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Electric Power to the People

​オックスフォード大学の機械学習PhD学生のブログ

​略歴

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足立 真輝(アダチマサキ)

  • オックスフォード大学機械学習PhD(クラレンドン特待生)

  • データサイエンティスト(トヨタR&D)
  • 2014年度東京大学総長賞受賞者

オックスフォード大学で、エネルギー領域への機械学習の応用の研究をしている博士課程の学生です。同時に、トヨタ自動車の東富士研究所に所属しています。電気自動車、再生可能エネルギーなどの普及において、経済的な実利を付与することに機械学習を活用したいと考えています。

​仕事の依頼やお問い合わせは、こちらからお願いします。

​リンク

​​Language

Click the button [EN] English [JA] Japanese

上部[EN]ボタンをJAに変更で日本語になります

Email

   masaki [atmark] robots.ox.ac.uk
Links

Home: About Me

​ニュース

  • 2023年3月
    以下の機関で招待講演を行いました。
         1. Battery Modelling Webinar Series (BMWS) hosted by Carnegie Mellon University [Slide]
         2. Machine Learning Group hosted by Prof. Motonobu Kanagawa, EURECOM, France [link] [Slide]
         3. Machine and Human Intelligence research group hosted by Prof. Luigi Acerbi, University of Helsinki, Finland.
         4. Data Science and AI Research Group hosted by Dr. Festus Adedoyin, Bournemouth University, UK.

  • 2023年3月
    制御の国際学会IFAC 2023に以下の論文がアクセプトされました。
    Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature 
    [arXiv] [GitHub]

  • 2023年2月
    以下のプレプリントを公開されました。
    ​     1. SOBER: Scalable Batch Bayesian Optimization and Quadrature using Recombination Constraints [arXiv]

         2. Machine learning benchmarks for the classification of equivalent circuit models from solid-state electrochemical impedance spectra [arXiv] [GitHub]

  • 2023年1月
    Inferable Energy Ltd.という会社を設立しました。主に講演会や執筆活動などを行っています。
    私と一緒に働くご興味やご相談がありましたら、是非連絡フォームまたはemailからご連絡ください。

  • 2022年12月
    AI・機械学習のトップカンファレンスNeurIPS 2022に以下の論文がアクセプトされました。

    Fast Bayesian inference with batch Bayesian quadrature via kernel recombination [Paper] [arXiv] [OpenReview] [GitHub

  • 2022年6
    BatteryDEVという国際的ハッカソンイベントを主催しました。
        1. ドイツのメディアZEIT für Klimaに取材を受けました。 [YouTube]
        2.  イベントのウェブサイトはこちら
     [link]

Publications

Peer-reviewed conference publication (6 first author papers, 1 co-author paper, 2 under review) 

  • M. Adachi*, S. Hayakawa*, M. Jørgensen, H. Oberhauser, M.A. Osborne (2022). "Fast Bayesian inference with batch Bayesian quadrature via kernel recombination"Advances in Neural Information Processing Systems (NeurIPS) 35, 16533 - 16547. *: equal contribution, acceptance rate 25.6%
    [Paper] [arXiv] [OpenReview] [GitHub] [Video]

  • M. Adachi, S. Hayakawa, M. Jørgensen, X. Wan, H. Oberhauser, M.A. Osborne (2023). "Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach", Artificial Intelligence and Statistics (AISTATS) 27 [arXiv][GitHub]acceptance rate 27.6%

  • M. Adachi, B. Planden, D.A. Howey, Mi.A. Osborne, S. Orbell, N. Ares, K. Muandet, S.L.Chau (2023). "Looping in the Human: Collaborative and Explainable Bayesian Optimization", Artificial Intelligence and Statistics (AISTATS) 27 [arXiv][GitHub]acceptance rate 27.6%

  • M. Adachi, Y. Kuhn, B. Horstmann, A. Latz, M. A. Osborne, D. A. Howey (2023). "Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature"The 22nd World Congress of the International Federation of Automatic Control (IFAC) accepted, 
    [Journal (Open access)][arXiv] [GitHub]

  • J. Ziomek, M. Adachi, M.A. Osborne, "Beyond Lengthscales: No-regret Bayesian Optimisation With Unknown Hyperparameters Of Any Type", [arXiv]

  • M. Adachi, S. Hayakawa, S. Hamid, M. Jørgensen, H. Oberhauser, M.A. Osborne (2023). "SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces", [arXiv[GitHub]

  • M. Adachi (2021). High-Dimensional Discrete Bayesian Optimization with Self-Supervised Representation Learning for Data-Efficient Materials Exploration, 35th International Conference on Neural Information Processing Systems (NeurIPS) Workshop; AI for Science: Mind the Gaps (Poster)
    [OpenReview] [GitHub]

