Electric Power to the People

​Energy/Data Scientist at Oxford | Toyota

About Me

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Masaki Adachi (足立 真輝)🇯🇵🇬🇧

I am a first-year DPhil student in Engineering Science (Information Engineering) in the Bayesian Exploration LabMachine Learning Reading Group at the University of Oxford under primary supervision of Prof Michael Osborne. My secondary advisor is Prof David Howey from Battery Intelligence Lab. I am also a Clarendon Scholar. Simultaneously, I belong to Toyota Motor Corporation. My research interest is in the interdisciplinary field between machine learning and energy science, applying Bayesian machine learning to the proliferation of electric vehicles and renewable energy. I wish to create solid motivations for renewables introduction not for the "forced" reasons of legislation and political demands but to provide economic benefits.

Research Interests

  • ​Gaussian Process, Bayesian Quadrature, Bayesian Optimization

  • Reinforcement Learning

  • Battery modeling (Doyle-Fuller-Newman model), Phase-field modelling

​Links

​​Content

​Click the upper button to scroll down. 上部[EN]ボタンをJAに変更で日本語になります

  • Working Experiences

  • Academic Background

  • Awards

  • ​Publication & Patents

SNS

Affiliation

  • ​Bayesian Exploration Lab: website

  • Machine Learning Research Group: website, GitHub

  • Battery Intelligence Lab: website

  • Oxford Man Institute of Quantitative Finance: website

  • Toyota Motor Corporation: website

  • Clarendon Scholar's Association: website

  • Department of Engineering Science: website

  • St. Catherine's College: website

 

Recent News

[08.10.2022] I was awarded for a NeurIPS 2022 Scholar Award.
[15.09.2022] Our paper has been accepted at NeurIPS 2022. "Fast Bayesian inference with batch Bayesian quadrature via kernel recombination
".

[22.06.2022] BatteryDEV was featured by German Publisher ZEIT für Klima, broadcasted on YouTube [link]

[09.06.2022] A new preprint "Fast Bayesian inference with batch Bayesian quadrature via kernel recombination" has been published.

[20.03.2022] Organised the international battery data science hackathon event as one of organisers. https://www.battery.dev

[14.03.2022] Oral presentation at ModVal18 with the title of "Bayesian Quadrature for Fast Parameter Estimation of a Lithium-ion Battery" https://modval2022.welcome-manager.de

[04.03.2022] Invited talk at Toyota Research Institute at California https://www.tri.global

[14.12.2021] Poster presentation at NeurIPS workshop AI for Science https://openreview.net/forum?id=xJhjehqjQeB

  [13.12.2021] Poster presentation at NeurIPS workshop Machine Learning and the Physical Sciences https://ml4physicalsciences.github.io/2021/

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Working Experiences

Data Scientist
Toyota Motor Corporation

Data-driven design of next-generation batteries and motors for revolutionising electric vehicles:

  • intrapreneur based on intelligent experimental data analytics service

  • Machine learning analysis of manufacturing & experimental data (Python, R)

  • Deep learning model development (Pytorch)

  • Research on next-generation batteries, including materials discovery of new materials using machine learning

APRIL 2018 - PRESENT

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​Embedded Researcher

University of Cambridge

Belonging to two laboratories at the same time:

  1. Machine Intelligence Laboratory, Department of Engineering (Host: Professor Roberto Cipolla)

  2. Electron Microscope Laboratory, Department of Materials Science & Metallurgy (Host: Professor Caterina Ducati)

Collaborating with Professor Clare Grey

FEBRUARY 2020 - JANUARY 2021

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Energy Scientist
Toyota Motor Corporation

Simulation-Based Design of Lithium-Ion Battery for Hybrid Vehicles (4th Prius)

  • Development of simulation software

  • Robust designing of commercial lithium ion batteries (HV, PHV, and EV)

  • Experimental skills of lithium ion batteries (fabrication & measurements)

APRIL 2015 - MARCH 2018

 
Image by Ben Seymour

Education

 
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OCTOBER 2021 - PRESENT

DPhil in Machine Learning
University of Oxford

Clarendon Scholar (the crème de la crème of the incoming cohort)

Thesis: Fast Bayesian Inference with Quadrature for Battery Control

Primary supervisor: Professor Michael Osborne,

Secondary supervisor: Professor David Howey

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APRIL 2013 - MARCH 2015

MEng in Electronic Engineering
University of Tokyo

Summa cum laude (ranked 1st in the cohort of 3,074 MEng graduates)

Department of Electrical Engineering and Information Systems,

Thesis title: Neuromorphic computing using frustrated magnetic thin films

Awarded President’s Prize (the best master’s thesis in the cohort)

​Superviser: Professor Hitoshi Tabata

Awards

Oxford Period (5 awards)

​Toyota Period (5 awards)

  • Best Research Award, Toyota Engineering Society, 2019

  • ​Best Research Award, Toyota Engineering Society, 2018

  • Most Invented Prize, Toyota Motor Corporation, 2017

  • Outstanding Youth Commendation, Toyota City Council, 2016

  • Most Invented Prize, Toyota Motor Corporation, 2016

Univ. Tokyo Period (6 awards)​​

 

Publications

​Peer-reviewed journal articles (3 first author papers, 3 co-author papers, 1 under review)

Refereed conference publication (5 first author papers, 1 co-author paper) 

Conference Presentations

  • Oral Talks (4 international and 7 domestic conference)

  • Posters Presentation (3 international and 1 domestic conference)

 

Patent

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

 

Organisation

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