news
02/26/2024 | Our research paper titled “MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling” has been accepted by CVPR 2024. |
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02/24/2024 | Our research paper titled “SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models” has been accepted by MLSys 2024. |
01/22/2024 | Our research paper titled “FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent” and “Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting” have been accepted by ICLR 2024. |
10/07/2023 | Our research paper titled “FedNAR: Federated Optimization with Normalized Annealing Regularization” has been accepted by NeurIPS 2023. |
08/14/2023 | I am deeply honored and thrilled to share that I have been selected as the recipient of the IEEE Cyber-Physical Systems (TCCPS) Outstanding Ph.D. Dissertation Award! |
05/08/2023 | I am beyond thrilled to share that I have been selected as one of the two recipients for the ECE Department Outstanding Ph.D. Dissertation Award for the academic year 22-23 at Duke University! |
04/27/2023 | Our research paper titled “Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction” has been accepted for presentation at ICML 2023. |
04/25/2023 | I am beyond thrilled to share that I have been selected as one of the two recipients for the ECE Department Outstanding Ph.D. Dissertation Award for the academic year 22-23 at Duke University! |
03/15/2023 | Our paper “AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving” has been accepted by MobiCom 2023! |
10/10/2022 | Our paper “FedSEA: A Semi-Asynchronous Federated Learning Framework for Extremely Heterogeneous Devices” has been accepted by SenSys 2022! |