Faculty Recruiting Support CICS

Erik G. Learned-Miller

248 CS Building
(413) 545-2993


Computer vision and machine learning. Deep learning. Probabilistic and statistical methods in vision and image processing. Non-parametric and distribution-free statistics. Information theoretic methods. Unsupervised and semi-supervised learning. Low-shot learning.


Professor Learned-Miller's interests can be broadly categorized as applying ideas and methods from machine learning to problems in computer vision. His research has included work on the following problems: learning from one example (one-shot learning), face recognition and face detection, segmentation of moving objects in video, algorithms for the joint alignment of unlabeled images, and text recognition. He has produced some of the most widely used benchmarks in face recognition research, including Labeled Faces in the Wild and the Face Detection Database and Benchmark. His current work focuses on unsupervised, self-supervised, and semi-supervised learning, and on mechanisms for regulating face recognition technology.


Ph.D., Electrical Engineering and Computer Science, Massachusetts Institute of Technology (2002), M.S., Electrical Engineering and Computer Science, Massachusetts Institute of Technology (1997), B.A., Psychology, Yale University (1988). Professor Learned-Miller joined the faculty at the University of Massachusetts Amherst College of Information and Computer Sciences (CICS) in 2004 as an Assistant Professor. Before joining CICS, he was a post-doctoral research engineer in the Electronics Research Laboratory at the University of California, Berkeley. Previously, Learned-Miller was the Chief Executive Officer and co-founder of CORITechs, Inc., a company that designed surgical planning software for neurosurgeons. 

Activities & Awards

Professor Learned-Miller received the Microsoft-MIT graduate student fellowship. He holds a patent for "apparatus for neurosurgical stereotactic procedures." Learned-Miller is on the editorial board of the Journal of Machine Learning Research and was program chair for the 2015 Computer Vision and Pattern Recognition conference. He received an NSF CAREER Award in 2006. In 2019, he received the Mark Everingham Award for contributions to the computer vision community for his development of the Labeled Faces in the Wild database.