Xiongxiao Xu

Ph.D. Student Department of Computer Science Illinois Institute of Technology Chicago, Illinois, United States E-mail: xxu85@hawk.iit.edu

About me

I am a Ph.D. student in the Department of Computer Science at Illinois Institute of Technology since Spring 2022, advised by Dr. Kai Shu. I received the B.S. degree in Computer Science from the Chongqing University in 2020, advised by Dr. Hong Xie. I worked as a research intern at Argonne Leadership Computing Facility, Argonne National Laboratory, Summer 2024. My research interests generally lie in machine learning and data mining, and their applications in high-performance computing. Previously, I work on multi-armed bandits and its application in recommender system.

Research Interests

• Machine Learning, Data Mining

• High-Performance Computing

Publications

1. Xiongxiao Xu, Canyu Chen, Yueqing Liang, Baixiang Huang, Guangji Bai, Liang Zhao, Kai Shu. "SST: Multi-Scale Hybrid Mamba-Transformer Experts for Long-Short Range Time Series Forecasting", arxiv, 2024. [arxiv][Code]

2. Xiongxiao Xu, Solomon Abera Bekele, Brice Videau, Kai Shu. "Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach", arxiv, 2024. [arxiv][Code]

3. Haoran Wang, Aman Rangapur, Xiongxiao Xu, Yueqing Liang, Haroon Gharwi, Carl Yang, Kai Shu. "Piecing It All Together: Verifying Multi-Hop Multimodal Claims", arxiv, 2024. [arxiv]

4. Baixiang Huang, Canyu Chen, Xiongxiao Xu, Ali Payani, Kai Shu. "Can Knowledge Editing Really Correct Hallucinations?", arxiv, 2024. [arxiv]

5. Canyu Chen, Baixiang Huang, Zekun Li, Zhaorun Chen, Shiyang Lai, Xiongxiao Xu, Jia-Chen Gu, Jindong Gu, Huaxiu Yao, Chaowei Xiao, Xifeng Yan, William Yang Wang, Philip Torr, Dawn Song, Kai Shu. "Can Editing LLMs Inject Harm?", arxiv, 2024. [arxiv]

6. Yueqing Liang, Liangwei Yang, Chen Wang, Xiongxiao Xu, Philip S. Yu, Kai Shu. "Taxonomy-Guided Zero-Shot Recommendations with LLMs", arxiv, 2024. [arxiv]

7. Xiongxiao Xu, Kaize Ding, Canyu Chen, Kai Shu. "MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection", Proceedings of The 11th IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2024. [Paper][Code]

8. Xiongxiao Xu, Kevin A. Brown, Tanwi Mallick, Xin Wang, Elkin Cruz-Camacho, Robert B. Ross, Christopher D. Carothers, Zhiling Lan, Kai Shu. "Surrogate Modeling for HPC Application Iteration Times Forecasting with Network Features", Proceedings of The ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS), 2024. [pdf]

9. Elkin Cruz-Camacho, Kevin A. Brown, Xin Wang, Xiongxiao Xu, Kai Shu, Zhiling Lan, Robert B. Ross and Christopher D. Carothers. "Hybrid PDES Simulation of HPC Networks using Zombie Packets.", ACM Transactions on Modeling and Computer Simulation (TOMACS), 2024. [pdf]

10. Xiongxiao Xu. "Exploring Machine Learning Models with Spatial-Temporal Information for Interconnect Network Traffic Forecasting", Proceedings of The ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS), PhD Colloquium, 2023. [pdf]

11. Xiongxiao Xu, Xin Wang, Elkin Cruz-Camacho, Christopher D. Carothers, Kevin A. Brown, Robert B. Ross, Zhiling Lan, Kai Shu. "Machine Learning for Interconnect Network Traffic Forecasting: Investigation and Exploitation.", Proceedings of The ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS), 2023. [pdf]

12. Elkin Cruz-Camacho, Kevin A. Brown, Xin Wang, Xiongxiao Xu, Kai Shu, Zhiling Lan, Robert B. Ross, Christopher D. Carothers. "Hybrid PDES Simulation of HPC Networks using Zombie Packets.", Proceedings of The ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS), 2023. [pdf] (Best Short Paper Award)

13. Canyu Chen, Yueqing Liang, Xiongxiao Xu, Shangyu Xie, Yuan Hong, Kai Shu. "On Fair Classification with Mostly Private Sensitive Attributes", arxiv, 2022. [arxiv]

14. Xiongxiao Xu, Hong Xie, John C.S. Lui. "Generalized Contextual Bandits With Latent Features: Algorithms and Applications", IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021. [pdf][Supplementary]

Awards

• Starr-Fieldhouse Research Fellowship Award, 2024

• Best Short Paper Award, ACM SIGSIGM-PADS, 2023

• ACM SIGSIGM-PADS Student Travel Award, 2023, 2024

Internship

• Argonne Leadership Computing Facility, Argonne National Laboratory, Summer, 2024

Talk

• Machine Learning Surrogate Models for the Parallel Discrete Event Simulation, Argonne National Laboratory, Aug. 30, 2023

Academic Services

Program Committee

• The International Conference on Learning Representations (ICLR'25)

• The AAAI Conference on Artificial Intelligence (AAAI’24, AAAI'25)

• The International Conference on Database Systems for Advanced Applications (DASFAA’24)

Reviewer

• IEEE Transactions on Neural Networks and Learning Systems (TNNLS)