Hi, I’m Negar Kamali.

I’m a third-year Ph.D. student in the Computer Science department at Northwestern University, advised by Professors Matt Groh and Jessica Hullman. My interests lie in Human-AI team decision-making, misinformation and deepfake detection, and uncertainty quantification.

Earlier, I completed a Ph.D. in Computational Mechanics, during which I developed several advanced numerical methods for solving nonlinear wave propagation PDEs. Following my passion in understanding how AI impacts human decision-making, I began a second Ph.D. in Computer Science at Northwestern University. This sparked my decision to go back to academia and do research on human-AI collaboration.

I also have prior industry experience as a Software Developer and Automation Expert.

Current Projects

The main project I am working on is investigating how people detect AI-generated images and what factors make them miss the subtle artifacts and implausibilities these images often contain. This project is under supervision of Matt Groh with 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 have what it takes to distinguish real photos from AI generated images? Put your skills to the test on our DetectFakes website!

The second project I am currently working on is in collaboration with the Center for Advancing Safety of Machine Intelligence (CASMI) advised by Maia Jacobs to Co-Design Patient-Facing Machine Learning for Prenatal Stress Reduction.

Past Projects

I have researched on enhancing AI-advised decision-making by accurately quantifying the prediction uncertainty of deep Neural Networks (NNs). I explore the effectiveness of conformal prediction sets as an alternative to traditional uncertainty measures, and how they impact human decisions in various real-world scenarios. Specifically, we measured the Utility of Conformal Prediction Sets for AI-advised Image Labeling. This work is published in Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems and was recognized by a best paper honorable mention award