Hello, I'm Alex Fish

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profile of alex

About Me

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!

Portfolio Projects

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

Professional Experience

Intern, Lawrence-Livermore National Lab

In the summer of 2022 I interned with the SUNDIALS team to optimize integrator parameters, in collaboration with the GPTune team.

Software Engineer, Northrop Grumman

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.

Intern, InterPublic Group

In the summer of 2017 I interned with InterPublic Group, writing user-management scripts and developing tools for their global IT Support.


Implicit-Explicit Multirate Infinitesimal Stage-Restart Methods

A flexible, efficient family of methods for numerically solving IVPs with the first adaptivity capability for any class of IMEX MRI methods.

Adaptive Time Step Control for Multirate Infinitesimal Methods

Novel time-step controllers specifically designed for multirate infinitesimal numerical IVP-solving methods leveraging techniques from Control Theory. 2022.


An Eclipse IDE plug-in to assist software engineers in managing code clones, similar segments of code within a codebase, across versions of software. 2018.


PhD, Applied and Computational Mathematics

Southern Methodist University. Expected May, 2023. Advisor: Daniel Reynolds. Thesis topic: Adaptivity in multirate numerical IVP-solving methods.

MS, Applied Mathematics

University of Washington. 2020.

BS, Mathematics with Computer Science Minor

University of Nebraska Omaha. 2019.