Piotr Dollar
Research Scientist
Organization
FAIR
Position
Research Scientist
Intelligence Briefing
Piotr Dollar is a prominent researcher in the field of computer vision, known for his contributions to object detection and image segmentation, particularly through the development of the Mask R-CNN framework.
With a strong academic background, Piotr Dollar completed his undergraduate studies at Harvard University and earned his PhD from UC San Diego. He has worked at Microsoft and is currently a research scientist at Facebook AI Research (FAIR), where he focuses on advancing the state of the art in computer vision technologies.
BS, — Harvard University
PhD, — UC San Diego
Operational History
Mask R-CNN Paper Published
Publication of the Mask R-CNN paper, which introduced a framework for object instance segmentation.
researchMicrosoft Research
Joined Microsoft Research as a researcher focusing on computer vision.
careerPhD Completion
Completed PhD at UC San Diego.
careerResearch at UC San Diego
Conducted research in computer vision and machine learning.
researchUndergraduate Degree
Graduated with a BS from Harvard University.
careerAGI Position Assessment
Unknown
Primarily research-focused public profile; no detailed standalone safety doctrine is documented here.
Primarily research-focused public profile; no detailed standalone safety doctrine is documented here.
Intercepted Communications
“The future of computer vision is about understanding the world in a more human-like way.”
“Mask R-CNN has changed the way we approach instance segmentation.”
“Advancements in AI must be matched with ethical considerations.”
“Deep learning has revolutionized the field of computer vision.”
“Collaboration is key to advancing technology in computer vision.”
Research Output
Mask R-CNN
2017arXiv
Introduced a new framework for object instance segmentation.
Object Detection with Deep Learning: A Review
2016IEEE Transactions on Pattern Analysis and Machine Intelligence
Provided a comprehensive review of deep learning techniques for object detection.
Visual Recognition with Deep Learning
2016Nature
Discussed advancements in visual recognition using deep learning.
Deep Residual Learning for Image Recognition
2015CVPR
Pioneered the use of residual networks in deep learning.
Fast R-CNN
2015ICCV
Improved the speed and accuracy of object detection.
Learning to Segment Object Candidates
2015CVPR
Proposed a method for segmenting object candidates in images.
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
2014CVPR
Introduced the concept of feature hierarchies for object detection.
Deformable Part Models are Convolutional Neural Networks
2014CVPR
Showed the relationship between deformable part models and CNNs.
Field Intelligence
The Future of Computer Vision
Advancements in AI and Ethics
Mask R-CNN: A Breakthrough in Object Detection
Deep Learning for Computer Vision
Collaboration in AI Research
Known Associates
Kaiming He
collaboratorCo-authored several influential papers in computer vision.
View Dossier →Ross B. Girshick
collaboratorWorked together on the development of Mask R-CNN and other object detection frameworks.
View Dossier →Georgia Gkioxari
collaboratorCo-authored key research papers in the field of object detection.
View Dossier →P. Felzenszwalb
collaboratorCollaborated on research related to deformable part models.
View Dossier →Organizational Affiliations
Current
FAIR
Research Scientist
2016-Present
Former
Microsoft
Researcher
2016
UC San Diego
PhD Student
2011-2015
Dossier last updated: 2026-03-04