Research

My research focuses on probabilistic models for learning problems arising from real applications. My research interests include probabilistic graphical models, Bayesian deep learning, spatial data modeling, text modeling. See my publication list.

Ongoing research projects

• Amortized variational inference for Gaussian processes [Liu & Liu, 2018]

• Embedding models: generalization of embedding models to items [Liu & Blei, 2017] [bird embedding].

• Spatial modeling: model spatial data with Gaussian processes

• Image Segmentation/Generation: segmenting images from probabilistic models

Previous research projects

Gaussian approximation of CGM, Superset (partial) label learning, Active learning for clustering, Labeling extremely imbalanced data

Teaching

• Spring 2019: COMP 150 Deep Neural Networks
• Fall 2018: COMP 135 Introduction to Machine Learning
• Spring 2018: COMP 135 Introduction to Machine Learning
• Fall 2017: COMP 150-01 Machine Learning for Ecology and Sustainability

Bio Sketch

I received my B.S. in computer science from Hebei University of Technology in 2006. After three years study in LAMDA group, I received my M.S. degree from Nanjing Univeristy in 2009. My advisor was Prof. Yuan Jiang and Zhi-Hua Zhou. Then I worked in Alibaba for one year and a half. After that, I went to Oregon State University and completed my PhD degree in 2016. My PhD advisor was Prof. Thomas Dietterich. Prior to joining Tufts, I worked as a postdoc researcher at Columbia University working with Prof. David Blei.