Current Projects

I am investigating how people detect AI-generated images and what factors make them miss the subtle artifacts these images contain, under supervision of Matt Groh at Kellogg School of Management. My research aims to develop tools and techniques to enhance human ability to distinguish between real and synthetic images. In a world where AI-generated images are increasingly realistic, understanding how people perceive them is crucial for combating misinformation and maintaining trust in visual media.

Think you can distinguish real photos from AI-generated images? Try our DetectFakes website!

Past Projects

Conformal Prediction for AI-Advised Decisions: I researched enhancing AI-advised decision-making by accurately quantifying prediction uncertainty of deep neural networks. I explored the effectiveness of conformal prediction sets as an alternative to traditional uncertainty measures, measuring how they impact human decisions in real-world image labeling scenarios. This work received a Best Paper Honorable Mention at CHI 2024.

Prenatal Stress Reduction: In collaboration with the Center for Advancing Safety of Machine Intelligence (CASMI), advised by Maia Jacobs, I worked on co-designing patient-facing machine learning for prenatal stress reduction.