Olga Russakovsky

Last updated
Olga Russakovsky
Alma mater Stanford University
Known for ImageNet
Scientific career
Fields Computer vision
Machine learning [1]
Institutions Carnegie Mellon University
Princeton University
Thesis Scaling Up Object Detection  (2015)
Doctoral advisor Fei-Fei Li
Website www.cs.princeton.edu/~olgarus/ OOjs UI icon edit-ltr-progressive.svg

Olga Russakovsky is an assistant professor of computer science at Princeton University. Her research investigates computer vision and machine learning. [1] [2] 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.

Contents

Early life and education

Russakovsky studied mathematics at Stanford University and remained there for her doctoral studies. [3] When she finished her undergraduate degree she had dismissed computer science and felt disconnected from research and 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. [5] She developed an algorithm that could separate selected objects from the background, which made her acutely aware of human bias. [5] 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. [5] Russakovsky is Ukrainian-American.

Research and career

After her PhD, she was a postdoctoral research fellow at Carnegie Mellon University. [3] Russakovsky works on computer vision and machine learning. [6] She is an associate professor of computer science at Princeton University. [3] [7] Her research has investigated the historical and societal bias within visual recognition and the development of computational solutions that promote algorithmic fairness. [8] [9] For example, in 2015, a new photo identification application developed by Google labeled a black couple as "gorillas". [5] At the time 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] Instead, she has involved training deep learning models that de-correlate protected characteristics such as race or gender. [8] In 2019 she was awarded a Schmidt DataX grant to study accuracy in image captioning systems. [11]

Public engagement

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 AI4ALL foundation, which looks to improve diversity in artificial intelligence. [13] As part of AI4ALL Russakovsky led a summer camp for high school girls. [14] [15] She ran the first summer camp in 2015, named the Stanford Artificial Intelligence Laboratory's Outreach Summer Program (SAILORS). By 2018 it had expanded into six other US campuses. [16] [17] She has launched similar initiatives at Princeton University. [3] [18] 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]

Selected publications

Russakovsky is the lead author of Imagenet large scale visual recognition challenge, [20] 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, and its use in an annual competition between the visual recognition algorithms of participating institutions. The paper discusses the challenges of creating such a large dataset, the developments in algorithmic object classification and detection that have resulted from the competition, and the current (at time of publication) state of the object recognition field. According to the journal website, the article has been cited over 5,000 times. [21] According to Google Scholar, which includes citations of the pre-print of the article on arXiv, the article has been cited over 13,000 times in total. [22]

Russakovsky is the author of more than 20 other academic articles, six of which have been cited more than 100 times each, according to Google Scholar. [1]

Awards and honours

Russakovsky's awards and honours include:

Related Research Articles

Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is a field of study in computer science that develops and studies intelligent machines. Such machines may be called AIs.

Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, generative artificial neural networks have been able to surpass many previous approaches in performance.

Melanie Mitchell is an American scientist. She is the Davis Professor of Complexity at the Santa Fe Institute. Her major work has been in the areas of analogical reasoning, complex systems, genetic algorithms and cellular automata, and her publications in those fields are frequently cited.

Caltech 101 is a data set of digital images created in September 2003 and compiled by Fei-Fei Li, Marco Andreetto, Marc 'Aurelio Ranzato and Pietro Perona at the California Institute of Technology. It is intended to facilitate Computer Vision research and techniques and is most applicable to techniques involving image recognition classification and categorization. Caltech 101 contains a total of 9,146 images, split between 101 distinct object categories and a background category. Provided with the images are a set of annotations describing the outlines of each image, along with a Matlab script for viewing.

<span class="mw-page-title-main">Fei-Fei Li</span> Chinese American computer scientist (born 1976)

Fei-Fei Li is a China-born American computer scientist, known for establishing ImageNet, the dataset that enabled rapid advances in computer vision in the 2010s. She is Sequoia Capital professor of computer science at Stanford University and former board director at Twitter. Li is a co-director of the Stanford Institute for Human-Centered Artificial Intelligence and a co-director of the Stanford Vision and Learning Lab. She served as the director of the Stanford Artificial Intelligence Laboratory from 2013 to 2018.

