About
I work on AI for Biology, building biological foundation models and computational infrastructure toward AI-driven digital twins. My current projects include microbiome foundation models and interpretable multi-omic sequence-to-function models.
I am a PhD researcher at the University of New South Wales (UNSW), fully funded by the University, conducting research at the Victor Chang Cardiac Research Institute. I am also a research intern at Shanghai AI Laboratory, working with A/Prof. Siqi Sun in the BEAM Lab, and a long-term visiting researcher at the Artificial Intelligence Research and Innovation (CAIRI Lab), led by Prof. Stan Z. Li (IEEE Fellow).
My long-term vision is to build AI-driven digital twins of human health by integrating genomic, molecular, microbial, and clinical information across biological scales.
Research Interests
My research sits at the intersection of AI and biology, spanning three connected directions:
Interpretable Sequence-to-Function Models
Interpretable sequence-to-function models that connect DNA sequence, chromatin accessibility, gene expression, and cellular state.
Biological Foundation Models
Foundation and representation models for microbial communities, multi-omic data, and biological sequences.
AI for Molecular & Viral Discovery
Protein structure prediction and RNA virus identification from sequence and structural information.
News
- 🎉🎉🎉 Our Rider work is now published in Nature Communications: Deep learning coupled with scalable domain-specific structural validation expands RNA virus discovery from metatranscriptomes Nature Portfolio.
- 🎉🎉🎉 Our work on deep-learning-based RNA virus discovery, Deep learning coupled with scalable domain-specific structural validation expands RNA virus discovery from metatranscriptomes, was accepted by Nature Communications Nature Portfolio.
- Started research internships at Shanghai AI Laboratory and Fudan University.
- Moved to Sydney and joined the University of New South Wales (UNSW).
- 🎉🎉 Our article, Determining Antimicrobial Resistance in the Plastisphere: Lower Risks of Nonbiodegradable vs Higher Risks of Biodegradable Microplastics, was accepted by Environmental Science & Technology ACS journal.
- Joined the Department of Artificial Intelligence, School of Engineering, Westlake University.
- 🎉🎉 Our review, Determining the Contribution of Micro/Nanoplastics to Antimicrobial Resistance: Challenges and Perspectives, was accepted by Environmental Science & Technology ACS journal.
Professional Experience
- Research Intern Internship, Shanghai AI Laboratory and Fudan University
- Researcher Full-time, UNSW Microbiome Research Center
- Research Assistant Full-time, CAIRI and EMBLab, Westlake University
- Research Intern Internship, AI for Healthcare Group, Chinese University of Hong Kong
- Visiting Student Visiting, Southern University of Science and Technology
Selected Publications
Selected publications, including preprints and work in preparation.
Deep learning coupled with scalable domain-specific structural validation expands RNA virus discovery from metatranscriptomes
Nature Communications First author 2026 JIF 18.1 (2025) Nature Index journal
Rider combines deep representation learning with scalable RdRp structural validation to expand RNA virus discovery.
Expanding the RNA Virus Universe by Deep Learning Discovery
bioRxiv preprint First author 2025
Preprint version of the Rider framework for deep-learning-based RNA virus discovery.
Determining Antimicrobial Resistance in the Plastisphere: Lower Risks of Nonbiodegradable vs Higher Risks of Biodegradable Microplastics
Environmental Science & Technology First author 2025 JCR Q1 JIF 12.2 (2026) Nature Index journal Field-leading journal
Quantifying antimicrobial-resistance risk in the plastisphere.
Determining the Contribution of Micro/Nanoplastics to Antimicrobial Resistance: Challenges and Perspectives
Environmental Science & Technology First author 2023 JCR Q1 JIF 12.2 (2026) Nature Index journal Field-leading journal
A review of antimicrobial-resistance risk associated with micro- and nanoplastics.
Research Software
Open-source research software.
CompRanking: a pipeline for quantitatively ranking antimicrobial-resistance risk in environmental metagenomic samples
Open-source software GitHub
A pipeline for quantitative antimicrobial-resistance risk ranking in environmental metagenomic samples.
Rider: a deep-learning method for rapid RNA virus identification
Open-source software GitHub
A deep-learning tool for rapid RNA virus identification.
Open Resources
Rider RdRp Structure Database
Versioned non-redundant RdRp structure references supporting Rider's structural alignment workflow.
CompRanking Alignment Database
Version 1.1 curated alignment database for comparative antimicrobial-resistance analysis.
Global Water Pathogen Database (GWPD)
An open resource for waterborne pathogens and related knowledge. Contributing researcher.
Education
- PhD, University of New South Wales (UNSW)
- MEng, Harbin Institute of Technology (HIT)
- BSc, Jilin Agricultural University (JLAU)