  • M. Adachi (2021). Mixture-of-Experts Ensemble with Hierarchical Deep Metric Learning for Spectroscopic Identification, 35th International Conference on Neural Information Processing Systems (NeurIPS) Workshop; Machine Learning and the Physical Science (Poster).
    [Paper] [GitHub]

  • M. Adachi, I. Budvytis, C. Ducati, R. Cipolla (2020). Physics-Aware Image-to-Image Translation to Explore Long-Life Solid-State Batteries, 34th International Conference on Neural Information Processing Systems (NeurIPS) Workshop; Machine Learning and the Physical Science (Poster).
    [Paper]

  • M. Adachi, H. Yamahara, M. Seki, H. Matsui, H. Tabata (2014), Terahertz magnonics using ultrathin films of samarium ferrite garnet, 39th IEEE International Conference on Infrared, Millimeter, and Terahertz waves (IRMMW-THz) (Oral).
    [Paper]

  • Y. Ohki, M. Adachi, M. Komatsu, M. Mizuno, K. Fukunaga (2013), Detection of polymer degradation and metal corrosion by terahertz imaging using a quantum cascade laser and a THz camera, 13th IEEE International Conference on Solid Dielectrics (ICSD) (Oral).
    [Paper]

​Peer-reviewed journal articles (4 first author papers, 5 co-author papers)

  • J. Schaeffer, P. Gasper, E. G. Tamayo, R. Gasper, M. Adachi, J. P. G. Cardona, S. M. Bedoya, A. Bhutani, A. Schiek, R. Goodall, R. Findeisen, R. D. Braatz, S. Engelke, (2023) "Machine learning benchmarks for the classification of equivalent circuit models from solid-state electrochemical impedance spectra." Journal of Electrochemical Society, 170, 060512
    [Journal (Open access)
    ] [arXiv] [GitHub]

  • H. Yamahara, B. Feng, M. Seki, M. Adachi, et al., (2021). Flexoelectric Nanodomains in Rare-Earth Iron Garnet Thin Films under Strain Gradient, Communications Materials 2, 95
    [Paper]

  • W. Xiaohan, J. Billaud, I. Jerjen, F. Marone, Y. Ishihara, M. Adachi, Y. Adachi, C. Villevieille, Y. Kato (2019). Operando Visualization of Morphological Dynamics in All-Solid-State Batteries, Advanced Energy Materials 9, 1901547
    [Paper] [ChemRxiv]

  • M. Adachi., M. Seki, H. Yamahara, H. Nasu, H. Tabata. (2015). Long-term potentiation of magnonic synapses by photocontrolled spin current mimicked in reentrant spin-glass garnet ferrite Lu3Fe5-2xCoxSixO12 thin films. Applied Physics Express 8,4, 043002-1-4.
    [Paper]

  • M. Adachi., H. Matsui, M. Seki, H. Yamahara, H. Tabata. (2015). High-temperature terahertz absorption band in rare-earth gallium garnet. Physical Review B 91, 085118.
    [Paper]

  • H. Yamahara, M. Seki, M, Adachi, M. Takahashi, H. Nasu, K. Horiba, H. Kumigashira, H. Tabata. (2015). Spin-glass behaviors in carrier polarity controlled Fe3-xTixO4 semiconductor thin films. Applied Physics 118, 6, 063905
    [Paper]

  • M. Adachi., H. Yamahara, S. Kawabe, H. Matsui, H. Tabata. (2014). Strong optical reflection of rare-earth garnets in the terahertz regime by reststrahlen bands. Physical Review B 89, 205124.
    [Paper]

  • M. Seki, M. Takahashi, M, Adachi, H. Yamahara, H. Tabata. (2014). Fabrication and characterization of wüstite-based epitaxial thin films: p-type wide-gap oxide semiconductors composed of abundant elements. Applied Physics Letters 105, 112105
    [Pape]

Conference Presentations

  • Oral Talks (4 international and 7 domestic conference)

  • Posters Presentation (3 international and 1 domestic conference)

Talks

Invited Talks

  1. 29.02.2024, LAM Research, CA, USA, hosted by Dr. Joe Lu.

  2. 24.03.2023, Machine Learning Group hosted by Prof. Motonobu Kanagawa, EURECOM, France
    Machine Learning for Science via Fast Bayesian Optimisation and Quadrature [Slide]

  3. 09.03.2023, Data Science and AI Research Group hosted by Dr. Festus Adedoyin, Bournemouth University, UK.
  4. 06.03.2023, Machine and Human Intelligence research group hosted by Prof. Luigi Acerbi, University of Helsinki, Finland.

