I am an Assistant Professor in the Department of Biostatistics, leading the Liu Lab at Yale Biostatistics.
You can learn more about our research on my Yale profile.
Outside research, I enjoy swimming, hiking, and traveling to explore the world. I recently started learning tennis!
Latest
- Conditional Attribute Estimation with Autoregressive Sequence Models
- Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling
- BGM-IV: an AI-powered Bayesian generative modeling approach for instrumental variable analysis
- A Multi-modal Diffusion Model with Dual-Cross-Attention for Multi-Omics Data Generation and Translation
- A Bayesian Generative Modeling Approach for Arbitrary Conditional Inference
- How to improve polygenic prediction from whole-genome sequencing data by leveraging predicted epigenomic features?
- Deconer: An Evaluation Toolkit for Reference-based Deconvolution Methods Using Gene Expression Data
- An AI-powered Bayesian generative modeling approach for causal inference in observational studies
- Sampling-guided Heterogeneous Graph Neural Network with Temporal Smoothing for Scalable Longitudinal Data Imputation
- Leveraging genomic large language models to enhance causal genotype-brain-clinical pathways in Alzheimer's disease
- CREATE: cell-type-specific cis-regulatory elements identification via discrete embedding
- An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies
- EpiGePT: a pretrained transformer-basedlanguage model for context-specific humanepigenomics
- DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies
- Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring
- DeepDrug: A general graph-based deep learning framework for drug-drug interactions and drug-target interactions prediction
- Applications of transformer-based language models in bioinformatics: a survey
- Deep generative modeling and clustering of single cell Hi-C data
- HiChIPdb: a comprehensive database of HiChIP regulatory interactions
- AggEnhance: Aggregation Enhancement by Class Interior Points in Federated Learning with Non-IID Data
- Regulatory analysis of single cell multiome gene expression and chromatin accessibility data with scREG
- DualGCN: a dual graph convolutional network model to predict cancer drug response
- scGraph: a graph neural network-based approach to automatically identify cell types
- DeepCAGE: Incorporating Transcription Factors in Genome-Wide Prediction of Chromatin Accessibility
- Multimodal single cell data integration challenge: Results and lessons learned
- OpenAnnotate: a web server to annotate the chromatin accessibility of genomic regions
- Simultaneous deep generative modelling and clustering of single-cell genomic data
- Density estimation using deep generative neural networks
- Boost Neural Networks by Checkpoints
- DeepCDR: a hybrid graph convolutional network for predicting cancer drug response
- Feature-enhanced graph networks for genetic mutational prediction using histopathological images in colon cancer
- Mstree: a multispecies coalescent approach for estimating ancestral population size and divergence time during speciation with gene flow
- A decentralized system for medical data management via blockchain
- Reinforced molecular optimization with neighborhood-controlled grammars
- Quantifying functional impact of non-coding variants with multi-task Bayesian neural network
- EpiFIT: functional interpretation of transcription factors based on combination of sequence and epigenetic information
- hicGAN infers super resolution Hi-C data with generative adversarial networks
- Automatically structuring on Chinese ultrasound report of cerebrovascular diseases via natural language processing
- DeepHistone: a deep learning approach to predicting histone modifications
- Chromatin accessibility prediction via a hybrid deep convolutional neural network
- A sequence-based method to predict the impact of regulatory variants using random forest
- Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm