AI/ML Research Engineer
Machine Learning Research Engineer pushing the boundaries of intelligent systems through self-supervised learning, adversarial AI, and grounded theoretical research. Currently at Johns Hopkins University Applied Physics Laboratory.
// Career Path
Johns Hopkins University Applied Physics Laboratory
Data Science Club at Sacramento State
Intel Corporation
Aruba, a Hewlett Packard Enterprise Company
// Academic Background
Johns Hopkins University
Aug 2023 — Dec 2025
Focus in Deep Learning and Generative AI. Conducting research on quantization-aware training and AI-generated image detection using foundation models.
GPA: 4.0California State University, Sacramento
Aug 2019 — May 2023
Graduated Summa Cum Laude with a Minor in Mathematics. President of the Data Science Club.
GPA: 3.984// Research & Publications
JHU course research on 1.58-bit (ternary) Quantization-Aware Training for GPT architectures. Demonstrated that ternary weight quantization achieves competitive validation loss using smooth gradient approximations to overcome traditional STE limitations.
View on GitHub →Co-authored research presenting a novel detection model for AI-generated imagery. Demonstrated that a carefully finetuned DINOv2 model achieves superior cross-generator generalization compared to linear probe methods.
View on GitHub →Presented as primary contributor at AGU Fall Meeting. A neural network package designed for the heliophysics research community, enabling rapid data modeling and predictive analysis.
Contributed to presentation examining the reliability of combining multi-viewpoint data from three spacecraft through interpolation methods.
// Open Source
A framework for creating "guided spirals" for Large Language Models. Features components for managing chat histories, flows, conditional branching, and external memory integration with OpenAI models.
Programming Artificial Intelligence Utilities — a Python package designed to remove common delays in machine learning research through a collection of helpful APIs and tools.
Built PyTorch-style deep learning packages from the ground up in C++, Python, and Java. Implemented custom neural network layers, batch normalization, activations, and gradient descent optimizers.
// Thoughts & Insights
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// Let's Connect