Paul Pu Liang

Carnegie Mellon University


I am a Ph.D. student in Machine Learning at Carnegie Mellon University, advised by Louis-Philippe Morency and Ruslan Salakhutdinov. My long-term research goal is to build socially intelligent embodied agents with the ability to perceive and engage in multimodal human interaction. As steps towards this goal, my research focuses on 1) the fundamentals of multimodal learning, specifically the representation, translation, fusion, and alignment of heterogeneous data sources, 2) human-centered language, vision, speech, robotics, and healthcare applications, as well as, 3) the real-world deployment of socially intelligent agents by improving fairness, robustness, and interpretability. My research has received the distinguished student paper award at the NeurIPS 2019 workshop on federated learning and the best paper honorable mention award at ICMI 2017. I regularly organize the workshop on human multimodal language (NAACL 2021, ACL 2020, and ACL 2018), organized the workshop on tensor networks at NeurIPS 2020, and was a workflow chair for ICML 2019. For more information, please visit his website: www.cs.cmu.edu/~pliang/.