About 95% of stars in the Universe will eventually become white dwarfs,
including the Sun. Understanding these stars and their planetary
systems is crucial for understanding the physics of stellar evolution
and opens a window into the future of our solar system.
I am broadly interested in the study of white dwarf pollution, binaries,
accretion, and the evolution of their planetary systems:
Pham & Majeski et al. (2026):
observational constraints on the properties of the magnitude and number of velocity kicks as the result of episodic mass loss episodes in white dwarf progenitors
Majeski & Pham et al. (2026):
a Fokker-Planck theory for the orbital evolution with predictions on white dwarf binaries separation and eccentricity
Pham, Taylor & Cunningham (2026):
a magnetic accretion model onto white dwarfs, with observation predictions such as undetected magnetic patches on white dwarfs might understimate accretion rates
Pham & Rein (2024):
the dynamics of exo-Oort clouds driving persistent white dwarf pollution matching observed accretion rates
Statistical Methods
I am interested in applying statistical methods and machine learning
to astrophysical problems:
Poon & Pham et al. (2026)
a hierarchical Bayesian inference method to analyze the spin-orbit alignment of visual binary stars
Poon et al. (2025):
Bayesian inference on the current ensemble of observed exoplanet obliquity to understand formation mechanisms
Pham & Kaltenegger (2022):
machine learning and statistical methods to optimize filters for future missions to detect exoplanetary surface water
Pham & Kaltenegger (2021):
comparing machine learning methods and noise injection affects the characterization of Earth-like planet surface properties
I am interested in all areas of planetary dynamics:
Majeski & Pham et al. (2026):
a Fokker-Planck theory for the orbital evolution of white dwarf planetary systems after episodic mass loss and velocity recoil kicks
Pham & Rein (2024):
dynamics of Oort cloud objects under the effects of the Galactic tide and a massive companion (stellar or planetary), modifying the shielded loss cone with energy perturbations from the companion
Numerical Methods
Many problems require numerical simulations. I am interested in
improving numerical methods and ensuring their accuracy.
I have worked in developing a new adaptive timestepping criterion
(Pham, Rein, & Spiegel, 2024),
a fast dynamical integration technique for small bodies around binary systems
(Pham & Rein, 2024),
and a new long-term comet evolution code that fully couples dynamical,
stellar, thermal, and volatiles evolution.
Small Bodies & Fast Radio Bursts
Fast radio bursts (FRBs) are millisecond radio events.
Their physical origin remains unknown. I am interested in the possibility
that these FRBs are caused by small bodies (asteroids, comets) impacting neutron stars.
I have modeled the dynamics of these small bodies around neutron stars,
and their collision rates, and made some observational predictions for this scenario
(Pham, Hopkins et al., 2024).
Simulation & Atmosphere Removal for the TIME Collaboration
TIME (Tomographic Ionized Carbon Intensity Mapping Experiment)
is an [CII]/CO line-intensity mapping instrument seeking
to understand the epoch of reionization.
Having an understanding of the epoch of reionization remains a
central task in building a complete picture of the Universe's evolution,
early galaxies formation, and star formation history. In this project,
I am mostly involved in building a simulation pipeline and perform
atmosphere removal for TIME observations
(Butler et al., 2024, Yang et al., 2025, Vaughan et al., 2026).