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My research lies at the intersection of deep learning, dynamical systems, and robotics. I develop algorithms for combining deep learning with first-principles physics models. I explore how these modeling algorithms can facilitate robotic tasks such as model-based control, through simulation and hardware validations.
 T. Z. Jiahao, K. Y. Chee, and M. A. Hsieh, “Online Dynamics Learning for Predictive Control with an Application to Aerial Robots,” Conference on Robot Learning (CoRL), 2022.
 Y. Wu, T. Z. Jiahao, J. Wang, P. A. Yushkevich, M. A. Hsieh, and J. C. Gee, "NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registration," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
 K. Y. Chee, T. Z. Jiahao, and M. A. Hsieh, “Knode-mpc: A knowledge-based data-driven predictive control framework for aerial robots,” IEEE Robotics and Automation Letters (RA-L), vol. 7, no. 2, pp. 2819–2826, 2022.
 T. Z. Jiahao, L. Pan, and M. A. Hsieh, "Learning to swarm with knowledge-based neural ordinary differential equations," IEEE International Conference on Robotics and Automation (ICRA), 2022.
 T. Z. Jiahao, M. A. Hsieh, and E. Forgoston, “Knowledge-based learning of nonlinear dynamics and chaos,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 31, no. 11,p. 111101, 2021.
I am a partner writer on Medium. I blog about my research and other topics on Medium:
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