News

Our paper "Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing" is accepted by IEEE/ACM Transactions on Networking

We are glad to share that our paper "Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing" has been accepted by the IEEE/ACM Transactions on Networking (ToN).The paper develops asynchronous, fractional multi-agent deep reinforcement learning to minimize the age of information in mobile edge computing, coordinating multiple agents that make decisions

Our paper "When Sample Selection Bias Precipitates Model Collapse" is accepted by ICML 2026

We are glad to share that our paper "When Sample Selection Bias Precipitates Model Collapse" has been accepted to the International Conference on Machine Learning (ICML 2026).The paper analyzes how sample selection bias can precipitate model collapse — the progressive quality loss that can occur when models are trained on selectively filtered or model-generated data — identifying the conditions un

Our paper "Timely CPU Scheduling for Computation-intensive Status Updates" is accepted by IEEE Transactions on Information Theory

We are glad to share that our paper "Timely CPU Scheduling for Computation-intensive Status Updates" has been accepted by the IEEE Transactions on Information Theory (TIT).The paper develops CPU scheduling policies that keep computation-intensive status updates fresh, characterizing and minimizing the age of information when each update requires non-trivial computation before it can be delivered.

Our paper "Minimizing Age of Information in Mobile Edge Computing: Nested Index Policy with Preemptive and Non-Preemptive Structure" is accepted by IEEE Transactions on Mobile Computing

We are glad to share that our paper "Minimizing Age of Information in Mobile Edge Computing: Nested Index Policy with Preemptive and Non-Preemptive Structure" has been accepted by the IEEE Transactions on Mobile Computing (TMC).The paper develops a nested index policy — in both preemptive and non-preemptive forms — for minimizing the age of information in mobile edge computing, giving a scalable s

Our paper "MoE²: Optimizing Collaborative Inference for Edge Large Language Models" is accepted by IEEE/ACM Transactions on Networking

We are glad to share that our paper "MoE²: Optimizing Collaborative Inference for Edge Large Language Models" has been accepted by the IEEE/ACM Transactions on Networking (ToN).The paper optimizes collaborative inference across a mixture of edge large language models, jointly improving response quality, latency, and serving cost when LLM requests are handled at the network edge.

Our paper "Heterogeneous Mean-Field Reinforcement Learning for Age-Minimal GPU Batching" is accepted by IEEE INFOCOM 2026

We are glad to share that our paper "Heterogeneous Mean-Field Reinforcement Learning for Age-Minimal GPU Batching" has been accepted to the IEEE International Conference on Computer Communications (INFOCOM 2026).The paper introduces a heterogeneous mean-field reinforcement learning approach to GPU request batching that minimizes the age of information, scaling to many heterogeneous jobs that must

Our paper "Conditional Age-at-Risk for Task Assignment across Heterogeneous Servers" is accepted by IEEE INFOCOM 2026

We are glad to share that our paper "Conditional Age-at-Risk for Task Assignment across Heterogeneous Servers" has been accepted to the IEEE International Conference on Computer Communications (INFOCOM 2026).The paper proposes a conditional age-at-risk criterion for assigning tasks across heterogeneous servers, balancing timeliness against tail risk when server speeds and reliability differ.

Our paper "FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness" is accepted by AAAI 2026

We are glad to share that our paper "FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness" has been accepted to the AAAI Conference on Artificial Intelligence (AAAI 2026).The paper presents FedShard, a federated unlearning framework that removes the influence of requested data while jointly maintaining efficiency fairness and performance fairness across participating cl

Assist Prof. Zhang Meng Joined ZJUI 2023 Junior Faculty Teaching Exchange and Competition

Read sourceThe ZJUI Junior Faculty Teaching Exchange and Competition was held successfully in the afternoon of October 26th, 2023. The competition was intended to augment faculty teaching, encourage junior faculty to update education and teaching ideology, and improve teaching effects as well as education quality.Prof. Lee Der-Horng, Dean of ZJUI, Prof. Jin Jianming, Executive Dean of ZJUI, Prof.