I am a Data Scientist at Meta, working to improve the productivity and effectiveness of advertising ranking systems and the engineers who build them.

I am a quantitative psychologist by training, with a Ph.D. in Quantitative Psychology from the University of Notre Dame and nearly a decade of experience in psychological research. My research spans human motivation, behavior, and measurement, from understanding how changes in motivation influence performance to developing statistical methods for studying complex behavioral processes and evaluating fairness in AI systems. I have published over 20 peer-reviewed papers in psychology, quantitative methodology, and AI research.

I am particularly interested in the human side of AI: how we can meaningfully evaluate AI systems, and how advances in AI reshape human motivation, psychological needs, and people’s sense of value at work.

Professional Work

At Meta, my work focuses on developing meaningful measurements of advertising ranking system health and performance, evaluating engineering productivity, and understanding how AI-powered tools can accelerate engineering workflows. I work with engineering teams to translate complex system behavior into actionable insights that support better decisions and continuous improvements.

Previously, I worked on Ray-Ban Meta smart glasses, focusing on product health, quality, and user engagement. My work involved using product and behavioral data to understand user experiences and identify opportunities for product improvements.

Research Interests

Human Motivation and Psychological Experience. Understanding how motivation, emotions, and psychological needs influence human behavior, performance, and well-being, particularly as people adapt to new technologies and changing work environments.

AI Measurement and Fairness. Developing rigorous approaches to evaluating AI systems, including their behavior, performance, and fairness. I am particularly interested in how psychological measurement principles can help identify biases and improve the reliability and interpretability of AI evaluations.

Human Value and Work in the Age of AI. Exploring how AI changes people’s sense of competence, autonomy, purpose, and personal value at work, and how these changes affect the fulfillment of fundamental psychological needs.

Academic Background

I earned my Ph.D. in Quantitative Psychology from the University of Notre Dame, where I worked under the supervision of Prof. Lijuan (Peggy) Wang. My doctoral research focused on quantitative methods for understanding behavioral processes, particularly longitudinal modeling and statistical approaches to complex psychological data.

Over nearly a decade of psychological research, I have collaborated with interdisciplinary teams across psychology, statistics, education, and medicine. I have worked closely with Prof. Irene J. K. Park at Indiana University School of Medicine, Prof. Margarita Alegría at Harvard Medical School, and Prof. Tiffany Yip at Fordham University.

For a complete list of publications and professional experience, please see my Publications and CV.