Guan-Ting Liu

Guan-Ting (Danny) Liu completed his Ph.D. in the Graduate Institute of Networking and Multimedia at National Taiwan University in Taipei, Taiwan. During his Ph.D. program, he is advised by Pu-Jen Cheng, Iris Hui-Ru Jiang, and Shao-Hua Sun. His research focuses on Reinforcement Learning, Machine Learning, and applying ML techniques for Design Automation Problems. Before entering the graduate school, he finished his bachelor's degree in Electronic Engineering with cross-discipline in Computer Science at NCTU (NYCU by now).

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I'm interested in Machine Learning, Reinforcement Learning(RL), and Electronic Design Automation (EDA). Much of my research is about interpretability and generalization of RL and applying machine learning techniques to solve EDA problems.

We propose a neural collaborative filtering-based framework capable of capturing the convoluted relationships between circuit pins and timing corners. This framework enables the precise identification of timing variant regions across different corners.

This work proposes Program Machine Policies (POMPs), which bridge the advantages of programmatic RL and state machine policies, allowing for the representation of complex behaviors and the address of long-term tasks.

Our proposed hierarchical programmatic reinforcement learning (HPRL) framework can produce program policies that describe out-of-distributionally complex behaviors and directly assign credits to programs that induce desired behaviors.

We are the first to generate SRAFs using reinforcement learning to address SRAF interference and produce mask-rule-compliant results directly.

We propose a novel technique that regularizes representation learning by comparing state embedding similarities across different state-action pairs.