-
The program is designed for academics from universities around the globe who want to work on large-scale technical challenges while continuing to teach and conduct research at their universities. -
The AVA program is aimed at pre-tenure to newly-tenured academics who seek to apply research methods to tackle complex technical challenges while continuing their university work.
Our academics
Meet some of our Scholars and Visiting Academics at Amazon.
-
Amazon Scholar
Columbia University -
Amazon Visiting Academic
Dartmouth College -
Amazon Scholar
University of British Columbia -
Amazon Scholar
University of Maryland -
Amazon Visiting Academic
University of Chile -
Amazon Scholar
Caltech
Watch some of the latest videos featuring our Academics.
Up next
On now
Academics discuss computer vision at Amazon | CVPR 2021
On now
Amazon Scholars at KDD 2021
On now
Alexa & Friends with Amazon Scholar Julia Hirschberg
On now
Women in conversational AI panel discussion
Academics discuss computer vision at Amazon | CVPR 2021
During the annual conference on Computer Vision and Pattern Recognition (CVPR 2021), Amazon Scholars and Amazon Visiting Academics held a panel discussion on computer vision at Amazon.
I love developing algorithms and building systems that work in every-day products. Being an Amazon Scholar allows me to do this at a scale far beyond my university role.
Thorsten Joachims, Professor of Computer Science/ML, Cornell University
Research stories
View allLearn more about the work our Academics are doing at Amazon.
-
August 13, 2021Amazon Scholar Chandan Reddy on the trends he sees in knowledge discovery research and their implications for his own work.
-
July 15, 2021Hirschberg explains why mastering empathetic speech is critical for successful dialogue systems.
-
Courtesy of Radhika Nagpal, Harvard UniversityJuly 06, 2021Radhika Nagpal has created robots that can build towers without anyone in charge. Now she’s turned her focus to fulfillment center robots.
-
June 22, 2021Scientists describe the use of privacy-preserving machine learning to address privacy challenges in XGBoost training and prediction.
