• Optimizing Resource Management for Shared Microservices: A Scalable System Design, ACM Transactions on Computer Systems, 2023
• Interference-aware Multiplexing for Deep Learning in GPU Clusters: A Middleware Approach, SC’2023
• The power of prediction: microservice auto scaling via workload learning, SoCC’2022
• HARMONY: Heterogeneity-Aware Hierarchical Management for Federated Learning System, IEEE/ACM Int’l Conf. on Microarchitecture, 2022
• Erms: Efficient Resource Management for Shared Microservices with SLA Guarantees, ACM ASPLOS’2023
• PDMA: Probabilistic service migration approach for delay-aware and mobility-aware mobile edge computing. Softw. Pract. Exp. 52(2): 394-414 (2022)
• Improving Concurrent GC for Latency Critical Services in Multi-tenant Systems, ACM/IFIP Middlware‘ 2022
• Characterizing Microservice Dependency and Performance: Alibaba Trace Analysis, ACM SoCC’2021 (best paper award)
• Cost-Driven Data Caching in the Cloud: An Algorithmic Approach, IEEE Infocom 2021.
• SmartDistance: A Mobile-based Positioning System for Automatically Monitoring Social Distance, IEEE Infocom 2021
• Multi-layer Coordination for High-Performance Energy-Efficient Federated Learning. IWQoS 2020: 1-10
• RS-pCloud: An Peer-to-Peer Based Edge-Cloud System for Fast Remote Sensing Image Processing, IEEE EDGE 2020 (best student paper award)
• SmartPC: Hierarchical Pace Control in Real-Time Federated Learning System. IEEE RTSS 2019: 406-418
• N. Tziritas, et al. Online live VM migration algorithms to minimize total migration time and downtime, IEEE IPDPS’2019.
• G. Xu and C. Xu, MEER: online estimation of optimal memory reservations for long lived containers in in-memory cluster computing. IEEE ICDCS’2019.
• Y. Zhao*, X. Gao*(equal), et al. Mayo: A Framework for Auto-generating Hardware Friendly Deep Neural Networks. In MobiSys EMDL workshop, Jun 2018.
• R. Li, C. Shen, H. He, Z. Xu and C. Xu. A lightweight secure data sharing scheme for mobile cloud computing. IEEE Transactions on Cloud Computing, April 2018.
• N. Tziritas, et al, Data replication and virtual machine migrations to mitigate network overhead in edge computing systems. IEEE Transactions on Sustainable Computing, June 2017.
• S. He, et al, Heterogeneity-aware collective I/O for parallel I/O systems with hybrid HDD/SSD servers, IEEE Transactions on Computers, 66(6):1091-1098, 2017.
• S. He, et al, HARL: optimizing parallel file systems with heterogeneity-aware region-level data layout. IEEE Transactions on Computers, 66(6):1048-1060, June 2017.
• Y. Wang, et al. On service migrations in the cloud for mobile accesses: a distributed approach, ACM Transactions on Autonomous Adaptive Systems, 12(2):1-25, May 2017.
• L. Zeng, et al, Raccoon: a novel network I/O allocation framework for workload-aware VM scheduling in virtual environments. IEEE Trans. on Parallel and Distributed Systems, 28(9):2651-2662, Sept 2017.
• S. He, et al, Using minmax-memory claims to improve in-memory workflow computations in the cloud, IEEE Trans. on Parallel and Distributed Systems, 28(4):1202-1214, April 2017.
• T. Maqsood, et al, Leveraging on deep memory hierarchies to minimize energy consumption and data access latency on single-chip cloud computers. IEEE Transactions on Sustainable Computing, 2(2):154-166, February 2017.
• L. Zeng, S. Xu, Y. Wang, K. Kent, D. Bremner, C. Xu, Toward cost-effective replica placements in cloud storage systems with QoS-awareness. Software: Practice and Experience, 47(6):813-829, June 2017.
• Y. Liu, et al, Barrier-Aware warp scheduling for throughput processor, ACM Int. Conf. on Supercomputing (ICS), June 2016.
• G. Xu, C. Xu and S. Jiang. Prophet: Scheduling Executors with Time-Varying Resource Demands on Data-Parallel Computation Frameworks. Proc. ICAC 2016: 45-54.
• D. Dilli, et al, A low disk-bound transaction logging system for in-memory distributed data stores, IEEE Proc. of Cluster Computing , September 2016 (Best paper award nominee).
• N. Tziritas, et al, On improving constrained single and group operator placement using evictions in big data environments, IEEE Transactions on Service Computing, Sept 2016.
• Z. Bei, et al, RFHOC: A random-forest approach to auto-tuning Hadoop’s configuration. IEEE Trans. on Parallel and Distributed Systems, 27(5): 1470-1483, May 2016.
• C. Jiang, et al, Two-Level Hybrid Sampled Simulation of Multithreaded Applications. ACM Transactions on Architecture and Code Optimization (TACO), 12(4): 39:1-39:25, April 2016.

• BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving, AAAI’2024
• HCPerf: Driving Performance-Directed Hierarchical Coordination for Autonomous Vehicles, ICDCS’2023
• Weakly Supervised Monocular 3D Object Detection Using Multi-View Projection and Direction Consistency, CVPR’2023
• Context‐aware trajectory prediction for autonomous driving in heterogeneous environments, Computer‐Aided Civil and Infrastructure Engineering, 2023
• Distributed Data-Sharing Consensus in Cooperative Perception of Autonomous Vehicles, ICDCS’2022
• MORA: Improving ensemble robustness evaluation with model-reweighing attack, NeurIPS’2022
• Boosting Active Learning via Improving Test Performance. AAAI 2022
• Semi-supervised 3D Object Detection with Proficient Teachers. ECCV (38) 2022
• HiTPR: Hierarchical Transformer for Place Recognition in Point Cloud,ICRA’2022
• Ada-Detector: Adaptive Frontier Detector for Rapid Exploration, ICRA’2022
• Grounding Commands for Autonomous Vehicles via Layer Fusion with Region-specific Dynamic Layer Attention. IROS 2022
• Learning Moving-Object Tracking with FMCW LiDAR. IROS 2022
• Peer-Assisted Robotic Learning: A Data-Driven Collaborative Learning Approach for Cloud Robotic Systems, ICRA’2021
• A general elimination strategy for camera motion estimation, ICRA’2021
• LAFEAT: Piercing Through Adversarial Defenses with Latent Features, CVPR’2021 (oral presentation) [Robustness and Security]
• Focused Quantization for Sparse CNNs. NeurIPS 2019: 5585-5594 [Model inference efficiency in AD]
• Dynamic channel pruning: feature boosting and suppression, ICLR 2019 [Model inference efficiency in AD]
• RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr, ICML‘2020, [Model buildup in small data samples]
• FPGA Implementation for CNN acceleration,  FTS’2019 [HW/SW co-design for model inference]
• B. Y. Liu, L. J. Wang, X. Q. Chen, L. X. Huang, C. Z. Xu, “Peer-Assisted Robotic Learning: A Data-Driven Collaborative Learning Approach for Cloud Robotic Systems”, in Proc. ICRA 2021.
• Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems With Heterogeneous Sensor Data. IEEE Robotics Autom. Lett. 5(2): 3509-3516 (2020)
• FC-SLAM: Federated Learning Enhanced Distributed Visual-LiDAR SLAM In Cloud Robotic System. ROBIO 2019
• Boyi Liu, Lujia Wang, Ming Liu, Chengzhong Xu: Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems. CoRR abs/1901.06455 (2019)
• A Robust Stereo Camera Localization Method with Prior LiDAR Map Constrains. ROBIO 2019

