Ross is a Research Assistant Professor at the Center for Healthy Minds at the University of Wisconsin – Madison. His research uses machine learning, natural language processing, and intensive longitudinal data to improve the prediction and understanding of suicide risk and nonsuicidal self-injury. Much of his work focuses on analyzing high-frequency, real-world data—such as passively collected smartphone screenshots or momentary surveys—to develop algorithms that can adapt in real time to changing risk profiles.

Ross collaborates across disciplines to advance digital mental health tools, including projects funded by NIH, DARPA, and other federal agencies. He co-leads multiple studies aimed at refining how and when suicide risk is detected, including efforts that leverage foundation models and large-scale data integration. In addition to developing methods, he creates open-source software used in psychological and clinical research, and has published widely on machine learning, text analysis, and longitudinal modeling.

Before joining UW–Madison, Ross was an Assistant Professor at the University of Notre Dame. He earned his PhD in Quantitative Psychology from the University of Southern California.