I am a researcher specializing in computational genomics and bioinformatics. My primary research focuses on developing and applying methods in statistical genetics, deep learning, and high-performance computing to advance the understanding of biological systems through genomic technologies and large-scale data analysis.
At the Center for Precision Psychiatry, University of Oslo, my main tasks are to develop and apply computational approaches to precision psychiatry, leveraging expertise in multi-omics and deep learning to uncover genetic factors underlying mental health. The ultimate goal is to bridge fundamental genomic research with clinical applications, contributing to the development of personalized medicine, particularly in psychiatric care.
I recently completed a master's program in Data Science at the University of Texas at Austin (2022–2025). I also earned a PhD in Bioinformatics from the Norwegian University of Life Sciences in the same period. Previously, I was affiliated with the Center for Biomedical Informatics at Vingroup Big Data Institute (2020-2022), Karolinska Institute (2019-2020), Tel Aviv University (2017-2020), and Ho Chi Minh City University of Science (2012-2016).
Software and Tools
- MiXeR-SV (https://github.com/precimed/mixer_sv): A statistical genetics framework for structural variant enrichment and heritability analysis
- DeepFARM ( https://github.com/datngu/DeepFARM ): A deep learning framework for sequence-based chromatin activity modeling and regulatory impact prediction of genetic variants in cattle, chicken, pig, and Atlantic salmon
- cscQTL ( https://github.com/datngu/cscQTL ): An integrative framework for circular RNA quantitative trait locus discovery
- LmTag ( https://github.com/datngu/LmTag ): Functional-enrichment and imputation-aware tag SNP selection for population-specific genotyping arrays
- nf-rasqual ( https://github.com/datngu/nf-rasqual ): A Nextflow pipeline for molecular quantitative trait loci mapping in small sample size datasets
- Circall ( https://github.com/datngu/Circall ): Fast and accurate methodology for discovery of circular RNAs from paired-end RNA-sequencing data