Leadership

Xin Liu

Professor of Computer Science, UC Davis

Co-PI and Co-Lead of Use-Inspired and Foundational AI Cluster

About Xin

Xin Liu is a Computer Science Professor at the University of California, Davis. Before joining UC Davis, she was a postdoctoral research associate in the Coordinated Science Laboratory at UIUC. During 2012-2014, she took a leave of absence and was with Microsoft Research Asia.

Her current research interests fall in the general areas of machine learning algorithm development and machine learning applications in human and animal healthcare, food systems, and communication networks. Her research on networking includes cellular networks, cognitive radio networks, wireless sensor networks, network information theory, network security, and IoT systems.

Xin is an IEEE Fellow. She received the Computer Networks Journal Best Paper of Year award in 2003 for her work on opportunistic scheduling. In 2005, she received the NSF CAREER award for her research on Smart-Radio-Technology-Enabled Opportunistic Spectrum Utilization. She is a co-PI and AI cluster co-lead for AIFS.

20 projects

Project involvement

Year 4

37 publications

AIFS publications

Conference Article · International Conference on Multimedia Information Processing and Retrieval · 2025
FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization
Son, Ha M, Shahbaz Rezaei, and Xin Liu
Paper page for FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization Project for FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization DOI 10.48550/arXiv.2506.20841
Conference Article · International Conference on Data Mining Workshops · 2025
On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models
Rezaei, Shahbaz, Avishai Halev, and Xin Liu
Paper page for On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Project for On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models DOI 10.48550/arXiv.2506.03267
Conference Article · International Conference on Data Mining Workshops · 2025
Implet: A Post-hoc Subsequence Explainer for Time Series Models
Meng, Fanyu, Ziwen Kan, Shahbaz Rezaei, Zhaodan Kong, Xin Chen, and Xin Liu
Project for Implet: A Post-hoc Subsequence Explainer for Time Series Models DOI 10.48550/arXiv.2505.08748
Journal Article · Applied Intelligence · 2025
Adventurer: Exploration with BiGAN for Deep Reinforcement Learning
Liu, Yongshuai and Xin Liu
Paper page for Adventurer: exploration with BiGAN for deep reinforcement learning Project for Adventurer: exploration with BiGAN for deep reinforcement learning DOI 10.1007/s10489-025-06600-4
Journal Article · Machine Learning · 2025
An Empirical Study on Impact of Label Noise on Synthetic Tabular Data Generation
Kim, Jeonghoon, Chao Huang, and Xin Liu
Paper page for An empirical study on impact of label noise on synthetic tabular data generation PDF of An empirical study on impact of label noise on synthetic tabular data generation DOI 10.1007/s10994-024-06629-5
Conference Article · Advances in Neural Information Processing Systems 37 · 2024
Convergence Analysis of Split Federated Learning on Heterogeneous Data
Han, Pengchao, Chao Huang, Geng Tian, Ming Tang, and Xin Liu
Paper page for Convergence Analysis of Split Federated Learning on Heterogeneous Data PDF of Convergence Analysis of Split Federated Learning on Heterogeneous Data DOI 10.52202/079017-3287
Journal Article · Journal of Food Protection · 2024
Detection of Escherichia Coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy
Wisuthiphaet, Nicharee, Huanle Zhang, Xin Liu, and Nitin Nitin
Paper page for Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy PDF of Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy DOI 10.1016/j.jfp.2024.100396
Conference Article · Lecture Notes in Computer Science · 2024
An Accuracy-Shaping Mechanism for Competitive Distributed Learning
Huang, Chao, Justin Dachille, and Xin Liu
Paper page for An Accuracy-Shaping Mechanism for Competitive Distributed Learning DOI 10.1007/978-3-031-72347-6_10
Conference Article · International Conference on Data Mining Workshops · 2024
Explanation Space: A New Perspective Into Time Series Interpretability
Rezaei, Shahbaz and Xin Liu
Paper page for Explanation space: A new perspective into time series interpretability Project for Explanation space: A new perspective into time series interpretability DOI 10.48550/arXiv.2409.01354
Journal Article · IEEE/ACM Transactions on Networking · 2024
Linear Bandits With Side Observations on Networks
Kar, Avik, Rahul Singh, Fang Liu, Xin Liu, and Ness B Shroff
Paper page for Linear Bandits With Side Observations on Networks DOI 10.1109/tnet.2024.3422323
Conference Article · Computer Vision and Pattern Recognition · 2024
Feduv: Uniformity and Variance for Heterogeneous Federated Learning
Son, Ha M, Moon-Hyun Kim, Tai-Myoung Chung, Chao Huang, and Xin Liu
