About Me

I am currently a postdoctoral scholar from Department of Statistics, Stanford University, advised by Prof. Wing Hung Wong (NAS member). Prior to that, I was a PhD student from Tsinghua University, where I spent two years in Stanford University, jointly advised by Prof. Wing Hung Wong. My research area mainly covers statistical learning and machine learning algorithms. My current interests aim at solving several problems in statistics, such as density estimation, MCMC, likelihood-free Bayesian, causal inference with deep generative models. I'm also interested in applying machine learning algorithms to various problems in biomedical informatics and computational biology, which involves pharmacology data, biomedical data and next-generation sequencing (NGS) data analysis.

Recent News

More
  • 2020.09 - Our study on molecule optimization with reinforcement learning was accepted to NeurIPS 2020.
  • 2020.06 - Our study on drug sensitivity prediction with graph neural network was accepted to ECCB 2020.
  • 2020.06 - Our study on gene mutation prediction using histopathological images was accepted to MICCAI 2020.
  • 2019.09 - I will visit the Department of Statistics, Stanford University as a joint Ph.D. student advised by Wing Hung Wong.
  • 2019.07 - I will give a invited talk at Basel, Switzerland for ISMB 2019.
  • 2019.05 - I win the travel fellowship provided by International Society of Computational Biology (ISCB).

Employment

June. 2021 - Present
Department of Statistics, Stanford University, CA, USA
Postdoctoral Scholar

Education

Sep. 2019 - June. 2021
Department of Statistics, Stanford University, CA, USA
Joint Ph.D. student
Aug. 2016 - Sep. 2019
Department of Automation, Tsinghua University, Beijing, China
Ph.D. student
Aug. 2015 - Jan. 2016
Department of Computer Science, Lund University, Sweden
Exchange Student
Aug. 2012 - Jul. 2016
ShenYuan Honors College, Beihang University, Beijing, China
Bachelor of Engineering

Selected Publications

 
Desity Estimation with Deep Generative Neural Networks
Qiao Liu, Jiaze Xu, Rui Jiang, Wing Hung Wong
PNAS, 2021 [Abstract][PDF][Code]
 
Simultaneous Deep Generative Modeling and Clustering of Single-cell Genomic Data
Qiao Liu, Shengquan Chen, Rui Jiang, Wing Hung Wong
Nature Machine Intelligence, 2021 [Abstract][PDF][Code]
 
Boost Neural Networks by Checkpoints
Feng Wang, Guoyizhe Wei, Qiao Liu, Jinxiang Ou, Xian Wei, Hairong Lv
NeurIPS, 2021 [Abstract][PDF]
 
Reinforced Molecular Optimization with Neighborhood-Controlled Grammars
Chencheng Xu, Qiao Liu, Minlie Huang, Tao Jiang
NeurIPS, 2020 [Abstract][PDF][Code]
 
Feature-Enhanced Graph Networks for Genetic Mutational Prediction Using Histopathological Images in Colon Cancer
Kexin Ding, Qiao Liu, Edward Lee, Mu Zhou, Aidong Lu, Shaoting Zhang
MICCAI, 2020 [Abstract][PDF]
 
Incorporating Gene Expression in Genome-wide Prediction of Chromatin Accessibility via Deep Learning
Qiao Liu, Wing Hung Wong, Rui Jiang
Genomics, Proteomics & Bioinformatics, 2020 [Abstract][PDF][Code]
 
Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network
Qiao Liu, Zhiqiang Hu, Rui Jiang and Mu Zhou
ECCB/Bioinformatics, 2020 [Abstract][PDF][Code]
 
hicGAN Infers Super Resolution Hi-C Data with Generative Adversarial Networks
Qiao Liu, Hairong Lv, Rui Jiang
ISMB/Bioinformatics, 2019 [Abstract][PDF][Code]
 
Quantifying Functional Impact of Non-coding Variants With Multi-task Bayesian Neural Network
Chencheng Xu, Qiao Liu, Jianyu Zhou, Minzhu Xie, Jianxing Feng and Tao Jiang
Bioinformatics, 2019 [Abstract][PDF][Code]
 
Chromatin Accessibility Prediction via a Hybrid Deep Convolutional Neural Network
Qiao Liu, Xia Fei, Qijin Yin and Rui Jiang
Bioinformatics, 2017 [Abstract][PDF][Code]