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General Information

Full Name Timothy Lucas Makinen
Date of Birth 26th October 1997
Languages English, French, Finnish, Italian

Education

  • 2024
    PhD
    Imperial College London
  • 2021
    Master's
    Sorbonne University & Institut d'Astrophysique, Paris
  • 2021
    B.A. Astrophysics
    Princeton University, United States
    • Minors: Applied & Computational Mathematics, Statistics & Machine Learning
    • Royal College of Music Exchange Programme (2018-19)

Experience

  • 2021 - 2024
    PhD Candidate
    Department of Physics, Imperial College London
  • 2021-2022
    Predoctoral Researcher
    Center for Astrophysics | Harvard & Smithsonian
  • 2021
    Postgraduate Researcher
    Scuola Internazionale di Studi Avanzati, Trieste Italy
  • 2020-2021
    Master's Internship
    Institut d'Asrophysique de Paris
  • 2020-2021
    Data Scientist
    Center for Evolutionary Hologenomics, University of Copenhagen
  • 2019-2020
    Student Researcher
    Flatiron Institute & Princeton University
  • 2020
    Summer Researcher
    DAWN Institute, University of Copenhagen
  • 2018-2019
    Student Researcher
    Imperial College & Cambridge University
  • 2018
    Summer Research Intern
    Institut de Génetique Moléculaire de Montpellier (CNRS)
  • 2017
    Student Researcher
    Department of Astrophysics, Princeton University

Honors and Awards

  • 2021
    • Imperial College London President's Scholarship
  • 2020
    • Sorbonne University Master's Scholarship
    • Honor Graduate, Princeton University
  • 2019
    • Streicker International Fellowship
    • APS Outstanding Undergraduate Presentation
  • 2018
    • Office of International Programs Fellowship

Academic Interests

  • Large Scale Structure Formation.
    • How did large-scale structures form from initial conditions? What is the nature of Dark Matter?
    • What cosmological information is trapped in structure?
  • Statistical Analysis.
    • How can we compress massive datasets for robust statistical analysis
    • How can we optimize expensive simulations and sampling?
  • Deep Learning for Physics.
    • How can we use an entropy metric to guide hierarchical, graph-based learning?
    • Where do neural methods fail, and why?

Other Interests

  • Hobbies: Jazz & Classical Trumpet, languages & linguistics