Experience

Climatebase

• United States
• Technology, Information and Internet
• 100 - 200 Employee

• Climatebase Fellow

  • Sep 2023 - Present

  Participating in Cohort 4 of the Climatebase Fellowship, a selective accelerator program providing education and project support for future climate technologists. Data scientist for "Down the Block", a capstone project developing a co-op carshare model in Madison, WI; geospatial analysis (geopandas, folium), market landscape analysis. Member of the ad hoc data science team for a capstone project to appraise housing with decarbonizing retrofits; web scraping, data cleaning, structured… Show more Participating in Cohort 4 of the Climatebase Fellowship, a selective accelerator program providing education and project support for future climate technologists. Data scientist for "Down the Block", a capstone project developing a co-op carshare model in Madison, WI; geospatial analysis (geopandas, folium), market landscape analysis. Member of the ad hoc data science team for a capstone project to appraise housing with decarbonizing retrofits; web scraping, data cleaning, structured data analysis, and ML model development. Independent study in computer vision, remote sensing, and applications for supply chain resilience and land use analysis; synthetic aperture radar (SAR), geospatial analysis Show less



Kavli Institute for Theoretical Physics

• United States
• Research Services
• 1 - 100 Employee

• Postdoctoral Fellow

  • Sep 2020 - Sep 2023

  Santa Barbara, California, United States Developed C++ and Fortran-based software for predictive modeling of galaxy evolution and conducted time-series and image analysis to extract publishable insights from structured and unstructured data. Highlights • Engineered a Python and PyTorch-based pipeline to process unstructured simulation data and extract insights using deep CNN and U-net architectures (PyTorch, Python, Scikit-learn, SciPy, NumPy, Jupyter, Plotly, git). Showed that properties of cosmic rays can be inferred by… Show more Developed C++ and Fortran-based software for predictive modeling of galaxy evolution and conducted time-series and image analysis to extract publishable insights from structured and unstructured data. Highlights • Engineered a Python and PyTorch-based pipeline to process unstructured simulation data and extract insights using deep CNN and U-net architectures (PyTorch, Python, Scikit-learn, SciPy, NumPy, Jupyter, Plotly, git). Showed that properties of cosmic rays can be inferred by neural networks when given solely images of gas density, with classification accuracy, precision, and recall > 90% (Bustard and Wu 2023, submitted to Machine Learning Science and Technology) • Analyzed and visualized time-series data resulting from > 4 million CPU hours of simulations, 10 TB of output data, leading to novel insights on the interactions between cosmic rays and turbulence (python, numpy, scipy, matplotlib, astropy, pandas) • Awarded and managed > 15 million CPU hours of compute time supporting multiple team members by leading a successful proposal and performing code timing and scaling tests on 4 national supercomputers • Published 4 first-author and 3 co-authored papers related to multiphase gas in galaxy halos and the effects of cosmic rays on galaxy evolution • Attended a 10-week program on Galaxy Formation and Evolution in the Data Science Era, filled with lectures, tutorials, and informal discussions on e.g. CNNs, graph neural networks (GNNs), JAX, and emerging generative models Show less



University of Wisconsin-Madison

• Higher Education
• 700 & Above Employee

• NSF Graduate Research Fellow

  • Jun 2014 - Sep 2020

  Madison, Wisconsin, United States Incorporated numerical algorithms with image analysis to study cosmic ray effects on galaxies. Ph.D. in Physics awarded in 2020 with specialization in computational plasma astrophysics. Highlights: • Led 4 cross-disciplinary research projects, utilizing multiple tech stacks to model galaxy evolution and find publishable insights from TBs of simulation data. Resulted in 4 first-author and 3 co-authored papers in the Astrophysical Journal and Nature. • Developed new modules and… Show more Incorporated numerical algorithms with image analysis to study cosmic ray effects on galaxies. Ph.D. in Physics awarded in 2020 with specialization in computational plasma astrophysics. Highlights: • Led 4 cross-disciplinary research projects, utilizing multiple tech stacks to model galaxy evolution and find publishable insights from TBs of simulation data. Resulted in 4 first-author and 3 co-authored papers in the Astrophysical Journal and Nature. • Developed new modules and algorithms for gas cooling and semi-stochastic particle formation, and integrated them with a broader fluid dynamics code to simulate dwarf galaxy evolution • Added functionality to an existing C++ fluid dynamics module for ionization-dependent cosmic ray propagation. Rigorously tested implementation against analytic theory • Advised other researchers on numerical algorithms, high performance computing, and software best practices • Awarded an NSF Graduate Research Fellowship for 3 years of fully-funded research and tuition • Served as a teaching assistant for introductory modern physics (> 100 students) • Mentored 5 junior researchers through the Madison Metropolitan School District, UW-Madison Undergraduate Research Scholars program, and the UW-Madison Physics and Astronomy departments. I also completed specialized courses on academic advising and teaching with technology. Awards: NSF Graduate Fellowship, Jansky Award, Roger Doxsley Travel Prize Show less



Los Alamos National Laboratory

• United States
• Research Services
• 700 & Above Employee

• Post-Baccalaureate Researcher

  • Jun 2013 - May 2014

  Los Alamos, New Mexico, United States I modeled inertial confinement fusion and it's resulting energy yield with massively parallel fluid dynamics simulations Highlights: • Collaborated with scientists across multiple internal groups and presented findings at a cross-divisional seminar • Gained valuable exposure to parallel computing architectures, code parallelization with MPI and OpenMP, and code optimization and timing



Sandia National Laboratories

• United States
• Defense and Space Manufacturing
• 700 & Above Employee

• Student Intern

  • May 2010 - May 2011

  Albuquerque, New Mexico, United States Modeled metamaterials and optical "tweezers" for various applications in photonics and optics Highlights: • Collaborated with a university graduate student to implement a C++-based code for Sandia Labs purposes • Compared results to laboratory fabrications • Presented results to senior leadership • Contributed analysis to a conference proceedings report