Dian Chen

Orcid: 0009-0000-7641-454X

Affiliations:
  • Virginia Tech, Department of Computer Science, Falls Church, VA, USA


According to our database1, Dian Chen authored at least 12 papers between 2023 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
AGRI-Fidelity: Evaluating the Reliability of Listenable Explanations for Poultry Disease Detection.
CoRR, March, 2026

Beyond Binary Opinions: A Deep Reinforcement Learning-Based Approach to Uncertainty-Aware Competitive Influence Maximization.
IEEE Trans. Netw. Sci. Eng., 2026

X-MAP: eXplainable Misclassification Analysis and Profiling for Spam and Phishing Detection.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2026

2025
Sustainable Smart Farm Networks: Enhancing Resilience and Efficiency with Decision Theory-Guided Deep Reinforcement Learning.
CoRR, May, 2025

fair-LDP: Uncertainty-Guided Fairness and Privacy for Federated Healthcare Learning.
Proceedings of the IEEE International Conference on Data Mining, 2025

Ethical AI for Healthcare Systems: Uncertainty-Aware, Fair Federated Learning.
Proceedings of the ACM/IEEE International Conference on Connected Health: Applications, 2025

2024
Energy-Adaptive and Robust Monitoring for Smart Farms Based on Solar-Powered Wireless Sensors.
IEEE Internet Things J., September, 2024

SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms.
CoRR, 2024

eMTD: Energy-Aware Moving Target Defense for Sustainable Solar-powered Sensor-based Smart Farms.
Proceedings of the NOMS 2024 IEEE Network Operations and Management Symposium, 2024

susFL: Federated Learning-based Monitoring for Sustainable, Attack-Resistant Smart Farms.
Proceedings of the IEEE International Conference on Big Data, 2024

2023
Attack-Resistant, Energy-Adaptive Monitoring for Smart Farms: Uncertainty-Aware Deep Reinforcement Learning Approach.
IEEE Internet Things J., August, 2023

A Survey on Attacks and Their Countermeasures in Deep Learning: Applications in Deep Neural Networks, Federated, Transfer, and Deep Reinforcement Learning.
IEEE Access, 2023


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