Enterprise AI systems
An in-house platform for AI agents and multi-agent workflows, LLM evaluation and hallucination detection pipelines, and human-in-the-loop tools that help business teams move from spreadsheets to AI workflows they can trust.
AI engineer and researcher building AI systems people can rely on.
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Senior AI Engineer at Universal Creative
I work in a forward-deployed role: I meet with business teams, scope what they need, and take the AI solution from first prototype to trusted production. I lead an AI engineering team across our AI platform, finance, and cost-estimating work. Before that, my PhD focused on designing neural networks for medical image segmentation that know when they might be wrong: uncertainty estimates and explainability that both improved the model's accuracy and told clinicians when to rely on its output. That same question, when an AI answer can be trusted, drives the evaluation work I do today.
- Role
- Senior AI Engineer, Universal Creative
- Education
- PhD & MS in Computer Science, UCF
- Focus
- LLM agents & evaluation, RAG, medical imaging
- Based in
- Orlando, FL
- Off the clock
- Competitive powerlifter
Selected work
Uncertainty-aware medical image segmentation
Novel 3D architectures and loss functions for tumor and organ-at-risk segmentation, with uncertainty estimates that tell clinicians when to rely on the model. Raised Swin UNETR Dice from 0.47 to 0.64.
See the codeAn assistant that knows its limits
The chat above answers only from my resume, a UCF News feature, and first-party facts. It uses hybrid keyword + embedding retrieval, declines instead of guessing when it isn't confident, and checks every sentence against its sources.
See how it worksPublications
- Large-scale multi-center CT and MRI segmentation of pancreas with deep learning — Medical Image Analysis, 2025
- Uncertainty-guided coarse-to-fine tumor segmentation with anatomy-aware post-processing — IEEE ACDSA, 2025
- Practical and ethical considerations for generative AI in medical imaging — MICCAI Workshop, 2024
- Self-supervised learning for organs at risk and tumor segmentation with uncertainty quantification — IEEE ICECCME, 2023
- Enhancing organ at risk segmentation with improved deep neural networks — SPIE Medical Imaging, 2022