CV

Education

• Ph.D in Mechanical Engineering, Delft University of Technology, 2026.01 (Planed, On time)
  • Research Interests: Uncertainty Quantification, Bayesian deep learning, Multi-fidelity modeling, Computational mechanics.
  • Advisors: Miguel A. Bessa, Marcel H.F. Sluiter, and Baris Caglar.
• M.S. in Design and Construction of Naval Architecture and Ocean Structure, Huazhong University of Science and Technology, 2021.06
  • Research Interests: Data-driven structure reliability analysis, Bayesian optimization.
  • Dissertation: Improved Sequential Kriging-Monte Carlo Simulation Reliability Analysis Methods and Their Applications to Ship Structural Design
  • Advisors: Jun Liu and Yuansheng Cheng.
  • GPA: 3.90/4.00
• B.S. in Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, 2018.06
  • GPA: 3.81/4.0

Selected Awards

• 2021 Outstanding Master Graduate, HUST
• 2020 National Scholarship for Postgraduates, HUST
• 2020 Merit Master Student, HUST
• 2018 Outstanding Graduate, HUST
• 2017 Merit Undergraduate Student, HUST
• 2017 National Encouragement Scholarship, HUST
• 2016 National Encouragement Scholarship, HUST
• 2016 Excellent Student Cadre, HUST

Publications

Single-to-multi-fidelity history-dependent learning with uncertainty quantification and disentanglement

Yi, J., Ferreira, B. P., & Bessa, M. A. (2025). “Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement.” arXiv preprint arXiv:2507.13416.

Cooperative Bayesian and Variance Networks Disentangle Aleatoric and Epistemic Uncertainties

Yi, J. & Bessa, M. (2025). “Cooperative Bayesian and Variance Networks Disentangle Aleatoric and Epistemic Uncertainties.” arXiv preprint arXiv:2505.02743.

Practical Multi-fidelity Machine Learning: Fusion of Deterministic and Bayesian Models

Yi, J., Cheng, J., & Bessa, M. (2024). “Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models.” arXiv preprint arXiv:2407.15110.

Hierarchize Pareto Dominance in Multi-Objective Stochastic Linear Bandits

Cheng, J., Xue, B., Yi, J., & Zhang, Q. (2024). “Hierarchize Pareto Dominance in Multi-Objective Stochastic Linear Bandits.” *Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24)*, pp. 11489–11497.

rvesimulator: An Automated Representative Volume Element Simulator for Data-Driven Material Discovery

Yi, J. & Bessa, M. (2023). “rvesimulator: An automated representative volume element simulator for data-driven material discovery.” AI4Mat 2023 Workshop, NeurIPS.

Talks

Learning Uncertainty-Aware Composite Constitutive Laws via Bayesian Recurrent Neural Network

August 22, 2024

Conference proceedings talk at 2024 SES Annual Technical Meeting, Hangzhou China

Bayesian design of recycled composite polymers with predictable uncertain behavior

June 07, 2024

Conference proceedings talk at ECCOMAS CONGRESS 2024: 9th European Congress on Computational Methods in Applied Sciences and Engineering, Lisboa, Portugal

Bayesian Neural Networks Predicting Aleatoric and Epistemic Uncertainties

February 28, 2024

Conference proceedings talk at 2024 SIAM Conference on Uncertainty Quantification, Trieste, Italy

Skills

• Coding Python, Matlab, C, C++ , FORTRAN, Git, Latex, …

• Software AutoCAD, ABAQUS, ANSYS, ParaView, …

• Tools MS Office, Origin, Inkscape, …

Miscellaneous Experience

Reviewer for:
Reliability Engineering & System Safety,
Computer Methods in Applied Mechanics and Engineering,
Applied Soft Computing,
Aerospace Science and Technology,
Engineering with Computers,
Information Sciences,
Computational Mechanics,
Probabilistic Engineering Mechanics,
Mechanics of Materials