Zhizhou Fang

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Dive into the research topics where Zhizhou Fang is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

1 Similar Profiles

MODIS: MLOps for Distributed Intelligent Cyber-physical Systems
Zdun, U. (Project Lead), Ntentos, E. (Scientific Project Staff), Warnett, S. J. (Scientific Project Staff), Fang, Z. (Scientific Project Staff), Chowdhary, D. (Scientific Project Staff), Urdih, F. (Scientific Project Staff), Amiri, A. (Scientific Project Staff) & Ennsberger, S. (Admin)
1/01/23 → 31/12/25
Project: Research cooperation

EDSVM: A Novel Approach for Environment Drift Detection in Reinforcement Learning Using Synthetic Drifted Examples
Fang, Z. & Zdun, U., Oct 2025, ECAI 2025: 28th European Conference on Artificial Intelligence, 25-30 October 2025, Bologna, Italy – Including 14th Conference on Prestigious Applications of Intelligent Systems (PAIS 2025), Proceedings. Lynce, I., Murano, N., Vallati, M., Villata, S., Chesani, F., Milano, M., Omicini, A. & Dastani, M. (eds.). IOS Press, p. 2410-2417 18 p. (Frontiers in Artificial Intelligence and Applications, Vol. 413).
Publications: Contribution to book › Contribution to proceedings › Peer Reviewed

Open Access
Detecting Environment Drift in Reinforcement Learning Using a Gaussian Process
Fang, Z. & Zdun, U., 2024, 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI 2024). Herndon: IEEE, p. 992-999 8 p.
Publications: Contribution to book › Contribution to proceedings › Peer Reviewed

Open Access

EDSVM: A Novel Approach for Environment Drift Detection in Reinforcement Learning Using Synthetic Drifted Examples
Fang, Z. (Speaker)
25 Oct 2025 → 30 Oct 2025
Activity: Talks and presentations › Talk or oral contribution › Science to Science
Detecting Environment Drift in Reinforcement Learning Using a Gaussian Process
Fang, Z. (Speaker)
30 Oct 2024
Activity: Talks and presentations › Talk or oral contribution › Science to Science

Zhizhou Fang

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Collaborations and top research areas from the last five years

Dive into details

MODIS: MLOps for Distributed Intelligent Cyber-physical Systems

EDSVM: A Novel Approach for Environment Drift Detection in Reinforcement Learning Using Synthetic Drifted Examples

Detecting Environment Drift in Reinforcement Learning Using a Gaussian Process

EDSVM: A Novel Approach for Environment Drift Detection in Reinforcement Learning Using Synthetic Drifted Examples

Detecting Environment Drift in Reinforcement Learning Using a Gaussian Process

Zhizhou Fang

Fingerprint

Collaborations and top research areas from the last five years

Projects

MODIS: MLOps for Distributed Intelligent Cyber-physical Systems

Publications

EDSVM: A Novel Approach for Environment Drift Detection in Reinforcement Learning Using Synthetic Drifted Examples

Detecting Environment Drift in Reinforcement Learning Using a Gaussian Process

Activities

EDSVM: A Novel Approach for Environment Drift Detection in Reinforcement Learning Using Synthetic Drifted Examples

Detecting Environment Drift in Reinforcement Learning Using a Gaussian Process