Erik Learned-Miller

Erik Learned-Miller is a Professor and Chair of the Faculty at the Manning College of Information and Computer Sciences (CICS), University of Massachusetts Amherst. He is based in the Department of Computer Science and leads the Computer Vision Lab, with strong affiliations to the Center for Data Science. His work bridges machine learning and computer vision, focusing on foundational and ethical aspects of visual recognition systems.

Education:

• PhD in Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2002
• MS in Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 1997
• BA in Psychology, Yale University, 1988

Learned-Miller's research centers on machine learning methods for computer vision problems, particularly in scenarios with limited labeled data. His work includes one-shot learning, face detection and recognition, image and video segmentation, joint image alignment, and text recognition. He emphasizes unsupervised, self-supervised, and semi-supervised learning paradigms, and is actively involved in addressing societal concerns around the regulation of face recognition technology.

His contributions have had a major impact on the computer vision community, most notably through the creation of widely used benchmarks such as Labeled Faces in the Wild and the Face Detection Database and Benchmark, which have become standard evaluation tools in the field.

Scientific Awards and Honors:

• NSF CAREER Award (2006)
• Mark Everingham Award (2019)
• Microsoft-MIT Graduate Student Fellowship

Learned-Miller has played significant roles in the academic community, including serving as Program Chair for the 2015 Conference on Computer Vision and Pattern Recognition (CVPR) and as a member of the editorial board of the Journal of Machine Learning Research. He has secured competitive research funding, including the NSF CAREER award, supporting his long-term research agenda. While specific advisees are not listed, he mentors graduate students through his lab and departmental roles.

He leads the Computer Vision Lab at UMass Amherst, a research group focused on advancing the state of the art in visual understanding through machine learning. The lab is part of the broader research ecosystem within CICS and collaborates with the Center for Data Science, contributing to interdisciplinary efforts in AI and data-driven science.