Experience

  1. Visiting Assistant in Research

    Department of Physics, Yale University
    • Developed hierarchical Bayesian models for multi-modal astrophysical datasets, improving population predictions.
    • Led cross-functional collaboration with 100+ scientists; results published in top-tier journal.
    • Leveraged survival analysis techniques to analyze noisy, incomplete datasets, producing actionable insights.

  2. Graduate Assistant

    Department of Physics, University of Connecticut
    • Designed hierarchical Bayesian models to enhance predictive analytics using multi-modal data; methods published in a high-impact journal.
    • Optimized predictive model efficiency by 300× using Hamiltonian Monte Carlo, reducing computational costs significantly.
    • Secured $8,000 NASA Space Grant Fellowship based on innovative research proposals.

  3. Graduate Research and Teaching Associate

    Department of Physics and Astronomy, San José State University
    • Built convolutional neural network pipelines for galaxy classification across large datasets.
    • Developed Bayesian analysis techniques for predictive spatial mapping of astronomical observations.

  4. Software Engineer

    Salient Process, Inc.
    • Led development of SPARK UI toolkit (acquired by IBM), showcasing practical experience in software engineering.
    • Pioneered Git-based version control for streamlined software management.
    • Designed and maintained software tools, improving productivity and quality of deliverables.

Education

  1. Ph.D. Physics

    University of Connecticut
    I developed statistical and computational models to analyze the nanohertz gravitational wave background (GWB) and its connection to supermassive black hole binaries (SMBHBs) using observational data. I found SMBHBs may be eight times more prevalent than expected, quasars up to seven times more likely to host SMBHBs, and identified a 16 nHz anomaly in the GWB spectrum (∼ 2σ confidence). I demonstrated expertise in data modeling, statistical inference, and analyzing complex datasets.

  2. M.S. Physics

    San José State University

    Developed a computational model using a leapfrog integration scheme to simulate the orbit of dense molecular gas around our galaxy’s central supermassive black hole (SMBH). Built a Bayesian analysis pipeline to fit the model to observational data, estimating the gas passes within ∼ 5 lightyears of the SMBH. Demonstrated expertise in algorithm development, statistical modeling, and data analysis.

    Selected coursework:

    • Data Analysis & Machine Learning in Astronomy
    • Statistical & Machine Learning Classification
    • Deep Learning

  3. B.S. Physics

    University of California, Davis
Skills & Hobbies
Programming
Python
SQL
Java
C++
C
Data Science & Machine Learning
Predictive Modeling
Bayesian Inference
Regression Analysis
Neural Networks
Causal Inference
Tools
Scikit-learn
Keras
TensorFlow
Git
Jupyter
Awards
NASA Connecticut Space Grant Graduate Research Fellowship
NASA Connecticut Space Grant Consortium ∙ May 2021

I was awareded an $8,000 NASA research fellowship, during which I developed a predictive model to analyze complex astrophysical data, focusing on quasar-based modeling of supermassive black hole binary populations. By integrating observational data from NANOGrav’s 12.5-year dataset with advanced statistical techniques, I quantified the local number density of these binaries, uncovering insights that challenged previous predictions based on galaxy merger simulations (e.g., ILLUSTRIS). This work required expertise in data modeling, statistical analysis, and designing Bayesian analysis frameworks, as well as deriving actionable insights from noisy, high-dimensional datasets.

This experience honed my ability to build robust, data-driven models and uncover critical patterns, skills I am eager to apply to complex, real-world challenges in industry.