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© 2025 Allan Paiz. All rights reserved.
main
../about.ap
© 2025 Allan Paiz. All rights reserved.
I'm currently a student at theUniversity of South Carolina. Computer Science major, Mathematics minor. Expected graduation December of 2026. I enjoy all sports with (and without) the use of analytics. soccer>(football>=baseball)>(hockey>=basketball)I enjoy playing tournament poker and competitive video games, though I don't get the chance to play as often as I'd like. I'm a United States Marine Corps Veteran. When I'm not on a computer... I'm enjoying life with my wife, twin babies, and 2 doggos.
Defensive Stopping Power : NFL Big Data Bowl 2024
- New defensive metrics using NFL tracking data. Wash Your Rice
- Horror themed puzzle game. Dynamical Systems and AI Research Group
- Research in robotics, AI, and dynamical systems. TuneUP by JETA
- Music application for my software engineering course. Predicting Construction Permit Delays
- Math Foundations of ML/DS course project. Green Dog : NFL Big Data Bowl 2025
- A tool that predicts the assignment of an NFL receiver. // This portfolio is meant to resemble a neovim text editor. // It looks great on my PC, but may not on yours... // if that's the case, we're UNDER CONSTRUCTION!
at] gmail [dot] comGithub: https://github.com/allanpaizResume: /resume.pdfUpdated: 8/19/2026
2024 NFL Big Data Bowl competition's tackling theme left me struggling to find a direction for my project.
Several of my ideas were limited by the provided datasets.
first_contact flag in the data was misleading. (See GIF)identify all contact, or potential contact, between ball carriers and defenders.
Links: GitHub Repository Kaggle Submission Finalists: NFL Announcement Kaggle Announcement Summary: Developed a neural network to detect all defender-ball carrier contact in NFL tracking data, addressing limitations in traditional defensive metrics. Technical Skills: Python, Pandas, NumPy, Keras, Matplotlib Personal Skills: data processing, problem solving, data analysis, machine learning, feature engineering, visualization, reporting Features: feed-forward neural network, spatial and velocity data modeling, detailed visualization and analytics Applications: NFL and NCAA player scouting and evaluation, game planning and analysis, in game decision making, Fantasy football and sports betting analytics
Wash Your Rice is a five person capstone project built in Unreal Engine 5.6.1 and C++.
The game combines several connected systems, including survival mechanics, puzzle progression, an in world computer, time management, and enemy encounters.
My main contributions focused on the computer interface and the systems that manage its puzzle progression.
Links: Capstone Site GitHub Repository Final Demo Proof of Concept Summary: Built core C++/UMG computer-interface and puzzle-progression systems for a five-person Unreal Engine 5.6.1 capstone, with automation coverage for UI state and puzzle behavior. Technical Skills: Unreal Engine 5.6.1, C++, Rider, UMG, Enhanced Input, Slate, GameInstanceSubsystem, Unreal Automation Testing, Git LFS Personal Skills: systems design, debugging, integration, test-driven iteration, team collaboration Engineering Focus: persistent game state, computer UI and progression, inventory and sanity components, timer-driven events, save/load, UI flow, regression coverage
undergraduate research assistant in the Dynamical Systems & AI Research Group under Dr. Vignesh Narayanan. My summer work continued through the 2026 NSF CISE Research Experience for Undergraduates program.
My research connected robotics, control, optimization, and AI through autonomous-navigation experiments, multi-agent path finding, simulation, and perception-system design.
The experience gave me the opportunity to move between mathematical ideas, software implementations, simulation, and experiments on physical robots.
Links: Resume Summary: Robotics research combining ROS 2/TurtleBot4 hardware experiments, multi-agent path finding, Gazebo simulation, constrained optimization for obstacle avoidance, and perception-system design. Technical Skills: ROS 2, Gazebo, TurtleBot4, Python, C++, Bash, OAK-D, 2D LiDAR, YOLOv8, constrained nonlinear optimization, control theory Personal Skills: experimental design, research communication, systems thinking, technical writing, technical documentation Research Focus: multi-agent path finding, hardware experimentation, event-triggered optimal control, obstacle avoidance, robotics simulation, sensor fusion, safety-aware robotics
TuneUp is a prototype music app developed in Java for my Software Engineering course.
Teamwork - Worked in a team of four to complete a project from start to finish.Planning - Engaged with a client to define project requirements and objectives.System Design - Created documentation and UML diagrams to guide development.Development & Testing - Built and tested a Java console app, using GitHub for version control and collaboration. Front End - Designed and developed a GUI with JavaFX, applying UI/UX principles.
Links: GitHub Repository Summary: Developed a music application for teacher-student interaction with composition and free play modes, lessons, and song libraries. Technical Skills: Java, JUnit, JavaFX, git, GitHub, Object-Oriented Programming, documentation Personal Skills: team collaboration, communication, project management, presentation Features: software development life cycle, system design, software engineering, unit testing, front end development
can permit timing help a project manager estimate schedule risk while preparing a bid?
Delays in permit issuance can change a construction schedule before work begins. I framed that uncertainty as a classification problem: flag issued residential permits that are likely to take longer than normal.
Links: City of Atlanta Building Permits Data Summary: A 7,504-record permit-issuance-delay classifier that compared a location-only RBF SVM, structured neural network, and text-enhanced neural network. The description model achieved 69.06% test accuracy and 77.44% high-risk sensitivity. Technical Skills: Python, scikit-learn, RBF kernel SVM, neural networks, TF-IDF, one-hot encoding, random oversampling Personal Skills: problem framing, model comparison, analytical communication, responsible interpretation Methods: temporal train/test split, public-data preprocessing, structured permit modeling, description-text features, classification metrics
Green Dog was my 2025 NFL Big Data Bowl project. It grew from a defensive question: can a pre-snap tool help predict whether an eligible receiver will run a route or stay in to block?
That decision can change a defender's assignment and help reveal an offense's tendencies before the ball is snapped.
Links: GitHub Repository Kaggle Submission Summary: A pre-snap model that estimates whether an eligible receiver will run a route or stay in to block, giving football decision-makers another way to inspect tendencies. Technical Skills: Python, TensorFlow, Keras, pandas, NumPy, scikit-learn, Matplotlib, Seaborn Personal Skills: feature engineering, model evaluation, sports analytics, technical storytelling Features: pre-snap NFL tracking data, play/player/team feature branches, route probability, week-by-week evaluation