Rate My Professor Percy Liang

His research specializes in machine learning and natural language processing, encompassing robustness, interpretability, human interaction, learning theory, grounding, semantics, reasoning, and particularly foundation models such as language models. Key publications include 'Prefix-Tuning: Optimizing Continuous Prompts for Generation' (Li and Liang, ACL 2021), 'WILDS: A Benchmark of in-the-Wild Distribution Shifts' (Koh et al., JMLR 2021), 'Stronger Data Poisoning Attacks Break Data Sanitization Defenses' (Koh, Steinhardt, and Liang, Machine Learning 2021), 'Concept Bottleneck Models' (Koh et al., ICML 2020), and 'Robust Encodings: A Framework for Combating Adversarial Typos' (Jones et al., ACL 2020). Liang has received major awards including the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), NSF CAREER Award (2016), Sloan Research Fellowship (2015), and Microsoft Research Faculty Fellowship (2014), along with best paper awards at ACL, EMNLP, ICML, COLT, ISMIR, CHI, UIST, and RSS. He has mentored over 30 Ph.D. students and postdocs, many now faculty at institutions like UC Berkeley, Stanford, NYU, and researchers at OpenAI, Google DeepMind, and Meta. Liang teaches courses including Language Models from Scratch (CS336), Advances in Foundation Models (CS324), and Artificial Intelligence: Principles and Techniques (CS221).