Xi Li (李溪)
she/her/hers
Assistant Professor, Ph.D.
University of Alabama at Birmingham
About
I am an Assistant Professor in the Department of Computer Science at the University of Alabama at Birmingham (UAB). I received my Ph.D. in Computer Science from Penn State University under the guidance of Dr. George Kesidis and Dr. David Miller. I obtained my M.S. degree in Computer Science from Penn State University in 2018 and my B.S. degree from the School of Information Science and Engineering at Southeast University in Nanjing, China, in 2016.
Research Interests: My research broadly focuses on trustworthy AI. Recently, we have been exploring the following topics:
- Robustness of large language/vision/multi-modal models
- Machine learning for IoT security and cybersecurity
- Trustworthy AI + X (e.g., education and healthcare)
Check my resume here.
Publications
Most recent publications are on Google Scholar.
Exploitation and Mitigation: Understanding Large-Scale Machine Learning Robustness under Paradigm Shift
Xi Li, Ruixiang Tang, Muchao Ye
SDM Tutorial, 2025
Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models
Xi Li, Yusen Zhang, Renze Lou, Chen Wu, Jiaqi Wang
Under review
Position Paper: Assessing Robustness, Privacy, and Fairness in Federated Learning Integrated with Foundation Models
Xi Li, Jiaqi Wang
Under review
Backdoor Mitigation by Correcting Distribution of Neural Activation
Xi Li, Zhen Xiang, David Miller, George Kesidis
Neurocomputing, 2024
BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers: A Comprehensive Study
Xi Li, David Miller, Zhen Xiang, George Kesidis
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024
Unveiling Backdoor Risks Brought by Foundation Models in Heterogeneous Federated Learning
Xi Li, Chen Wu, Jiaqi Wang
PAKDD, 2024
Temporal-Distributed Backdoor Attack Against Video-Based Action Recognition
Xi Li, Songhe Wang, Ruiquan Huang, Mahanth Gowda, George Kesidis
AAAI, 2024
Backdoor Threats from Compromised Foundation Models to Federated Learning
Xi Li, Songhe Wang, Chen Wu, Hao Zhou, Jiaqi Wang
FL@FM-NeurIPS'23
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers
Xi Li, David Miller, Zhen Xiang, George Kesidis
MLSP, 2023
Test-Time Detection of Backdoor Triggers of Poisoned Deep Neural Networks
Xi Li, David Miller, Zhen Xiang, George Kesidis
ICASSP, 2022
Detecting Backdoor Attacks Against Point Cloud Classifiers
Zhen Xiang, David Miller, Siheng Chen, Xi Li, George Kesidis
ICASSP, 2022
A Backdoor Attack against 3D Point Cloud Classifiers
Zhen Xiang, David Miller, Siheng Chen, Xi Li, George Kesidis
ICCV, 2021
Exploitation and Mitigation: Understanding Large-Scale Machine Learning Robustness under Paradigm Shift
Xi Li, Ruixiang Tang, Muchao Ye
SDM Tutorial, 2025
AAAR-1.0: Assessing AI's Potential to Assist Research
Renze Lou, Hanzi Xu, Sijia Wang, Jiangshu Du, Ryo Kamoi, Xiaoxin Lu, Jian Xie, Yuxuan Sun, Yusen Zhang, Jihyun Janice Ahn, Hongchao Fang, Zhuoyang Zou, Wenchao Ma, Xi Li, Kai Zhang, Congying Xia, Lifu Huang, Wenpeng Yin
Under review
Securing Federated Learning Against Novel and Classic Backdoor Threats During Foundation Model Integration
Xiaohuan Bi, Xi Li
Under review
Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models
Xi Li, Yusen Zhang, Renze Lou, Chen Wu, Jiaqi Wang
Under review
Position Paper: Assessing Robustness, Privacy, and Fairness in Federated Learning Integrated with Foundation Models
Xi Li, Jiaqi Wang
Under review
CEPA: Consensus Embedded Perturbation for Agnostic Detection and Inversion of Backdoors
Guangmingmei Yang, Xi Li, Hang Wang, David Miller, George Kesidis
Under review
Vulnerabilities of Foundation Model Integrated Federated Learning Systems Under Adversarial Threats
Xi Li, Chen Wu, Jiaqi Wang
Under review
Backdoor Mitigation by Correcting Distribution of Neural Activation
Xi Li, Zhen Xiang, David Miller, George Kesidis
Neurocomputing, 2024
BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers: A Comprehensive Study
Xi Li, David Miller, Zhen Xiang, George Kesidis
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024
Unveiling Backdoor Risks Brought by Foundation Models in Heterogeneous Federated Learning
Xi Li, Chen Wu, Jiaqi Wang
PAKDD, 2024
Temporal-Distributed Backdoor Attack Against Video-Based Action Recognition
Xi Li, Songhe Wang, Ruiquan Huang, Mahanth Gowda, George Kesidis
AAAI, 2024
Backdoor Threats from Compromised Foundation Models to Federated Learning
Xi Li, Songhe Wang, Chen Wu, Hao Zhou, Jiaqi Wang
FL@FM-NeurIPS'23
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers
Xi Li, David Miller, Zhen Xiang, George Kesidis
MLSP, 2023
Test-Time Detection of Backdoor Triggers of Poisoned Deep Neural Networks
Xi Li, David Miller, Zhen Xiang, George Kesidis
ICASSP, 2022
Detecting Backdoor Attacks Against Point Cloud Classifiers
Zhen Xiang, David Miller, Siheng Chen, Xi Li, George Kesidis
ICASSP, 2022
A Backdoor Attack against 3D Point Cloud Classifiers
Zhen Xiang, David Miller, Siheng Chen, Xi Li, George Kesidis
ICCV, 2021
Students
PhD Students
I’m looking for highly motivated PhD students to join my research group starting in Fall 2025 at CS@UAB. Please check the Recruitment page for more details. If you are interested, apply to the CS PhD program and mention my name in your application. Additionally, send your CV and transcript to xili.recruitment@gmail.com with the subject [25Fall PhD Application].
Mentored Students
- Xiaohuan Bi – Undergraduate at Renmin University of China.
- Falong Fan – M.S. from The Chinese University of Hong Kong, Shenzhen.
- Ruofan Mao – Master's student at UAB.
Teaching
Instructor UAB
- CS652/752 Advanced Algorithms and Applications2025 Spring
- CS685/785 Foundation of Data Science2024 Fall
Teaching Assistant PSU
- CMPSC465 Data Structures and Algorithms 2021 Spring
- CMPSC/MATH 451 Numerical Computations 2019 Fall - 2024 Spring
- CMPEN/EE 455 Digital Image Processing I 2018 Fall
Experience
Meta, NYC | Summer 2024
- Modern Recommendation System Team
- Machine Learning Engineer Intern
- Explored early fusion architecture for multimodal understanding.
Service
Conference Program Committee:
- Association for the Advancement of Artificial Intelligence (AAAI), 2025
- SIAM International Conference on Data Mining (SDM), 2024
Conference Reviewer:
- Artificial Intelligence and Statistics (AISTATS), 2025
- Association for the Advancement of Artificial Intelligence (AAAI), 2025
- International Conference on Learning Representations (ICLR), 2025
- IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2022-2025
- SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023, 2024
- IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2024, 2025
- Conference on Neural Information Processing Systems (NeurIPS), 2024
- Conference on Language Modeling (COLM), 2024
Journal Reviewer:
- Computers and Security (C&S), 2024
- IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2024
Student Volunteer:
- Conference on Neural Information Processing Systems (NeurIPS), 2023