Curriculum vitae

PhD in Physics (Starting Soon)
University of Massachusetts Lowell
Specializing in machine learning applications in astrophysics and gravitational wave detection.
Master of Science in Physics (2023 – 2026)
University of Washington – Seattle, WA
Coursework: Quantum Mechanics, Electromagnetism, Machine Learning for Signals in Engineering
Bachelor of Science in Physics (2012)
University of Florida – Gainesville, FL

Graduate Research – GWGASF Collaboration
University of Washington (2023 – Present)
- Developing deep learning models for gravitational wave detection and anomaly suppression in LIGO data
- Working with S3 object storage, Kubernetes, and distributed computing environments for large-scale data processing
- Special focus on time-series analysis, signal classification, and noise mitigation in astrophysical datasets
Researcher – He6-CRES Experiment
University of Washington (2023 – 2024)
- Contributed to high-precision spectroscopy for measuring neutrino mass via cyclotron radiation emission spectroscopy
- Involved in simulation development and calibration of RF detection systems
Research Assistant – National High Magnetic Field Laboratory (NHMFL)
Tallahassee, FL (2021)
- Performed advanced fluorescence microscopy and material characterization
- Conducted spectroscopic measurements on biological and inorganic samples under high magnetic fields

Project Technician II
Amazon Project Kuiper (March 2025 – Present)
- Supporting integration, testing, and validation of satellite subsystems
- Assisting with lab infrastructure, data acquisition systems, and hardware-software interface debugging
Materials, Process and Physics Engineer I
Boeing Research & Technology (April 2024 – January 2025)
- Led nondestructive evaluation (NDE) efforts on composite and metallic aerospace structures
- Utilized ultrasonic, radiographic, and Barkhausen noise inspection to support quality assurance
- Created engineering reports and performed failure analysis using both manual and digital tools
Materials, Process and Physics Technical Analyst
Boeing Research & Technology (September 2023 – April 2024)
- Supported engineering teams with data analysis and inspection planning
- Assisted in the evaluation of production process controls and defect classification

Programming & Tools: Python, MATLAB, Git, Docker, Kubernetes, Bash, Linux, Conda
Machine Learning: PyTorch, TensorFlow, Scikit-learn, HDF5, deep neural networks
Scientific Computing: S3-compatible storage, HPC clusters, data pipelines
Nondestructive Evaluation (NDE): Ultrasonic Testing (UT), Radiographic Film/CR, Barkhausen analysis
Instrumentation & Hardware: Oscilloscopes, transducers, RF signal generators, cryogenics