Polina Golland

Polina Golland is the Sunlin (1996) and Priscilla Chou Professor in MIT’s Department of Electrical Engineering and Computer Science (EECS). She leads the Medical Vision Group at the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her main research focus is developing novel techniques for analyzing and understanding biomedical images. Her work includes algorithms that explore the geometry of the world, process images in new ways, and improve image-based inference through statistical modeling of image data. Golland is interested in shape modeling and representation, predictive modeling and visualization of statistical models. Her research focuses on developing statistical methods for analyzing and characterizing biological processes based on image information. She earned a PhD in electrical engineering and computer science from MIT.

Selected Publications

• Dey, N., Billot, B., Wong, H. E., Wang, C., Ren, M., Grant, E., Dalca, A. V., & Golland, P. (2025). Learning general-purpose biomedical volume representations using randomized synthesis. Proceedings of the Thirteenth International Conference on Learning Representations (ICLR).
• Wang, P., Lam, B. D., Liu, Y., Asgari-Targhi, A., Panda, R., Wells, W. M., Kapur, T., & Golland, P. (2025). Calibrating expressions of certainty. Proceedings of the 13th International Conference on Learning Representations (ICLR).
• Wang, P., Shen, Y., Guo, Z., Stallone, M., Kim, Y., Golland, P., & Panda, R. (2025). Diversity measurement and subset selection for instruction tuning datasets. In Proceedings of the International Conference on Learning Representations (ICLR) Workshop on Data Problems.

Media

• April 4, 2025: MIT News, New method assesses and improves the reliability of radiologists’ diagnostic reports
• December 11, 2023: MIT News, MIT Generative AI Week fosters dialogue across disciplines
• May 24, 2023: MIT News, A better way to match 3D volumes