Curriculum Vitae
Embedded Files
Education
Education
Apr. 2022 - Present
Apr. 2022 - Present
Ph.D. Course at Graduate School of Agricultural and Life Sciences, The University of Tokyo, Japan (Lab. of Biometry and Bioinformatics)
Apr. 2025 - Jul. 2025
Apr. 2025 - Jul. 2025
Visiting researcher at UMR 7245 CNRS-MNHN, Molécules de Communication et Adaptation des Microorganismes (MCAM), France
Feb. 2023 - Oct. 2024
Ph.D. joint program (exchange student) at UMR MIA Paris-Saclay, AgroParisTech, INRAE, France
Apr. 2020 - Mar. 2022
MS at Graduate School of Agricultural and Life Sciences, The University of Tokyo, Japan (Lab. of Plant Breeding and Genetics)
Apr. 2016 - Mar. 2020
BS in Applied Biology, Faculty of Agriculture, The University of Tokyo, Japan
Employment
Employment
Apr. 2020 - Present
Apr. 2020 - Present
Teaching Assistant in Graduate School of Agricultural and Life Sciences, The University of Tokyo
Apr. 2022 - Oct. 2022
Support Engineer at Microsoft Japan (Permanent)
Aug. 2021 - Sep. 2021
Support Engineer at Microsoft Japan (Internship)
Grants / Awards
Grants / Awards
2023 - 2024
Grants from BGF (Bourses du Gouvernement Français; French Government Scholarship)
2023 - 2025
Grants from JSPS (Japan Society for the Promotion of Science) DC2
2022
Awarded as "An Excellent Student" by JASSO Scholarship Organization
2020 - 2022
Grants from JASSO Scholarship
Publications
Publications
Yoshioka H, Morota G, and Iwata H (2025). Reciprocal BLUP: A Predictability-Guided Multi-Omics Framework for Plant Phenotype Prediction. doi: https://doi.org/10.1101/2025.11.27.690927 (bioRxiv)
Yoshioka H, Morota G, and Iwata H (2025). Reciprocal BLUP: A Predictability-Guided Multi-Omics Framework for Plant Phenotype Prediction. doi: https://doi.org/10.1101/2025.11.27.690927 (bioRxiv)
Yoshioka H, Debeljak P, Prado S, and Iwata H (2025). Interpretable Machine Learning on Soybean Multi Omics Data Reveals Drought-Driven Shifts of Plant-Microbe Interactions. doi: https://doi.org/10.1101/2025.08.13.670005 (bioRxiv)
Yoshioka H, Debeljak P, Prado S, and Iwata H (2025). Interpretable Machine Learning on Soybean Multi Omics Data Reveals Drought-Driven Shifts of Plant-Microbe Interactions. doi: https://doi.org/10.1101/2025.08.13.670005 (bioRxiv)
Yoshioka H, Aubert J, and Mary-Huard T (2025). rrda: Ridge Redundancy Analysis for High-Dimensional Omics Data. doi:10.32614/CRAN.package.rrda (CRAN R Package)
Yoshioka H, Aubert J, and Mary-Huard T (2025). rrda: Ridge Redundancy Analysis for High-Dimensional Omics Data. doi:10.32614/CRAN.package.rrda (CRAN R Package)
Yoshioka H, Aubert J, Iwata H, and Mary-Huard T (2025). Ridge Redundancy Analysis for High-Dimensional Omics Data. doi:10.1101/2025.04.16.649138 (bioRxiv)
Yoshioka H, Aubert J, Iwata H, and Mary-Huard T (2025). Ridge Redundancy Analysis for High-Dimensional Omics Data. doi:10.1101/2025.04.16.649138 (bioRxiv)
Yoshioka H, Mary-Huard T, Aubert J, Toda Y, Ohmori Y, Yamasaki Y, Tsujimoto H, Takahashi H, Nakazono M, Takanashi H, Fujiwara T, Tsuda M, Kaga A, Fuji Y, Hirai MY, Nose Y, Kumaishi K, Usui E, Kobori S, Sato T, Narukawa M, Ichihashi Y, and Iwata H (2025). Integrating Proxy Intermediate Omics Traits in a Nonlinear Two-Step Model for Accurate Phenotype Prediction. doi: https://doi.org/10.1101/2025.03.14.643213 (bioRxiv)
Yoshioka H, Mary-Huard T, Aubert J, Toda Y, Ohmori Y, Yamasaki Y, Tsujimoto H, Takahashi H, Nakazono M, Takanashi H, Fujiwara T, Tsuda M, Kaga A, Fuji Y, Hirai MY, Nose Y, Kumaishi K, Usui E, Kobori S, Sato T, Narukawa M, Ichihashi Y, and Iwata H (2025). Integrating Proxy Intermediate Omics Traits in a Nonlinear Two-Step Model for Accurate Phenotype Prediction. doi: https://doi.org/10.1101/2025.03.14.643213 (bioRxiv)
Yoshioka H, Kimura K, Ogo Y, Ohtsuki N, Nishizawa-Yokoi A, Itoh H, Toki S, and Izawa T (2021) Real-Time Monitoring of Key Gene Products Involved in Rice Photoperiodic Flowering. Front. Plant Sci. 12:766450. doi: 10.3389/fpls.2021.766450
Yoshioka H, Kimura K, Ogo Y, Ohtsuki N, Nishizawa-Yokoi A, Itoh H, Toki S, and Izawa T (2021) Real-Time Monitoring of Key Gene Products Involved in Rice Photoperiodic Flowering. Front. Plant Sci. 12:766450. doi: 10.3389/fpls.2021.766450
Presentations
Presentations
Yoshioka H et al. (2024) “A Machine Learning Approach to Improve Yield Prediction Model with Multi-omics Data" The 55th Journées de Statistique de la SFdS, France.
Yoshioka H et al. (2024) “A Machine Learning Approach to Improve Yield Prediction Model with Multi-omics Data" The 55th Journées de Statistique de la SFdS, France.
Yoshioka H et al. (2022) “Modeling of plant-microbe interactions using multi-omics data" The 142nd Japanese Society of Breeding Meeting, Japan.
Yoshioka H et al. (2022) “Modeling of plant-microbe interactions using multi-omics data" The 142nd Japanese Society of Breeding Meeting, Japan.
Yoshioka H and Izawa T. (2022) “Development of in vivo monitoring system for proteins regulating photoperiodic flowering with Deep Learning.” Selection Symposium, John Innes Centre, UK.
Yoshioka H and Izawa T. (2022) “Development of in vivo monitoring system for proteins regulating photoperiodic flowering with Deep Learning.” Selection Symposium, John Innes Centre, UK.
Yoshioka H and Izawa T. (2022) “Development of in vivo monitoring system for proteins regulating photoperiodic flowering with Deep Learning.” Selection Symposium, Max Planck MPIPZ, Germany.
Yoshioka H and Izawa T. (2022) “Development of in vivo monitoring system for proteins regulating photoperiodic flowering with Deep Learning.” Selection Symposium, Max Planck MPIPZ, Germany.
Membership
Membership
Japanese Society of Breeding. 2022 – Present
Japanese Society of Breeding. 2022 – Present
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