Selected Work & Community Impact
Expert data science consulting delivering machine learning solutions, predictive modeling, and advanced analytics across healthcare, research, and data-driven organizations.
Comprehensive solutions tailored to your analytical needs, from predictive modeling to custom tool development
Custom ML solutions using ensemble methods, neural networks, and advanced algorithms. Specializing in classification, regression, and time-series forecasting with comprehensive model evaluation.
In-depth statistical analysis and visualization to uncover patterns, relationships, and insights. Author of production-grade EDA toolkits used across research institutions.
Develop sophisticated models for healthcare outcomes including kidney disease risk estimation, ICU mortality prediction, and patient stratification tools validated against clinical standards.
Build proprietary Python libraries and analytical frameworks. Experience creating model diagnostics tools, SHAP explainers, and automated evaluation pipelines for reproducible research.
Corporate workshops and academic instruction in data science fundamentals, machine learning techniques, and best practices. Curriculum development for university-level courses.
Partner on academic research projects with emphasis on healthcare analytics, clinical decision support systems, and methodological innovation. Co-authorship and publication support.
is a Data Scientist at UCLA Health with over 15 years of experience across healthcare, financial services, and education. He serves as an adjunct professor at the University of San Diego, where he teaches statistics and machine learning in the M.S. in Applied Artificial Intelligence program. He has contributed to clinical prediction research, co-developed a production-grade EDA toolkit contracted for publication with Taylor & Francis, and presented at JupyterCon 2025.
is a Data Scientist at the University of California, Riverside, with over ten years of experience in the education data management industry. He excels in data warehousing, analytics, machine learning, SQL, Python, R, and report authoring, and holds an M.S. in Applied Data Science from the University of San Diego. He has co-developed analytical tools and pipelines deployed in research and institutional settings, and presented alongside Leon at JupyterCon 2025.
is a Professor of Practice and Academic Director of the Applied Data Science (MS-ADS) and Applied Artificial Intelligence (MS-AAI) programs at the University of San Diego's Shiley-Marcos School of Engineering. A former senior technical advisor for the CDC's National Institute for Occupational Safety and Health (NIOSH) and visiting scholar at UCSD's San Diego Supercomputer Center, he holds a PhD in Geo-Engineering from the University of Nevada, Reno, an MSc in Data Science from Michigan Tech, and an MBA from UNR. He has published widely in international journals and received numerous university, national, and international awards.
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