Paper page for Feduv: uniformity and variance for heterogeneous federated learning Project for Feduv: uniformity and variance for heterogeneous federated learning DOI 10.1109/CVPR52733.2024.00560
Journal Article · IEEE Internet of Things Journal · 2024
When Federated Learning Meets Oligopoly Competition: Stability and Model Differentiation
Huang, Chao, Justin Dachille, and Xin Liu
Paper page for When Federated Learning Meets Oligopoly Competition: Stability and Model Differentiation DOI 10.1109/JIOT.2024.3400216
Conference Article · IEEE/RSJ International Conference on Intelligent Robots and Systems · 2024
DAVIS-Ag: A Synthetic Plant Dataset for Developing Domain-Inspired Active Vision in Agricultural Robots
Choi, Taeyeong, Dario Guevara, Zifei Cheng, Grisha Bandodkar, Chonghan Wang, Brian N Bailey, Mason Earles, and Xin Liu
PDF of DAVIS-Ag: A Synthetic Plant Dataset for Developing Domain-Inspired Active Vision in Agricultural Robots Project for DAVIS-Ag: A Synthetic Plant Dataset for Developing Domain-Inspired Active Vision in Agricultural Robots
Journal Article · AI Magazine · 2024
The AIFS Institute: Building a Better Food System Through AI
Tagkopoulos, Ilias, Mason Earles, Danielle G Lemay, Xin Liu, Nitin Nitin, Aaron Smith, Tarek I Zohdi, and Steve Brown
Paper page for The AIFS Institute: Building a better food system through AI PDF of The AIFS Institute: Building a better food system through AI DOI 10.1002/aaai.12164
Journal Article · IEEE communications magazine · 2023
Promoting Collaboration in Cross-Silo Federated Learning: Challenges and Opportunities
Huang, Chao, Ming Tang, Qian Ma, Jianwei Huang, and Xin Liu
Paper page for Promoting Collaboration in Cross-Silo Federated Learning: Challenges and Opportunities DOI 10.1109/MCOM.005.2300467
Journal Article · IEEE Internet of Things Journal · 2023
Client Selection in Federated Learning: Principles, Challenges, and Opportunities
Fu, Lei, Huanle Zhang, Ge Gao, Mi Zhang, and Xin Liu
Paper page for Client Selection in Federated Learning: Principles, Challenges, and Opportunities PDF of Client Selection in Federated Learning: Principles, Challenges, and Opportunities DOI 10.1109/jiot.2023.3299573
Conference Article · IEEE Transactions on Big Data · 2023
CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing
Liu, Yongshuai, Jiaxin Ding, Zhi-Li Zhang, and Xin Liu
Project for CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing DOI 10.1109/BigData52589.2021.9671840
Journal Article · Scientific Reports · 2023
Infection Prediction in Swine Populations with Machine Learning
Halev, Avishai, Beatriz Martínez-López, Maria Clavijo, Carlos Gonzalez-Crespo, Jeonghoon Kim, Chao Huang, Seth Krantz, Rebecca Robbins, and Xin Liu
Paper page for Infection prediction in swine populations with machine learning PDF of Infection prediction in swine populations with machine learning DOI 10.1038/s41598-023-43472-5
Conference Article · 2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) · 2023
On the Impact of Label Noise in Federated Learning
Ke, Shuqi, Chao Huang, and Xin Liu
Paper page for On the Impact of Label Noise in Federated Learning DOI 10.23919/wiopt58741.2023.10349830
Workshop Article · 2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) · 2023
On the Impact of Label Noise in Federated Learning
Ke, Shuqi, Chao Huang, and Xin Liu
Paper page for On the Impact of Label Noise in Federated Learning Project for On the Impact of Label Noise in Federated Learning DOI 10.23919/wiopt58741.2023.10349830
Journal Article · IEEE Internet of Things Journal · 2023
Federated Learning Hyperparameter Tuning From a System Perspective
Zhang, Huanle, Lei Fu, Mi Zhang, Pengfei Hu, Xiuzhen Cheng, Prasant Mohapatra, and Xin Liu
Paper page for Federated Learning Hyperparameter Tuning From a System Perspective PDF of Federated Learning Hyperparameter Tuning From a System Perspective DOI 10.1109/jiot.2023.3253813
Journal Article · IEEE Transactions on Network Science and Engineering · 2023
Duopoly Business Competition in Cross-Silo Federated Learning
Huang, Chao, Shuqi Ke, and Xin Liu
DOI 10.1109/TNSE.2023.3297880
Journal Article · Computation · 2023
Quantifying Causal Path-Specific Importance in Structural Causal Model
Wang, Xiaoxiao, Minda Zhao, Fanyu Meng, Xin Liu, Zhaodan Kong, and Xin Chen
Paper page for Quantifying Causal Path-Specific Importance in Structural Causal Model DOI 10.3390/computation11070133
Journal Article · Applied Intelligence · 2023
Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importance
Wang, Xiaoxiao, Fanyu Meng, Xin Liu, Zhaodan Kong, and Xin Chen
Paper page for Causal explanation for reinforcement learning: quantifying state and temporal importance DOI 10.1007/s10489-023-04649-7
Journal Article · Frontiers in Microbiology · 2023
Predicting Antimicrobial Resistance of Bacterial Pathogens Using Time Series Analysis
