About Me

I am a dedicated data scientist and educator committed to advancing evidence-based practices across science, research, government, and society. With expertise in model validation, statistical analysis, and machine learning, I focus on improving data science standards to ensure models deliver reliable results in real-world settings.

My work bridges the gap between theoretical machine learning and practical implementation, helping organizations avoid costly pitfalls through rigorous validation methodologies. Through teaching, writing, and consulting, I empower data practitioners to build trustworthy models that genuinely serve their intended purposes.

Professional Focus

Evidence-Based Practice Champion

I advocate for evidence-based approaches that ensure data-driven decisions are grounded in sound methodology. My work helps organizations develop robust validation frameworks that withstand scrutiny and deliver consistent value in production environments.

Data Science Standards Advocate

I'm passionate about establishing and promoting higher standards in data science practice. Through my courses, publications, and speaking engagements, I highlight common validation pitfalls and provide actionable frameworks to improve model reliability.

AI & LLM Research

My research examines the capabilities, limitations, and validation challenges of large language models and emerging AI systems. I'm particularly interested in evaluation methodologies that accurately assess model performance beyond simple benchmarks.

Educational Impact

As an educator, I create accessible, practical content that helps data practitioners at all levels improve their validation techniques. My teaching philosophy emphasizes real-world case studies and hands-on exercises that develop critical thinking about model evaluation.

Expertise Areas

Model validation methodologies

Cross-validation techniques

Statistical significance in machine learning

Detecting and preventing data leakage

Evaluation frameworks for LLMs and generative AI

Time series and structured data validation

Production model monitoring and maintenance

Model bias identification and mitigation

Through my courses, consulting, and research, I'm committed to advancing the field of data science by ensuring that the models we build truly serve their intended purposes and deliver reliable results when it matters most.