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 interests lie on the intersection of machine learning, statistics, and computational biology. I'm especially fascinated in solving several problems in statistics, such as density estimation, causal inference, likelihood-free Bayesian, with deep generative models. Besides, I'm also interested in various problems in computational biology and biomedical informatics, which involves genomic data, pharmacology data, biomedical data analysis.

Recent News

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  • 2021.02 - Our study on single cell analysis with deep generative models was accepted to Nature Machine Intelligence.
  • 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).

Academic Appointment

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

- Generative models in Statistics -
 
CausalEGM: a general causal inference framework by encoding generative modeling
Qiao Liu, Zhongren Chen, Wing Hung Wong
arXiv, 2022 [Paper][Code]
 
Desity Estimation with Deep Generative Neural Networks
Qiao Liu, Jiaze Xu, Rui Jiang, Wing Hung Wong
PNAS, 2021 [Paper][Code]
- Generative models in computational biology -
 
Deep generative modeling and clustering of single cell Hi-C data
Qiao Liu, Wanwen Zeng, Wei Zhang, Sicheng Wang, Hongyang Chen, Rui Jiang, Mu Zhou, Shaoting Zhang
Briefings in Bioinformatics, 2022 [Paper]
 
Simultaneous Deep Generative Modeling and Clustering of Single-cell Genomic Data
Qiao Liu, Shengquan Chen, Rui Jiang, Wing Hung Wong
Nature Machine Intelligence, 2021 [Paper][PDF][Code]
 
hicGAN Infers Super Resolution Hi-C Data with Generative Adversarial Networks
Qiao Liu, Hairong Lv, Rui Jiang
ISMB/Bioinformatics, 2019 [Paper][ISMB talk slides][Code]
- Others -
 
HiChIPdb: a comprehensive database of HiChIP regulatory interactions
Wanwen Zeng*,Qiao Liu*, Qijin Yin*, Rui Jiang, Wing Hung Wong
Nucleic Acids Research, 2022 [Database Link][Paper]
 
Incorporating Gene Expression in Genome-wide Prediction of Chromatin Accessibility via Deep Learning
Qiao Liu, Wing Hung Wong, Rui Jiang
Genomics, Proteomics & Bioinformatics, 2020 [Paper][Code]
 
Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network
Qiao Liu, Zhiqiang Hu, Rui Jiang and Mu Zhou
ECCB/Bioinformatics, 2020 [Paper][ECCB talk slides][Code]
 
Chromatin Accessibility Prediction via a Hybrid Deep Convolutional Neural Network
Qiao Liu, Xia Fei, Qijin Yin and Rui Jiang
Bioinformatics, 2017 [Paper][PDF][Code]