Kim, Jeonghoon, Ruwini Rupasinghe, Avishai Halev, Chao Huang, Shahbaz Rezaei, Maria Clavijo, Rebecca Robbins, Beatriz Martínez-López, and Xin Liu
Paper page for Predicting antimicrobial resistance of bacterial pathogens using time series analysis PDF of Predicting antimicrobial resistance of bacterial pathogens using time series analysis DOI 10.3389/fmicb.2023.1160224
Preprint · Research Square · 2023
Outbreak Prediction in Swine Populations with Machine Learning
Halev, Avishai, Beatriz Martínez-López, Maria Clavijo, Carlos Gonzalez-Crespo, Jeonghoon Kim, Chao Huang, Rebecca Robbins, and Xin Liu
Paper page for Outbreak Prediction in Swine Populations with Machine Learning PDF of Outbreak Prediction in Swine Populations with Machine Learning DOI 10.21203/rs.3.rs-2814859/v1
Workshop Article · AAAI Workshop on AI for Agriculture and Food Systems · 2023
Constrained Reinforcement Learning for Autonomous Farming: Challenges and Opportunities
Liu, Yongshuai, Taeyeong Choi, and Xin Liu
Paper page for Constrained Reinforcement Learning for Autonomous Farming: Challenges and Opportunities Project for Constrained Reinforcement Learning for Autonomous Farming: Challenges and Opportunities
Conference Article · IEEE Symposium on Security and Privacy · 2023
Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective
Rezaei, Shahbaz, Zubair Shafiq, and Xin Liu
Project for Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective DOI 10.1109/SP46215.2023.10179463
Conference Article · Military Communications Conference · 2022
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters From System Perspective
Zhang, Huanle, Mi Zhang, Xin Liu, Prasant Mohapatra, and Michael DeLucia
Paper page for FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective DOI 10.1109/MILCOM55135.2022.10017717
Preprint · arXiv · 2022
Cross-Silo Federated Learning: Challenges and Opportunities
Huang, Chao, Jianwei Huang, and Xin Liu
Paper page for Cross-Silo Federated Learning: Challenges and Opportunities PDF of Cross-Silo Federated Learning: Challenges and Opportunities Project for Cross-Silo Federated Learning: Challenges and Opportunities DOI 10.48550/arxiv.2206.12949
Journal Article · Frontiers in Artificial Intelligence · 2022
Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-supervised Learning
Zhang, Huanle, Nicharee Wisuthiphaet, Hemiao Cui, Nitin Nitin, Xin Liu, and Qing Zhao
Paper page for Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-supervised Learning DOI 10.3389/frai.2022.863261
Journal Article · Computers and Electronics in Agriculture · 2022
Special Report: AI Institute for Next Generation Food Systems (AIFS)
Tagkopoulos, Ilias, Steve Brown, Xin Liu, Qing Zhao, Tarek I Zohdi, Mason Earles, Nitin Nitin, Daniel Runcie, Danielle G Lemay, Aaron Smith, Pamela Ronald, Hao Feng, and Gabriel Youtsey
Paper page for Special report: AI Institute for next generation food systems (AIFS)Project for Special report: AI Institute for next generation food systems (AIFS)DOI 10.1016/j.compag.2022.106819
Preprint · arXiv · 2022
Incentivizing Data Contribution in Cross-Silo Federated Learning
Huang, Chao, Shuqi Ke, Charles Kamhoua, Prasant Mohapatra, and Xin Liu
Paper page for Incentivizing Data Contribution in Cross-Silo Federated Learning PDF of Incentivizing Data Contribution in Cross-Silo Federated Learning Project for Incentivizing Data Contribution in Cross-Silo Federated Learning DOI 10.48550/arxiv.2203.03885
Conference Article · Asian Conference on Machine Learning · 2021
CTS2: Time Series Smoothing with Constrained Reinforcement Learning
Liu, Yongshuai and Xin Liu
Paper page for CTS2: Time Series Smoothing with Constrained Reinforcement Learning Project for CTS2: Time Series Smoothing with Constrained Reinforcement Learning
Conference Article · International Joint Conference on Artificial Intelligence · 2021
Policy Learning with Constraints in Model-free Reinforcement Learning: A Survey
Liu, Yongshuai, Avishai Halev, and Xin Liu
Project for Policy Learning with Constraints in Model-free Reinforcement Learning: A Survey DOI 10.24963/ijcai.2021/614
Journal Article · Frontiers in Veterinary Science · 2021
Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene
Kim, Jeonghoon, Kyuyoung Lee, Ruwini Rupasinghe, Shahbaz Rezaei, Beatriz Martínez-López, and Xin Liu
Paper page for Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene PDF of Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene DOI 10.3389/fvets.2021.683134
Conference Article · IFIP Networking Conference · 2021
Resource Allocation Method for Network Slicing Using Constrained Reinforcement Learning
Liu, Yongshuai, Jiaxin Ding, and Xin Liu
Project for Resource Allocation Method for Network Slicing Using Constrained Reinforcement Learning DOI 10.23919/IFIPNetworking52078.2021.9472202

