Curriculum Vitae
DANG THANH TUNG
Project researcher
Laboratory of Medical Science Mathematics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences,
The University of Tokyo (Homepage).
EDUCATION
2021 - 2023 Doctor of Philosophy in Bioinformatics, The University of Tokyo.
Thesis: "Development of variable selection methods based on machine learning and Bayesian statistics for multi-omics microbiome data analysis" (Main publication) (Full thesis)
Supervisor: Professor Hiroyoshi IWATA (Google Scholar)
2020 - 2021 Master of Science in Bioinformatics, The University of Tokyo.
Thesis: "Random forest coupled with phylogenetic variable selection for microbiome study: A challenge for high dimensional big data" (Main publication)
Supervisor: Professor Hirohisa Kishino (Google Scholar)
2019 - 2020 Graduate Research Student (kenkyusei) in Bioinformatics, The University of Tokyo.
Research topic: "Stochastic Variational Inference for Bayesian Phylogenetics: A Case of CAT Model" (Main publication)
Supervisor: Professor Hirohisa Kishino (Google Scholar)
CAREER
2024 - Project researcher
Laboratory of Medical Science Mathematics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo (Homepage).
Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences (Homepage).
2021 - 2023 Research Assistant
Digital Cognitive Neuroscience Lab, The Center for Brain, Mind and KANSEI Research Sciences ('The BMK Center'), Hiroshima University (Homepage).
2021 - 2023 Researcher Fellowship
The Japan Society for the Promotion of Science (Homepage).
Laboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agriculture and Life Science, The University of Tokyo (Homepage).
RESEARCH INTERESTS
METHODOLOGY
Bayesian Statistics: Bayesian Infinite Mixture Processes, Bayesian Variable Selection, Stochastic Variational Inference.
Machine Learning: Automated Machine Learning (AutoML), Self-Supervised Learning (SSL).
Parallel Programming Environments: Open MPI (Python, C/C++).
APPLICATION
Nanopore techniques for biological samples.
Microbiome: 16S ribosomal RNA (rRNA) sequencing, Shotgun metagenomic sequencing, and so on.
Neuroimage: fMRI, EEG, MEG, and so on.