Olga Russakovsky is an associate professor of computer science at Princeton University, associated with Stanford University and Carnegie Mellon University.^[17] In 2015, she completed her PhD thesis "Scaling Up Object Detection" at Stanford University advised by Fei-Fei Li (http://vision.stanford.edu/documents/Russakovsky_PhD_thesis_2015.pdf). Her research investigates computer vision and machine learning.^[1,2,6] She was one of the leaders of the ImageNet Large Scale Visual Recognition challenge and has been recognised by MIT Technology Review as one of the world's top young innovators.

Russakovsky is Ukrainian-American. She studied mathematics at Stanford University and remained there for her doctoral studies.^[3] When she finished her undergraduate degree, she had dismissed computer science, felt disconnected from research, and was the only woman in her laboratory.^[4] Then Fei-Fei Li arrived at Stanford.

Russakovsky eventually completed her PhD in computer vision in 2015, during which she worked with Fei-Fei Li on image classification. She developed an algorithm that could separate selected objects from the background, which made her acutely aware of human bias. She worked on mechanisms to reduce the burden of image classification on human annotators, by asking fewer, and more generalised, questions about the images being inspected.^[5] Together with Fei-Fei Li, Russakovsky developed ImageNet, a database of millions of images that is now widely used in computer vision.

After her PhD, she was a postdoctoral research fellow at Carnegie Mellon University.^[3] Russakovsky works on computer vision and machine learning, and serves as an associate professor of computer science at Princeton University.^[1,2,6]

Her research has investigated the historical and societal bias within visual recognition and the development of computational solutions that promote algorithmic fairness.^[8,9,7] For example, in 2015, a new photo identification application developed by Google labeled a black couple as "gorillas", at a time when only 2% of their workforce were African American.^[5] Russakovsky has emphasised that whilst the workforces designing artificial intelligence systems are not diverse enough, only improving the diversity of computer scientists will not be sufficient for rectifying algorithmic bias.^[5,10,13] Instead, she has involved training deep learning models that de-correlate protected characteristics such as race or gender. In 2019 she was awarded a Schmidt DataX grant to study accuracy in image captioning systems.^[11]

Russakovsky has been involved in several initiatives to improve access to computer science and public understanding of artificial intelligence.^[12] She serves on the board of the AI4ALL foundation, which looks to improve diversity in artificial intelligence. As part of AI4ALL, Russakovsky led a summer camp for high school girls, running the first camp in 2015 named the Stanford Artificial Intelligence Laboratory's Outreach Summer Program (SAILORS).^[14,15] The summer camp looks to keep bias out of artificial intelligence by educating people from diverse backgrounds about computer science, machine learning, and policy.^[19,18] By 2018, it had expanded into six other US campuses.^[16] She has launched similar initiatives at Princeton University.

Russakovsky's awards and honours include being named one of the Foreign Policy 100 Leading Global Thinkers in 2015 and receiving the PAMI Everingham Prize in 2016. She was also named to the MIT Technology Review 35 under 35 in 2017 and received the Anita Borg Early Career Award (BECA) in 2020.

Russakovsky is the lead author of "Imagenet large scale visual recognition challenge", which was published in the International Journal of Computer Vision in 2015. The paper describes the creation of a publicly available dataset of millions of images of everyday objects and scenes, as well as its use in an annual competition between the visual recognition algorithms of participating institutions. It discusses the challenges of creating such a large dataset, the developments in algorithmic object classification and detection that resulted from the competition, and the state of the object recognition field at the time of publication. According to the journal website, the article has been cited over 5,000 times, while according to Google Scholar, which includes citations of the article's pre-print on arXiv, it has been cited over 13,000 times in total.^[21,20,22,1] According to Google Scholar, Russakovsky is also the author of more than 20 other academic articles, six of which have been cited more than 100 times each.