Welcome

Thank you for stopping by my personal website. I am a methodologist, applied scientist, and user experience researcher with a passion for making complex methods approachable and actionable. My work lives at the intersection of education, AI, and user-centered design. Over the past decade, I’ve led projects that not only advance the science of how we learn but also translate those insights into products and platforms that people actually use.

My passion is for rigorous, human-centered research. I specialize in blending quantitative and qualitative methods — randomized trials, causal inference, usability testing, workflow studies, and interviews — to understand how people interact with complex systems. With the rise of AI, those systems are changing rapidly. My work explores how we can design tools that are not only powerful, but also trustworthy, transparent, and usable for the educators, researchers, and developers who rely on them.

This commitment led me to create MetaReviewer, a free, collaborative platform now used by over 1,300 researchers across 150+ projects. In building and scaling MetaReviewer, I led UX studies on developer workflows, ran A/B tests, analyzed adoption patterns, and partnered with engineers to translate insights into product features. I also serve as PI of an NSF project where we benchmark AI models and study how users interpret and trust their outputs — work that directly informs design decisions for future AI-powered tools.

Earlier in my career, I authored national research standards for the U.S. Department of Education’s What Works Clearinghouse, where I focused on making evidence accessible to educators and policymakers. I’ve also conducted large-scale RCTs and systematic reviews funded by NSF, IES, and NIJ, studying SEL programs, cyberbullying prevention, and financial aid. Across all of these roles, I’ve been driven by the same question: how do we design research and technology so that it truly works for the people it’s meant to serve?

I also care deeply about teaching and mentorship. As an adjunct professor at the University of San Francisco (Psychology program), I teach graduate students research methods with a focus on evidence-based practice. I’ve mentored junior researchers (several of whom now lead projects as PIs), trained hundreds at national meta-analysis institutes, and authored 80+ publications. Alongside writing for academic audiences, I’ve contributed to newsletters and outlets like IEEE Spectrum to share insights on AI, UX, and open science.

Looking ahead, my focus is on shaping the responsible and human-centered use of AI in education and research. The future of research synthesis and evidence-based practice depends not only on methodological rigor, but also on creating intuitive, accessible, and inclusive user experiences.

Thanks again for visiting — feel free to reach out with questions, collaborations, or just a curious thought.