Back to the team

Xin Liu

Project involvement

AI-Enabled Models for Reducing Postharvest Food Spoilage

Dietary Assistant Powered by Language Models and Knowledge Graphs

AI-Enabled Models for Food Digestion and Nutrient Delivery

AIFS publications

FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models

Implet: A Post-hoc Subsequence Explainer for Time Series Models

Adventurer: Exploration with BiGAN for Deep Reinforcement Learning

An Empirical Study on Impact of Label Noise on Synthetic Tabular Data Generation

Convergence Analysis of Split Federated Learning on Heterogeneous Data

Detection of Escherichia Coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy

An Accuracy-Shaping Mechanism for Competitive Distributed Learning

Explanation Space: A New Perspective Into Time Series Interpretability

Linear Bandits With Side Observations on Networks

Feduv: Uniformity and Variance for Heterogeneous Federated Learning

When Federated Learning Meets Oligopoly Competition: Stability and Model Differentiation

DAVIS-Ag: A Synthetic Plant Dataset for Developing Domain-Inspired Active Vision in Agricultural Robots

The AIFS Institute: Building a Better Food System Through AI

Promoting Collaboration in Cross-Silo Federated Learning: Challenges and Opportunities

Client Selection in Federated Learning: Principles, Challenges, and Opportunities

CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing

Infection Prediction in Swine Populations with Machine Learning

On the Impact of Label Noise in Federated Learning

On the Impact of Label Noise in Federated Learning

Federated Learning Hyperparameter Tuning From a System Perspective

Duopoly Business Competition in Cross-Silo Federated Learning

Quantifying Causal Path-Specific Importance in Structural Causal Model

Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importance

Predicting Antimicrobial Resistance of Bacterial Pathogens Using Time Series Analysis

Outbreak Prediction in Swine Populations with Machine Learning

Constrained Reinforcement Learning for Autonomous Farming: Challenges and Opportunities

Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective

FedTune: Automatic Tuning of Federated Learning Hyper-Parameters From System Perspective

Cross-Silo Federated Learning: Challenges and Opportunities

Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-supervised Learning

Special Report: AI Institute for Next Generation Food Systems (AIFS)

Incentivizing Data Contribution in Cross-Silo Federated Learning

CTS2: Time Series Smoothing with Constrained Reinforcement Learning

Policy Learning with Constraints in Model-free Reinforcement Learning: A Survey

Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene

Resource Allocation Method for Network Slicing Using Constrained Reinforcement Learning