Lili Mou, PhD

Assistant Professor, Faculty of Science - Computing Science


Assistant Professor, Faculty of Science - Computing Science



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:

  1. Collaborator of me
  2. Has a publication at a reputable venue (such as those I have published)
  3. Recommended by a trusted researcher who I know

Due to the time constraint, I may not reply every email if you don't meet the above criteria.


CMPUT 367 - Intermediate Machine Learning

This course in machine learning focuses on higher-dimensional data and a broader class of nonlinear function approximation approaches. Topics include: optimization approaches (constrained optimization, hessians, matrix solutions), kernel machines, neural networks, dimensionality reduction, latent variables, feature selection, more advanced methods for assessing generalization (cross-validation, bootstrapping), introduction to non-iid data and missing data. Credit cannot be obtained for both CMPUT 367 and CMPUT 466. Prerequisites: CMPUT 204 and 267; one of MATH 115, 118, 136, 146, or 156.

CMPUT 466 - Machine Learning

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. Credit cannot be obtained for both CMPUT 367 and CMPUT 466. Prerequisites: CMPUT 204 or 275; MATH 125; CMPUT 267 or MATH 214; or consent of the instructor.

CMPUT 499 - Topics in Computing Science

This topics course is designed for a one on one individual study course between a student and an instructor. Prerequisites are determined by the instructor in the course outline. See Note (3) above.

CMPUT 566 - Topics in Computing Science

CMPUT 605 - Topics in Computing Science

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