting Memory: Multi-Concept Forgetting in Diffusion Models via Dynamic Mask and Concept-Aware Optimization”, in Proceedings of the International Conference on Computer Vision (ICCV 2025).
  • [25’CIKM] Yang Xiao, Ruimeng Ye, Bohan Liu, Xiaolong Ma, Bo Hui, “Efficient Knowledge Graph Unlearning with Zeroth-order Information" in the 34th ACM International Conference on Information and Knowledge Management (CIKM, 2025)
  • [25’ECAI] Bohan Liu, Yang Xiao, Ruimeng Ye, Zinan Ling, Xiaolong Ma, Bo Hui, Towards Distributed Backdoor Attacks with Network Detection in Decentralized Federated Learning” in the 28th European Conference on Artificial Intelligence (ECAI-2025).
  • [25’COLING] Yang Xiao, Ruimeng Ye, Bo Hui, “Knowledge Graph Unlearning with Schema" in the 31st International Conference on Computational Linguistics (COLING, 2025)
  • [24’CIKM] Yang Xiao, Zijie Zhang, Yuchen Fang, Da Yan, Yang Zhou, Wei-Shinn Ku, Bo Hui, “Advancing Certified Robustness of Explanation via Gradient Quantization,” in 33rd ACM International Conference on Information and Knowledge Management (CIKM, 2024)
  • [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).
    • © 2024 Bo Hui.