"I believe in intuition and inspiration. Imagination is more important than knowledge. For knowledge is limited, whereas imagination embraces the entire world, stimulating progress, giving birth to evolution. It is, strictly speaking, a real factor in scientific research.”
― Albert Einstein 1929, quoted by Dr. Boris Kovalerchuk in Visual Knowledge Discovery and Machine Learning 2018

Double Bachelor's of Science Computer Science and Applied Mathematics senior student. Active research assistant at the Visual Knowledge Discovery and Imaging Lab of Central Washington University. Former system administrator, full-stack software developer, and web-developer. Next, planning graduate studies in Computational Science with a focus on Visual Machine Learning. Solving problems at the intersection of Machine Learning & Data Visualization by blending machine computation with human cognition.

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Ordered from current to first work.

3. "Synthetic Data Generation with Visual Knowledge Discovery"

• Authors: Alice Williams and Dr. Boris Kovalerchuk.
• Status: Actively researching at the CWU Visual Knowledge Discvery and Imaging Lab.
• Contributions: Expanding on synthetic data generation methods with interactive algorithms paired with Generative Adversarial Networks (GANS). Expanding software with relabeling and GAN data synthesization, in support of theorized algorithms.
• Topics: Synthetic Tabular Data Generation, General Line Coordinates, Hyperblock Model Representation, Generative Adversarial Networks
• Developed software: Dynamic_Coordinates_Vis_System

2. "Representation Learning with Visual Knowledge Discovery"

• Authors: Alice Williams & Dr. Boris Kovalerchuk.
• Status: Preparing camera ready version, accepted for presentation at AI Human Computer Interfaces 2025 in thematic area of Artificial Intelligence in HCI.
• Contributions: Visual solution to the 2003 Gödel Prize problem of AdaBoost classifier by visual observation of options for different classifiers before selecting them for boosting allowing alternative ways to build stronger classifiers, including directly building classifiers in a lossless visualization space.
• Topics: Human-Centered AI, Machine Learning, Multi-Class Classification, Feature Engineering, Explainable AI, AI Risk Mitigation, Human-in-the-Loop, Multi-Dimensional Distributions, General Line Coordinates, Concentric Coordinates.
• Developed software: Java_Tabular_Vis_Toolkit, HyperblockParser, InLineCoordinatesCoefficientSolver

1. "Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates"

• Authors: Alice Williams & Dr. Boris Kovalerchuk.
• Status: Published in IEEE proceedings and presented at IV2024 in track of Artificial Intelligence and Visual Knowledge Discovery, earned the 'Best Paper Award'.
• Contributions: Proposed an algorithm for labeled synthetic data generation, addressed data balancing, demonstrated deficiencies of popular SMOTE (Synthetic Minority Oversampling Technique), and showed improvements to classifier performance across 14 standard classifiers.
• Topics: Synthetic Data Generation, Automated Data Labeling, General Line Coordinates, Circular Coordinates, Parallel Coordinates, Shifted Paired Coordinates, Tabular AI/ML Data, Multidimensional Data Visualization, Visual Knowledge Discovery.
• Developed software: Dynamic_Coordinates_Vis_System

I'm always open to discussing new projects or opportunities. Feel free to reach out or connect with me!