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.