Michael Shieh

Michael Shieh is an Assistant Professor in the Department of Computer Science at the National University of Singapore. He obtained his Master's and PhD from Carnegie Mellon University and his bachelor's degree from Shanghai Jiao Tong University. He conducted research at Google DeepMind for two years. His research interests include Large Language Models, Deep Learning, and Natural Language Processing. He worked on semi-supervised learning and published Noisy Student and UDA. He also contributed to the RACE benchmark. He has been an area chair for machine learning and AI conferences such as NeurIPS, ICML, and ICLR. His teaching includes courses such as CS6216 Advanced Topics in Machine Learning and CS6280 Deep Learning with Language Applications.

Michael Qizhe Shieh is a researcher focused on Large Language Models and AI. His work explores methods to enhance model performance through techniques such as unsupervised data augmentation, self-training, and adversarial feature learning. He has contributed to areas like reasoning, knowledge transfer, and safety mechanisms in LLMs. His research includes creating large-scale datasets for reading comprehension and cloze tests, as well as improving textual graph learning and code repository integration. Shieh's publications also address challenges in visual reasoning, model selection, and prompt optimization, emphasizing robustness and interpretability in AI systems.