Career

I am currently a Senior Data Scientist at Freddie Mac working on home price estimatation models, housing indices and geospatial/geometric data and its applications to the housing market.

Tutoring

• ACT and SAT math section preparation
• GRE quantitative section preparation
• Algebra
• Trigonometry
• Calculus
• Differential Equations
• Linear Algebra
• Complex Analysis
• Probability and Statistics
• Discrete Mathematics
• Python
• Data Structures and Algorithms
• Data Science
• Machine Learning
• Artifial Intelligence
• Deep Learning

Ph.D. in Computer Science

August 2018 - December 2021

I completed my Ph.D. at the University of South Carolina in the Department of Computer Science and Engineering. Jason O'Kane advised me in discovering more about Visibility-Based Multi-Robot Pursuit-Evasion problems, including innovative sampling ideas and robustness.

Ph.D. in Mathematics

August 2016 - May 2020

I earned a Ph.D. from the University of South Carolina in the Department of Mathematics. Éva Czabarka and László Székely co-advised me in exploring graph invariants in triangulations and quadrangulations of the sphere.

Other Education

August 2009 - May 2015

• M.S. Computer Science (2021) - University of South Carolina
• M.A. Mathematics (2015) - University of Miami
• B.S. Computer Science and Mathematics (2013) - Palm Beach Atlantic University
• Associate of Arts (2011) - Inver Hills Community College

Computer Science

1. Robust-by-Design Plans for Multi-Robot Pursuit-Evasion (accepted, ICRA 2022)
2. Rapid Recovery from Robot Failures in Multi-Robot Visibility-Based Pursuit-Evasion (accepted, IROS 2021)
3. A Visibility Roadmap Sampling Approach for a Multi-Robot Visibility-Based Pursuit-Evasion Problem (accepted, ICRA 2021)

Mathematics (Authors are typically ordered alphabetically)

1. Minimum Wiener Index of Triangulations and Quadrangulations (accepted, Discrete Applied Mathematics)
2. Proximity in Triangulations and Quadrangulations (accepted, Electronic Journal of Graph Theory and Applications)
3. Wiener Index and Remoteness in Triangulations and Quadrangulations (accepted, Discrete Mathematics & Theoretical Computer Science)

Miscellaneous

1. Clinical Characteristics of Suspected COVID-19 in Pediatric Patients (accepted, International Journal of Critical Care and Emergency Medicine)

In Preparation

1. Increasing Nicotine Dependence Treatment Encounters Through Nursing Education
2. Sampling in Multi-Robot Pursuit Evasion
3. Robustness in Multi-Robot Pursuit Evasion
4. Minimum Complexity in Triangulations and Quadrangulations

Python Programming

• 100 Days of Code: The Complete Python Pro Bootcamp for 2022 teaches Python from the ground up and covers a lot of interesting concepts in an easy to follow format.
• OnlineGDB allows you to run Python code without any installation.
• Advent of Code provides programming challenges with some personality.
• Project Euler challenges both your programming and mathematical abilities.
• LeetCode contains a massive amount of exercises closely related to technical coding interviews.

Data Science and Machine Learning

• Complete Machine Learning & Data Science Bootcamp 2022 teaches essential data science and machine learning skills in a repitious way that makes the lessons stick. I prefer PyTorch to Tensorflow, so skip the Tensorflow portion of this course for the bullet point below.
• PyTorch for Deep Learning teaches the essentials of deep learning using PyTorch. I have yet to work through this course but the instructors previous courses have been so solid I need to initially recommend it. A free sample can be found here. This course will be added to Udemy in the future.
• Kaggle has thousands of datasets as well as forums and posted solutions. This can be utilized for nearly every type of data science project.
• Stat Quest presents a wide range of topics in an enjoyable and understandable way.
• The Hundred-Page Machine Learning Book concisely covers a lot of essential topics in machine learning. The author also has an expanded follow up book Machine Learning Engineering.