Choosing Appropriate AI-enabled Edge Devices, Not the Costly Ones. - Ziyang Zhang; Feng Li; Changyao Lin; Shihui Wen; Xiangyu Liu; Jie Liu 001 (ICPADS, 2021)

ECSRL: A Learning-Based Scheduling Framework for AI Workloads in Heterogeneous Edge-Cloud Systems. - Changyao Lin; Ziyang Zhang; Huan Li; Jie Liu 001 (SenSys, 2021)

Using Householder's method to improve the accuracy of the closed-form formulas for implied volatility. - Daniel Wei-Chung Miao; Xenos Chang-Shuo Lin; Changyao Lin (Math. Methods Oper. Res., 2021)

POS: An Operator Scheduling Framework for Multi-model Inference on Edge Intelligent Computing. - Ziyang Zhang; Huan Li; Yang Zhao 020; Changyao Lin; Jie Liu 001 (IPSN, 2023)

BCEdge: SLO-Aware DNN Inference Services with Adaptive Batching on Edge Platforms. - Ziyang Zhang; Huan Li; Yang Zhao 020; Changyao Lin; Jie Liu 001 (CoRR, 2023)

StressViT: Splitting and Compressing Vision Transformer Through Edge-Cloud Collaboration. - Changyao Lin; Yi Liu; Xiangyu Li; Chengxiang Li; Hao Zhang 016; Jing Jin; Jie Liu 001 (ICPR (5), 2024)

Poster Abstract: Xpi: Real-Time Progressive Inference Serving with Explainable AI in Edge-Cloud Systems. - Changyao Lin; Zhenming Chen; Jie Liu 001 (IPSN, 2024)

COS: Cross-Processor Operator Scheduling for Multi-Tenant Deep Learning Inference. - Changyao Lin; Jie Liu 001 (IWQoS, 2024)

A QoS-Aware Training Framework for ViT Compression, Partition, and Distillation. - Changyao Lin; Chengxiang Li; Jie Liu 001 (IWQoS, 2024)

DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative Inference. - Ziyang Zhang; Yang Zhao 020; Huan Li; Changyao Lin; Jie Liu 001 (IEEE Trans. Mob. Comput., 2024)

E4: Energy-Efficient DNN Inference for Edge Video Analytics via Early Exiting and DVFS. - Ziyang Zhang; Yang Zhao 020; Ming-Ching Chang; Changyao Lin; Jie Liu 001 (AAAI, 2025)

E3: Early Exiting with Explainable AI for Real-Time and Accurate DNN Inference in Edge-Cloud Systems. - Changyao Lin; Zhenming Chen; Ziyang Zhang; Jie Liu 001 (SenSys, 2025)

Exploiting Operator-Level Concurrency Control to Guide Deployment for Real-Time Tasks in Edge AI Cluster. - Changyao Lin; Ziyang Zhang; Jie Liu 001 (SenSys, 2025)

TOP: Task-Based Operator Parallelism for Asynchronous Deep Learning Inference on GPU. - Changyao Lin; Zhenming Chen; Ziyang Zhang; Jie Liu 001 (IEEE Trans. Parallel Distributed Syst., 2025)

E4: Energy-Efficient DNN Inference for Edge Video Analytics Via Early-Exit and DVFS. - Ziyang Zhang; Yang Zhao 020; Ming-Ching Chang; Changyao Lin; Jie Liu 001 (CoRR, 2025)