Ph.D. 2022
Now: Hitachi America, Ltd.
- Ph.D., Mechanical Engineering, Iowa State University, 2022
- B.S., Mechanical Engineering, Iowa State University, 2016
Xian Yeow received his bachelor's degree in Mechanical Engineering from Iowa State University in 2016 and went on to pursue his Ph.D. at Iowa State. His research interests included applications of deep reinforcement learning and deep generative methods for the robust and efficient design of cyber-physical systems.
He graduated in May 2022 and is currently working at Hitachi America, Ltd.
Research interests: Deep reinforcement learning, deep generative methods, robust and efficient design of cyber-physical systems
Publications with the lab (21)
- A study of natural robustness of deep reinforcement learning algorithms towards adversarial perturbations — AI Open (2024)
- Deep Reinforcement Learning for Robotic Control with Multi-Fidelity Models — IFAC-PapersOnLine (2022)
- Multi-fidelity machine learning models for structure–property mapping of organic electronics — Computational Materials Science (2022)
Show all 21
- A graph policy network approach for volt-var control in power distribution systems — Applied Energy (2022)
- Generative design of material microstructures for organic solar cells using diffusion models — AI for Accelerated Materials Design NeurIPS 2022 Workshop (2022)
- Distributed online non-convex optimization with composite regret — 2022 58th Annual Allerton Conference on Communication, Control, and … (2022)
- Stochastic conservative contextual linear bandits — 2022 IEEE 61st Conference on Decision and Control (CDC) (2022)
- MDPGT: momentum-based decentralized policy gradient tracking — Proceedings of the AAAI conference on artificial intelligence (2022)
- Multi-resolution 3D CNN for learning multi-scale spatial features in CAD models — Computer Aided Geometric Design (2021)
- Fast inverse design of microstructures via generative invariance networks — Nature Computational Science (2021)
- Inverse design of microstructures via generative networks for organic solar cells — AAAI 2022 Workshop on AI for Design and Manufacturing (ADAM) (2021)
- Query-based targeted action-space adversarial policies on deep reinforcement learning agents — Proceedings of the ACM/IEEE 12th international conference on cyber-physical … (2021)
- Robustifying reinforcement learning agents via action space adversarial training — 2020 American control conference (ACC) (2020)
- Spatiotemporally constrained action space attacks on deep reinforcement learning agents — Proceedings of the AAAI conference on artificial intelligence (2020)
- Differentiable programming for piecewise polynomial functions — (2020)
- Two Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures — Data in Brief (2020)
- Automated detection of part quality during two-photon lithography via deep learning — Additive Manufacturing (2020)
- Multi-Level 3D CNN for Learning Multi-Scale Spatial Features — CVPR 2019 Workshop on Deep Learning for Geometric Shape Understanding (2019)
- Learning to cope with adversarial attacks — arXiv preprint arXiv:1906.12061 (2019)
- A case study of deep reinforcement learning for engineering design: Application to microfluidic devices for flow sculpting — Journal of Mechanical Design (2019)
- Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning — Deep Reinforcement Learning Workshop, NeurIPS 2018 (2018)
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