  5. 08.02.2023, Battery Modelling Webinar Series (BMWS) hosted by Carnegie Mellon University, 08.02.2023 [Slide]

  6. 04.03.2022, Toyota Research institute

Oral & Poster presentations

  1. 26.04.2023, Advanced Battery Power Conference, Balancing accuracy and complexity when selecting battery models to fit data [Poster]

  2. 22.03.2023, ModVal 2023, Fast uncertainty prediction in digital twins of lithium-ion batteries via Bayesian quadrature [Poster]

  3. 30.12.2022, NeurIPS 2022, Fast Bayesian inference with Batch Bayesian Quadrature via Kernel Recombination [Slide]

  4. 14.03.2022, ModVal 2022, Bayesian Quadrature for Fast Parameter Estimation of a Lithium-ion Battery Model [Slide]

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職歴

データサイエンティスト
トヨタ自動車
​東富士研究所

先端電池のデータ解析、新材料発見の方法などを研究しています。

2018年4月-現在

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客員研究員
ケンブリッジ大学

2つの研究室に同時在籍していました。

  1. 工学部 機械知能研究所 (Host: Professor Roberto Cipolla)

  2. 材料科学冶金部 電子顕微鏡グループ (Host: Professor Caterina Ducati)

  研究協力者: Professor Clare Grey

2020年2月-2021年1月

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エネルギー研究者
トヨタ自動車
東富士研究所

​第4世代プリウス用リチウムイオン電池の研究開発をしていました。

2015年4月-2018年3月

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Image by Ben Seymour

学歴

Home: Education
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OCTOBER 2021 - PRESENT

機械学習博士課程(在籍中)
オックスフォード大学

クラレンドン特待生 (工学部首席入学)

テーマ: 非定常多腕バンディット問題としてのバッテリー制御最適化

共同指導教員: Professor Michael Osborne, Associate Professor David Howey

​所属研究室: Machine Learning Research Group & Battery Intelligence Laboratory

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APRIL 2013 - MARCH 2015

電気系工学専攻 修士号
東京大学

東京大学総長賞受賞(学業)

工学系研究科長賞(最優秀)

修士論文:フラストレート材料を用いた脳型コンピューティング

指導教員: 田畑 仁

​受賞歴

オックスフォード時代 (受賞数:3)

トヨタ時代 (受賞数:5)

  • トヨタ技術会最優秀発表賞, Toyota Engineering Society, 2019

  • トヨタ技術会東富士支部最優秀発表賞, Toyota Engineering Society, 2018

  • 最多発明賞, Toyota Motor Corporation, 2017

  • 青少年ボランティア表彰, Toyota City Council, 2016

  • 最多発明賞, Toyota Motor Corporation, 2016

東大時代 (受賞数: 6)​​

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特許

Patent Issued (5 Patents)

  • Electrolyte of non-polar solvent and bipolar salt LiBPh4 for lithium ion batteries, Issued Jun 9, 2021  Patent JP06895079

  • Fluoride ion conductor PbSnF2 coated negative electrode for five volt lithium-ion batteries, Issued Apr 21, 2021, EPP3547409, DE3547409, FR3547409, GB3547409, US3547409

  • high-power lithium-ion batteries using cyclopentyl methyl ether of electrolyte, Issued Nov 26, 2020, JP06799783

  • Mixed Zeta potential oxides coated cathode materials for high power lithium ion batteries, Issued Nov 4, 2020, EPP3547419, DE3547419, FR3547419, GB3547419, US3547419

  • Magnetic-phase-transitional porous metal-complex nanocoils for high-power lithium-ion batteries, Issued Jun 14, 2019, JP06536908

Patent Filed (8 Patents)

  • Algorithm to efficiently discover the desired materials, Aug 2021

  • Algorithm to support the chemical elemental composition inference from crystal structural spectra, Aug 2021

  • Algorithm to support the new material discovery from spectral data mining, Aug 2021

  • Automatic spectral identification of materials using hierarchical deep metric learning, Aug 2021

  • Automatic inspection of hue/texture anomaly for car leathers based on camera images, Mar 2021

  • Nano-porous micro-structured silicide alloy framework for high-capacity lithium-ion solid-state batteries, Filed Mar 23, 2021, 202100588JP00

  • Novel cathode material Bi2FeCoO3F6 for high-capacity fluorine-ion batteries, Filed Sep 6, 2019, 201904863JP00

  • Si - NiTi nano-composite sub-nanoparticles for high-capacity lithium-ion solid-state batteries, Filed Feb 7, 2018, 180892JP

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