Maja Waldron

Hello, I am an Assistant Professor in the Department of Statistics at the University of Wisconsin–Madison. We are part of the College of Computing & Artificial Intelligence, and I am affiliated with the Department of Computer Sciences and the Data Science Institute as well. My research develops statistical methods for trustworthy and efficient AI, with a focus on uncertainty quantification, conformal prediction, deep probabilistic modeling, and generative models. I completed my PhD under the supervision of David Blei.
My recent publications are available on Google Scholar.

Research Group

• Avil Aneja, Computer Sciences Master's Student
• Heesung Ahn, Statistics PhD Student
• Yuheng Lai, Statistics PhD student co-advised with Garvesh Raskutti

My group studies how modern AI systems can make reliable predictions when data, compute, or supervision is limited. Our work combines conformal methods, probabilistic modeling, and generative approaches. Students at UW-Madison who are interested in these topics are encouraged to join our weekly reading group, where we read and discuss recent research papers on topics of interest.

Selected Work

• CAOS: Conformal Aggregation of One-Shot Predictors, ICML 2026.
  Develops finite-sample uncertainty guarantees for one-shot prediction settings using conformal methods. This work is part of a broader research direction on uncertainty quantification and reliable prediction with limited labeled data.

Clinical Translation

I serve as Chief Technology Officer of CranioSure, a company developing a smartphone app to support infant head-shape assessment and improve access to craniofacial care. In this role, I lead CranioSure's technical strategy, machine learning research, and app development. This work raises broader research questions about learning from limited labeled data, producing reliable predictions, and adapting foundation models to specialized clinical settings. These challenges help ground my academic research in real clinical needs.

Teaching

• Spring 2026: Statistical Data Visualization
• Fall 2025: Statistical Frontiers in Foundation Models and LLMs
• Spring 2025: Introduction to Applied Deep Learning

Background

Before joining UW–Madison, I was a research scientist at Bosch Research, where I worked on deep probabilistic modeling and anomaly detection. I served as a technical lead of the Bosch Center for AI (at the time Europe's largest AI lab with 150+ Ph.D. researchers) and contributed to the development of the center's strategy on foundation models. My work has led to methodological contributions published at PAMI, ICML, and ICLR, as well as to numerous patent applications.