• TrafficAdaptor: An adaptive obfuscation strategy for vehicle location rrivacy against traffic flow aware attacks, ACM SIGSPATIAL, 2022
• Li Li, et al, SmartDistance：A　mobile　based　positioning　system　for　automatically　monitoring　social　distance，INFOCOM 2021: 1-10　（PDF ）
• L. Yan, et al, Employing opportunistic charging for electric taxicabs to reduce idle time, Proc of ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), vol 2(1), 2018 (Proc. of ACM on Ubicomp, 2018).
• H. Zhang, et al, Urban-Scale Human Mobility Modeling With Multi-Source Urban Network Data. IEEE/ACM Transactions on Networking 26(2): 671-684 (2018).
• Z. Yu, et al, MIA: Metric importance analysis for big data workload characterization, IEEE Trans. on Parallel and Distributed Systems, 29(6):1371-1384, June 2018.
• L. Yan, et al. CatCharger: Deploying wireless charging lanes in a metropolitan road network through categorization and clustering of vehicle traffic. INFOCOM 2017:1-9.
• B. Zhao, et al, A data-driven congestion diffusion model for characterizing traffic in metrocity scales, Proc. of IEEE Conf. on Big Data, 2017.
• FFD: A Federated Learning Based Method for Credit Card Fraud Detection. BigData Congress 2019 (PDF)
• J. Zhao, et al, Estimation of passenger route choice pattern using smart card data for complex metro systems. IEEE Trans. on Intelligent Transportation Systems, 18(4):790-801, April 2017.
• Y. Zhao, et al, ER-CRLB: An Extended Recursive Cramér-Rao Lower Bound Fundamental Analysis Method for Indoor Localization Systems, IEEE Transactions on Vehicular Technology, 66(2):1605-1618, February 2017.
• F. Zhang, et al. Spatiotemproal segmentation of metro trips using smart card data. IEEE Transactions on Vehicular Technology, 65(3):1137-1149, March 2016.
• F. Tang, M. Guo, S. Guo, C. Xu. Mobility prediction based joint stable routing and channel assignment for mobile ad hoc cognitive networks. IEEE Trans. on Parallel and Distributed Systems, 27(3): 789-803, March 2016.
• M. Chen, et al, A novel approach to system design for dialect speech interaction with NAO robot, ICAR 2017:476-481.
• Z. He, et al. Exploiting Real-Time Traffic Light Scheduling with Taxi Traces. ICPP 2016: 314-323.
• Z. Tian, et al. Real-time charging station recommendation system for electric-vehicle taxis. IEEE Trans. on Intelligent Transportation Systems, 17(11): 3098-3109, Nov. 2016.
• W. Xiong, et al. ShenZhen transportation system (SZTS): a novel big data benchmark suite. The Journal of Supercomputing 72(11): 4337-4364, Nov. 2016.

See AI-overview for brief descriptions of  fundamental AI research   

Adversarial Attack Evaluation:

• LAFIT: Efficient anLAFIT: Efficient and Reliable Evaluation of Adversarial Defenses with Latent Features, TPAMI, 2023
• MORA: Improving Ensemble Robustness Evaluation with Model Reweighing Attack, NeurIPS 2022
• Efficient Loss Function by Minimizing the Detrimental Effect of Floating-Point Errors on Gradient-Based Attacks, CVPR’2023
• LAFEAT: Piercing Through Adversarial Defenses with Latent Features, CVPR’2021 (oral presentation) 

Transfer Learning:

• RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr. ICML 2020: 6010-6019
• Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutual Information, ICASSP’2023
• SMILE: Sample-to-feature Mixup for Efficient Transfer Learning, Transactions on Machine Learning Research, 2023
• Knowledge Distillation with Attention for Deep Transfer Learning of Convolutional Networks. ACM Trans. Knowl. Discov. Data 16(3), 2022
• Boosting Active Learning via Improving Test Performance, AAAI 2022
• Adaptive Consistency Regularization for Semi-Supervised Transfer Learning. CVPR 2021
• Pay Attention to Features, Transfer Learn Faster CNNs. ICLR 2020

Model Pruning and Compression:

• Dynamic channel pruning: feature boosting and suppression, ICLR 2019
• Focused Quantization for Sparse CNNs. NeurIPS 2019: 5585-5594 
• FPGA Implementation for CNN acceleration,  FTS’2019 [HW/SW co-design for model inference]
• SpHMC: Spectral Hamiltonian Monte Carlo, AAAI, 2019.

Federated Learning:

• FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction, CVPR 2022
• Adaptive Channel Sparsity for Federated Learning under system heterogeneity, CVPR2023

Reinforcement Learning and Multiagent:

• Model-free λ-policy iteration for discrete-time linear quadratic regulation, TNNLS’2023
• Adaptive Fuzzy Leader-Follower Synchronization of Constrained Heterogeneous Multiagent Systems. IEEE Trans. Fuzzy Syst. 30(1): 205-219 (2022)
• Robust Actor-Critic Learning for Continuous-Time Nonlinear Systems With Unmodeled Dynamics. IEEE Trans. Fuzzy Syst. 30(6): 2101-2112 (2022)