Curriculum Vitae

Ph.D. in Physics

Princeton University, Princeton, New Jersey, United States

• Advisor: Jo Dunkley
• Research interests: Cosmic Microwave Background, component separation, diffuse Galactic foregrounds, statistical inference, Monte Carlo simulation, large-scale data analysis, and harmonic-space analysis.

M.A. in Physics

Princeton University, Princeton, New Jersey, United States

B.S. in Applied Mathematics & Statistics, Physics, and Mathematics

Johns Hopkins University, Baltimore, Maryland, United States

• GPA: 3.99 / 4.00 (General Honors and Departmental Honors in all majors)
• Minor: Computer Science

• Donald E. Kerr Memorial Award, JHU top 2 graduating seniors majoring in physics
• Applied Math and Statistics Achievement Award, JHU top 6 graduating seniors majoring in applied math and statistics

• Sigma Pi Sigma Physics Honors Society
• Provost's Undergraduate Research Award, JHU funded research support

• Bloomberg Distinguished Professor Research Fellowship x2

• Dean's List

Graduate Researcher | Simons Observatory

Princeton, NJ | Mentors: Jo Dunkley, Susanna Azzoni

• Developed per-scale regression-based component separation algorithms to extract weak cosmological signals from noisy high-dimensional data.
• Investigated non-Gaussian statistics for next-generation foreground cleaning.
• Demonstrated that dust complexity biases cosmological inference using MCMC and Bayesian analysis.
• Quantified robustness of SO pipelines under various foreground complexity scenarios.
• Implemented pipelines for SO x Planck cross-correlation to validate instrument performance.

Undergraduate Researcher | Cosmology Large Angular Scale Surveyor

Baltimore, MD | Mentors: Charles L. Bennett, Tobias A. Marriage, Ivan L. Padilla

• Implemented and optimized a minimum-variance estimator (Needlet Internal Linear Combination) for multi-frequency signal decomposition.
• Built polarization-based foreground masks and benchmarked estimator performance across frequency bands.
• Developed and tested point-source identification algorithms using Fourier-space filtering techniques.
• Created Python routines to detect temporal anomalies via rolling-dispersion diagnostics in time-series data.

Undergraduate Researcher | Zakamska Astrophysics Group

Baltimore, MD | Mentors: Nadia L. Zakamska, Hsiang-Chih Hwang

• First-authored a publication modeling complex binary star systems using spectral decomposition and time-series fitting.
• Automated a Python-based photometric and spectral analysis pipeline for feature extraction in both time and frequency domains.

Simons Observatory: Assessing and Modeling Foreground Complexity in CMB Analyses

2026 American Physics Society Global Physics Summit, Denver, CO

The impact of dust complexity on the recovery of r

Pan-Experiment Galactic Science Group, Los Angeles, CA

From the Impact of Dust Complexity to Beyond

2025 SO Collaboration Meeting, Philadelphia, PA

Understanding the impact of dust complexity on the recovery of the tensor-to-scalar ratio

246th Meeting of the American Astronomical Society, Anchorage, AK

Understanding the impact of dust complexity on bias in r

2024 SO Collaboration Meeting, Chicago, IL

CMB analysis using the global-NILC method

2023 American Physics Society March Meeting, Las Vegas, NV

CSS1603+19: a low mass polar at the cataclysmic variable period minimum

241st Meeting of the American Astronomical Society, Seattle, WA

Teaching Assistant, EN.553.430/630 Intro to Statistics

Ugrad and Grad, 82 students

• Graduate-level introductory statistics course covering stochastic convergence, point estimation, hypothesis testing, and interval estimation.

Teaching Assistant, EN.553.633 Monte Carlo Method

Grad, 60 students

• Graduate-level course covering pseudo-random number generators and classic Monte Carlo simulation algorithms such as Metropolis-Hastings.

Teaching Assistant, AS.171.103 General Physics I

Ugrad, 23 students over 2 semesters

• Undergraduate-level introductory physics on classical mechanics.