CV

Personal Information

Full Name Jun Yamamoto
Languages Japanese, English
Email jun.j.yamamoto_at_gmail.com (replace `_at_` with `@`)

Academic Interests

  • Complex Networks
    • physical networks
    • critical phenomena on complex networks
    • fractal and multifractal properties
    • graph spectra
    • networks embedded in metric spaces
  • Statistical Physics
    • phase transitions, critical phenomena, disordered systems

Education

  • 2023/09 - Present
    PhD in Network Science
    Department of Network and Data Science, Central European University (Vienna, Austria)
  • 2022/09 - 2023/09
    MSc in Mathematics
    School of Mathematical Sciences, Queen Mary University of London (London, UK)
    • 96/100, distinction.
    • Modelling higher-order network dynamics in the presence of triadic interactions
      • Supervisor -- Prof. Dr. Ginestra Bianconi
      • Investigated the node dynamics on networks with triadic interactions, in which a node can regulate positively/negatively the interaction between two other nodes.
      • Showed that the triadic interactions result in nontrivial behaviours of conditional correlation coefficients between the node states and that the triadic interactions in real networks may be inferred from the conditional correlation coefficients.
  • 2017/04 - 2022/03
    BEng in Applied Physics
    School of Engineering, Hokkaido University (Sapporo, Japan)
    • 4.18/4.30. Nitobe College Summa Cum Laude.
    • Bifractality of fractal scale-free networks
      • Supervisor -- Prof. Dr. Kousuke Yakubo
      • Investigated analytically and numerically the multifractal property of fractal scale-free networks (FSFNs) generated by deterministic hierarchical, stochastic hierarchical, and non-hierarchical models.
      • Showed that all of them are bifractal and that the two local fractal dimensions implied by bifractality correspond to two types of substructures, one near the infinitely high-degree hubs and the other near finite-degree nodes that are infinitely distant from the infinitely high-degree hubs.
  • 2019/09 - 2020/05
    Exchange programme
    Department of Physics, ETH Zurich (Zurich, Switzerland)
    • Took courses in computational physics, superconductivity, and quantum information theory.
    • Discontinued due to the COVID-19 pandemic.

Employment

  • 2022/07 - 2022/09
    Data Scientist
    Fujitsu Limited (Tokyo, Japan)
    • Analysed large-scale datasets of newspaper articles using natural language processing and network analysis.
    • Quantified the trends of markets by employing the dynamic topic model and developed a software that visualises the correlation between the trending topics/keywords and economic indices.
    • Analysed the topology of collocation networks of keywords in the news articles and the bipartite networks of keywords and newspaper articles.
    • Developed a portfolio in which the collocation networks are visualised and used to recommend related news articles or keywords.

Awards and Scholarships

  • 2023/11
    Principal's Prize, Queen Mary University of London
    • Awarded for outstanding academic achievements at School of Mathematical Sciences.
  • 2022/09 - 2023/09
    Scholarship, Ito Foundation for International Education Exchange
    • Awarded ¥3,000,000 for the tuition fee, \$2,000/month for the duration of the study, and flight fees
  • 2022/03
    Dean's Award for Academic Achievement, School of Engineering, Hokkaido University
    • Awarded to students with outstanding academic achievements at School of Engineering. (one of the 14 recipients in 2022).
  • 2019/09 - 2020/05
    Scholarship, Japan Student Service Organization
    • Awarded ¥80,000/month for the duration of the exchange programme (discontinued due to the COVID-19 pandemic).
  • 2019/07
    Lane Memorial Award, Hokkaido University
    • Awarded to the eight students with outstanding grades in English in the first and second years of undergraudate studies.
  • 2018/07
    Nitobe Award, Hokkaido University
    • Awarded to the best student at each school by GPA of the first year.
  • 2018/06
    Nitobe College Incentive Award, Nitobe College, Hokkaido University
    • Awarded to the students with outstanding academic achievements among Nitobe College students.

Teaching Experience

  • 2022/04 - 2022/08
    Teaching Assistant
    School of Engineering, Hokkaido University (Sapporo, Japan)
    • Applied Mathematics II
      • Assisted the lecturer in the course "Applied Mathematics II" for second-year undergraduate students.
      • Held tutorial sessions and answered questions from students.
      • Graded the students' reports.
    • Statistical Mechanics I
      • Assisted the lecturer in the course "Statistical Mechanics I" for third-year undergraduate students.
      • Held tutorial sessions and answered questions from students.
      • Graded the students' reports.
  • 2021/04 - 2022/03
    Teaching Assistant
    Education and Research Center for Mathematics and Data Sciences, Hokkaido University (Sapporo, Japan)
    • Computational Science
      • Assisted the lecturer in the course "Computational Science" for third-year undergraduate students.
      • Prepared materials for the lectures on stochastic simulations (random number generators, Monte Carlo simulation, percolation, 2D Ising model).
    • MDS/AI Seminar
      • Assisted the lecturer in the online course "MDS/AI Seminar" accessible to all Hokkaido University students.
      • Prepared materials for and gave the lecture on descriptive statistics and linear regression.
      • Held office hours and answered questions from students.

Internships

  • 2020/07 - 2020/08
    Summer Research Intern
    Center for Computational Science and e-Systems, Japan Atomic Energy Agency (Chiba, Japan)
    • Application of machine learning to accelerate molecular dynamics simulation
      • Implement machine learning and deep learning algorithms in Julia lang.
      • Learn hands on how to use machine learning to accelerate molecular dynamics simulation.
  • 2019/02 - 2019/03
    Research Intern
    Quantum Wave Microscopy Unit, Okinawa Institute of Science and Technology (Okinawa, Japan)
    • Observation of protein nanocrystals using diffraction electron microscope
      • Learn hands on how to operate scanning electron microscope and transmission electron microscope.
      • Tested the feasibility of using a diffraction electron microscope which was under development at the lab to observe protein nanocrystals.