Welcome

Thank you for stopping by my personal website. I am a methodologist, applied scientist, and data operations leader with a passion for making complex methods usable in the real world. My work lives at the intersection of applied AI, human data, and actionable analytics. Over the past decade, I’ve led large-scale projects that not only advance how we evaluate evidence, but also translate those insights into tools, datasets, and platforms that people actually use.

Today, I serve as the Vice President of Analytics & Applied AI at Callahan & Associates. In this role, I architect the data quality and evaluation pipelines that drive core financial analytics products. I also lead our applied AI initiatives, focusing on identifying operational friction and deploying AI frameworks to streamline complex workflows, allowing teams to focus on high-leverage, strategic problem-solving.

My foundation is in rigorous, human-centered research. I specialized in building and running human data programs: designing labeling schemes, training and calibrating annotators, monitoring quality, and turning messy text and behavioral data into reliable signals. With the rise of large language models, those skills now sit squarely inside AI pipelines. A share of my work was about how we collect, structure, and benchmark human feedback so AI systems are not only powerful, but also trustworthy, transparent, and usable for the people who rely on them.

This commitment led me to found MetaReviewer, a free, collaborative web platform used by more than 1,300 researchers across 150+ projects. In building and scaling MetaReviewer, I owned the product vision, led user studies on screening and coding workflows, and partnered with engineers to turn those insights into features. Backed by several NSF-sponsored grants, I also designed and evaluated ML-assisted extraction workflows, creating gold-standard datasets and benchmarks to measure when automation was safe to use and when humans must stay firmly in the loop.

Earlier in my career, I served as a Principal Researcher at the American Institutes for Research and authored national research standards for the U.S. Department of Education’s What Works Clearinghouse, the U.S. Department of Labor's CLEAR, and several other federal agencies. I’ve been PI or co-PI on more than fifteen funded projects from agencies and foundations such as NSF, IES, and NIJ. Prior to joining Callahan full-time, I also ran an independent consulting practice partnering with organizations like 3ie and Development Services Group to build LLM-assisted extraction pipelines and evaluate model performance. Across all of this work, the throughline has been the same: design data, methods, and technology so they actually help people make better decisions.

I also care deeply about teaching and mentorship. I'm currently an Adjunct Professor at the University of San Francisco. In the past, I’ve taught graduate research methods, mentored junior researchers who now lead their own projects, trained hundreds of analysts at national meta-analysis institutes, and authored more than 100 publications spanning peer-reviewed articles, technical reports, and practitioner-facing pieces.

Looking ahead, my focus is on shaping responsible, human-centered uses of AI in both research and industry. The future of data-driven practice will depend not only on methodological rigor, but also on building intuitive, reliable, and inclusive systems for collecting and using human data at scale.

Thanks again for visiting. Feel free to reach out with questions, collaboration ideas, or just a curious thought.