Tyler Smith builds tools to investigate cyber threats. He has over 7 years of experience developing machine learning models and data pipelines, and holds Security+.

Tyler developed pkgaudit, a static analysis security tool for R packages – an often-overlooked attack surface in regulated environments handling sensitive data. He also built a network intrusion detection model trained on 2.8 million flows to detect ten attack types with recall above 0.96, and a serverless vulnerability-enrichment pipeline that uses AI/LLMs to triage cloud vulnerability findings while defending against prompt injection.

Tyler earned a PhD in epidemiology, where he learned to separate signal from noise and quantify uncertainty. He applies the same discipline to security: distinguishing meaningful threats from false positives and building systems that support defensible decisions. Before earning his PhD, Tyler worked in environmental risk assessment and often appeared before federal and state regulatory agencies, including the U.S. Food and Drug Administration.