Dr. Lili Mou is an Assistant Professor at the Department of Computing Science, University of Alberta. He is also an Alberta Machine Intelligence Institute (Amii) Fellow and a Canada CIFAR AI (CCAI) Chair. Lili received his BS and PhD degrees in 2012 and 2017, respectively, from School of EECS, Peking University. After that, he worked as a postdoctoral fellow at the University of Waterloo and a research scientist at Adeptmind (a startup in Toronto, Canada). His research interests include deep learning applied to natural language processing as well as programming language processing. He has publications at top conferences and journals, including AAAI, ACL, CIKM, COLING, EMNLP, ICASSP, ICLR, ICML, IJCAI, INTERSPEECH, NAACL-HLT, NeurIPS, and TACL (in alphabetical order).
My research mission is to build an intelligent system that can understand and interact with humans via natural language, involving both text understanding and generation. Towards this long-term goal, I am focusing on fundamental problems in machine learning (especially, deep learning) methods applied to natural language processing, including feature extraction in the discrete input space, weakly supervised learning in the discrete latent space, and sentence synthesis in the discrete output space. My work has been successfully applied to various NLP tasks, including information extraction, semantic parsing, syntactic parsing, text generation, and many others.
I am admitting all-level students. Applicants should be addressed to the department portal.
If you meet one of the following criteria, please directly contact me:
Due to the time constraint, I may not reply every email if you don't meet the above criteria.
Probabilistic graphical models (PGMs; including Bayesian Belief Nets, Markov Random Fields, etc.) now contribute significantly to many areas, including expert systems, computer perception (vision and speech), natural language interpretation, automated decision making, and robotics. This course provides an introduction to this field, describing semantics, inference and learning, as well as practical applications of these systems. Programming assignments will include hands-on experiments with various reasoning systems. Prerequisites: one of CMPUT 340 or 418; one of STAT 141, 151, 235 or 265 or SCI 151; or consent of the instructor.Fall Term 2021
Learning is essential for many real-world tasks, including recognition, diagnosis, forecasting and data-mining. This course covers a variety of learning scenarios (supervised, unsupervised and partially supervised), as well as foundational methods for regression, classification, dimensionality reduction and modeling. Techniques such as kernels, optimization and probabilistic graphical models will typically be introduced. It will also provide the formal foundations for understanding when learning is possible and practical. Prerequisites: one of CMPUT 340 or 418; one of STAT 141, 151, 235 or 265 or SCI 151; or consent of the instructor.Fall Term 2021 Winter Term 2022