I am expecting to graduate with a PhD in Applied and Computational Mathematics from Southern Methodist University in May, 2023. My advisor is Daniel Reynolds and I am currently funded by FASTMath SciDAC. My research is focused on adaptivity in multirate numerical IVP-solving methods.
I am passionate about mathematics and software development with experience in industry, academia, and national labs.
I am currently looking to start my career in data science, machine learning, and computational mathematics!
A Chess AI With Poor Eyesight
Oh, no! The computer lost its glasses! This chess AI can only recognize the location and color of pieces on board, and was trained with a deep-learning model on a database of millions of games played by real people to be able to guess the correct board state when it makes a move. See the Github (link) or just the report (link).
Keywords: data mining, feature engineering, classification, class imbalance, deep learning, PyTorch, parameter tuning, ensemble modeling, model deployment, dashboard
Multi-Location/Multi-Product Demand Forecasting
Forecasting demand of a variety of products for branches of a chain store scattered across multiple cities. I evaluated and compared multiple model structures, informed by analysis of the data. See the Github (link) or just the report (link).
Keywords: forecasting, regression, time series, auto-regressive (AR) models, XGBoost, ensemble modeling, parameter tuning, feature selection, table joins, dashboard
Predicting Chess Game Winner During the Middlegame
Predicting the winner of a chess game after turn 20 given the board state and history of the game to that point, trained on millions of games with 25+ turns played. See the Github (link) or just the report (link).
Keywords: data mining, feature engineering, classification, logistic regression, XGBoost
Forecasting Wind Power Generation
A suite of forecasting models predicting wind-power generation from a single turbine. See the Github (link) or just the report (link).
Keywords: data cleaning, feature selection, forecasting, linear regression, SARIMA, XGBoost
In the summer of 2022 I interned with the SUNDIALS team to optimize integrator parameters, in collaboration with the GPTune team.
From May, 2018 to June, 2020, as both intern and full software engineer, I worked with a team in an agile environment to develop software for the US Government.
In the summer of 2017 I interned with InterPublic Group, writing user-management scripts and developing tools for their global IT Support.
A flexible, efficient family of methods for numerically solving IVPs with the first adaptivity capability for any class of IMEX MRI methods.
Novel time-step controllers specifically designed for multirate infinitesimal numerical IVP-solving methods leveraging techniques from Control Theory. 2022.
Southern Methodist University. Expected May, 2023. Advisor: Daniel Reynolds. Thesis topic: Adaptivity in multirate numerical IVP-solving methods.
University of Washington. 2020.
University of Nebraska Omaha. 2019.