A small, deliberate team led by a founder with two U.S. patent-pending AI systems, peer-reviewed publications across healthcare, security, and large language models, and a doctorate in computer science focused on big data analytics.
Dr. Leonardo Lawrence is the Founder of Quantum Horizon AI, which he established in 2022. He has years of experience working in the U.S. federal government and is a college professor of artificial intelligence and machine learning, teaching graduate and doctoral students, mentoring their research, and advising on capstone and dissertation work.
He is the named inventor on two U.S. patent-pending AI systems, one for AI-driven supply chain optimization, the other for a multilayer cognitive AI architecture combining generative experimentation with human-guided learning, and the author or co-author of peer-reviewed research on adaptive encryption, deep learning for early-disease detection, large language model integration in enterprise systems, and machine learning for breast-cancer screening.
His technical work covers machine learning, deep learning, operations research, data science, and big data analytics, applied to problems in healthcare, defense, finance, manufacturing, and the public sector.
Credentials. Doctorate in Computer Science with a concentration in big data analytics; Master of Science in Artificial Intelligence and Machine Learning (Distinguished Scholar); graduate certificates in data science (Gamma Beta Phi) and college teaching; Bachelor of Science in Electrical Engineering with a mechatronics concentration (Magna Cum Laude); Master of Arts in Homeland Security focused on counter-terrorism. Senior Artificial Intelligence Engineer (ARTIBA) and Senior Data Scientist (DASCA) certifications.
Prior to academia and federal service, he served as an Infantry Squad Leader in the United States Marine Corps with operational deployments to Afghanistan.
Working knowledge spans:
Stack: Python, C++, CUDA, Rust, SQL for systems work; PyTorch, JAX, Hugging Face Transformers, LangChain, Ray, MLflow for model and pipeline engineering; AWS, GCP, Docker, Kubernetes, Triton Inference Server for deployment.
A patent-pending architecture for AI-driven supply chain optimization covering tariff and duty modeling, supplier discovery, multi-modal shipping, and integrated trade intelligence at the HTS-code level.
A patent-pending framework for multilayer cognitive AI that combines generative experimentation with human-guided learning to enable autonomous scientific discovery.
A context-aware adaptive encryption framework that integrates sensitive-data detection and network intrusion detection to deliver dynamic data security at the boundary where it matters most.
DOI: 10.61643/c95016A comparative deep-learning study on the early detection of Autism Spectrum Disorder from behavioral questionnaire data, with model selection driven by clinician-graded ground truth.
DOI: 10.61643/c478960A case study of LLM effectiveness across enterprise scenarios, with a proposed framework for universal integration into existing business systems.
DOI: 10.61643/c38193An applied study on how machine-learning systems can reduce human error in breast-cancer screening workflows, with attention to model design and clinician-facing presentation.
ISBN: 9798382911397We hire deliberately. Every addition is someone who has shipped real systems, not collected certifications. As new team members come on board, you'll find them here.
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