Hi, I'm Daniel!

I'm a Ph.D. candidate specializing in nuclear fusion, plasma physics, and scientific computing.
My research is focused on magnetohydrodynamic (MHD) stability of tokamaks, building open source simulation tools, and validating instability theory with experimental data.

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Select Publications

Select Projects

GPEC (General Plasma Equilibrium Code)

Fortran

Redeveloped and expanded SLAYER to include updated physics and quadtree adaptive mesh refinement (AMR) for robust calculation of both uncoupled and coupled classical tearing mode growth rates. AMR approach achieved over 20x speedup in root finding procedure.

Resume

Education

Columbia University
Ph.D. Candidate, Plasma Physics
2022 — Present
Columbia University
B.A. Astrophysics
2018 — 2022

Technical Skills

PythonFortranBash/UnixLaTeXEFITGPECTokaMakerFreeGSPyTorchGit

Select Talks & Posters

Intl. Cosmic Ray Conf.
2021

Research Experience

Columbia Plasma Physics Laboratory

Sep 2022 — Present
Ph.D. Student New York, NY
  • Redeveloped and benchmarked SLAYER code using Fortran for inclusion in the GPEC suite and analysis of experimental tearing mode discharges.
  • Designed and assessed operational and contingency scenarios for the SPARC tokamak utilizing kinetic equilibria generated in EFIT and FreeGS.

DIII-D National Fusion Facility

Jun 2022 — Aug 2022
SULI Intern San Diego, CA
  • Analyzed novel multi-machine, ELM-free regime database to assess regime suitability for future reactors.
  • Generated and analyzed kinetic equilibria and peeling-ballooning boundary calculations to analyze stability effects on ELM onset.

Lamont Doherty Earth Observatory

May 2021 — May 2022
Research Assistant New York, NY
  • Developed a modular, fault-tolerant software package to simulate atmospheric radiative transfer.
  • Vectorized and parallelized functions using Xarray and Dask to reduce calculation time from hours to seconds with minimal accuracy loss.

NuSTAR Team, Columbia Astrophysics Lab

Sep 2019 — May 2022
Research Assistant New York, NY
  • Led collaboration between research professors and observatory teams to model a neutron star's high-energy gamma-ray emission.
  • Analyzed 100+ X-ray telescope datasets (NuSTAR, XMM-Newton) using Python and Unix-based tools.

Teaching & Mentorship

Research Mentor

2025 — Present

Mentored Kevin Clavijo in development of tokamak pedestal scaling routines for edge-localized mode stability analysis.

Teaching Assistant

2020 — 2023
Columbia University

TA for APPH E4101 (Dynamical Systems), APPH E4100 (Quantum Physics), and ASTR W2001 (Intro to Astrophysics). Graded coursework and led student review sessions.

Writing

GlacierHub

2021
Staff Writer

Authored science communication pieces on climate science: Apr 19, May 5, Jul 9, Aug 6, and Sep 1.

Daniel Burgess

About Me

I am currently a Ph.D. candidate at Columbia University, where I explore the intersection of plasma physics and high-performance computing. I joined the program in Fall 2022 after receiving my B.A. in astrophysics, also from Columbia. My research focuses on the magnetohydrodynamic (MHD) stability of tokamak plasmas to tearing modes, modeling of scenarios and control for the SPARC and ARC tokamaks, and development of open source tools such as TokaMaker and the GPEC suite.

I'm excited to lead efforts that bridge fusion theory and experiment through rigorous validation and handling of measurement uncertainties, and I'm additionally passionate about mentoring the next generation of high school and undergraduate students. In my free time I enjoy running, skiing, gravel cycling, and playing guitar with friends or strangers.