Harvard University
UC San Diego
Alexander KirillovMember of Technical Staff · Thinking Machines LabResearcher↗
Researcher
Piotr Dollár
Thinking Machines LabTechnical Staff
Dollár helped shape computer vision by co-creating COCO, a shared test for finding and describing objects in images.[1] He co-developed focal loss, which helps models focus on hard examples during training[2], and contributed to Segment Anything, which can isolate objects in new images without additional training.[3]
Career & education
Current roleThinking Machines Lab2026-presentTechnical Staff
Thinking Machines Lab2026-presentPreviously
Meta2014-2025Research Director
Microsoft2011-2014Researcher
Caltech2007-2011Postdoc
Meta2014-2025Microsoft2011-2014Anchovi Labs2011-2012
Co-founderCaltech2007-2011Education
Work and education history is primarily focused on AI-relevant roles and may not be comprehensive.
Citation Trend
Total citations to scholarly work, including research papers, technical reports, books and theses, as recorded by Google Scholar. Each point is a dated snapshot.
citations393,278As of Nov 1, '26
citations+9,090Since last snapshot, Oct 1, '26
Selected contributions
Microsoft COCO: Common Objects in Context
With Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona and others ·A dataset of everyday scenes with individual objects labeled and outlined.
Focal Loss for Dense Object Detection
Focal loss shifts object-detector training toward difficult examples rather than easy ones.
Mask R-CNN
Mask R-CNN identifies objects and traces each one's outline in an image.
References
- Microsoft COCO: Common Objects in Contextarxiv.org
Dollár helped shape computer vision by co-creating COCO, a shared test for finding and describing objects in images.
- Focal Loss for Dense Object Detectionarxiv.org
He co-developed focal loss, which helps models focus on hard examples during training
- Segment Anythingarxiv.org
and contributed to Segment Anything, which can isolate objects in new images without additional training.
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Reviewed August 8, 2026 · Checked for accuracy and new information.