<span class="mw-page-title-main">Hao Li</span> American computer scientist & university professor

Hao Li is a computer scientist, innovator, and entrepreneur from Germany, working in the fields of computer graphics and computer vision. He is co-founder and CEO of Pinscreen, Inc, as well as associate professor of computer vision at the Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI). He was previously a Distinguished Fellow at the University of California, Berkeley, an associate professor of computer science at the University of Southern California, and former director of the Vision and Graphics Lab at the USC Institute for Creative Technologies. He was also a visiting professor at Weta Digital and a research lead at Industrial Light & Magic / Lucasfilm.

The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. ImageNet contains more than 20,000 categories, with a typical category, such as "balloon" or "strawberry", consisting of several hundred images. The database of annotations of third-party image URLs is freely available directly from ImageNet, though the actual images are not owned by ImageNet. Since 2010, the ImageNet project runs an annual software contest, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), where software programs compete to correctly classify and detect objects and scenes. The challenge uses a "trimmed" list of one thousand non-overlapping classes.

<span class="mw-page-title-main">Joy Buolamwini</span> Computer scientist and digital activist

Joy Adowaa Buolamwini is a Ghanaian-American-Canadian computer scientist and digital activist based at the MIT Media Lab. She founded the Algorithmic Justice League (AJL), an organization that works to challenge bias in decision-making software, using art, advocacy, and research to highlight the social implications and harms of artificial intelligence (AI).

<span class="mw-page-title-main">Meredith Whittaker</span> American artificial intelligence research scientist

Meredith Whittaker is the president of the Signal Foundation and serves on their board of directors. She was formerly the Minderoo Research Professor at New York University (NYU), and the co-founder and faculty director of the AI Now Institute. She also served as a senior advisor on AI to Chair Lina Khan at the Federal Trade Commission. Whittaker was employed at Google for 13 years, where she founded Google's Open Research group and co-founded the M-Lab. In 2018, she was a core organizer of the Google Walkouts and resigned from the company in July 2019.

<span class="mw-page-title-main">Andrej Karpathy</span> Czechoslovak-born AI researcher (born 1986)

Andrej Karpathy is a Slovak-Canadian computer scientist who served as the director of artificial intelligence and Autopilot Vision at Tesla. He currently works for OpenAI, where he specializes in deep learning and computer vision.

Kristen Lorraine Grauman is a Professor of Computer Science at the University of Texas at Austin on leave as a research scientist at Facebook AI Research (FAIR). She works on computer vision and machine learning.

<span class="mw-page-title-main">Aude Oliva</span> French computer scientist

Aude Oliva is a French professor of computer vision, neuroscience, and human-computer interaction at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).

<span class="mw-page-title-main">Timnit Gebru</span> Computer scientist (born 1983)

Timnit Gebru is an Eritrean Ethiopian-born computer scientist who works in the fields of artificial intelligence (AI), algorithmic bias and data mining. She is an advocate for diversity in technology and co-founder of Black in AI, a community of Black researchers working in AI. She is the founder of the Distributed Artificial Intelligence Research Institute (DAIR).

<span class="mw-page-title-main">Cynthia Rudin</span> American computer scientist and statistician

Cynthia Diane Rudin is an American computer scientist and statistician specializing in machine learning and known for her work in interpretable machine learning. She is the director of the Interpretable Machine Learning Lab at Duke University, where she is a professor of computer science, electrical and computer engineering, statistical science, and biostatistics and bioinformatics. In 2022, she won the Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (AAAI) for her work on the importance of transparency for AI systems in high-risk domains.

<span class="mw-page-title-main">Algorithmic Justice League</span> Digital advocacy non-profit organization

The Algorithmic Justice League (AJL) is a digital advocacy non-profit organization based in Cambridge, Massachusetts. Founded in 2016 by computer scientist Joy Buolamwini, the AJL uses research, artwork, and policy advocacy to increase societal awareness regarding the use of artificial intelligence (AI) in society and the harms and biases that AI can pose to society. The AJL has engaged in a variety of open online seminars, media appearances, and tech advocacy initiatives to communicate information about bias in AI systems and promote industry and government action to mitigate against the creation and deployment of biased AI systems. In 2021, Fast Company named AJL as one of the 10 most innovative AI companies in the world.

<span class="mw-page-title-main">Rashida Richardson</span> American attorney and scholar

Rashida Richardson is a visiting scholar at Rutgers Law School and the Rutgers Institute for Information Policy and the Law and an attorney advisor to the Federal Trade Commission. She is also an assistant professor of law and political science at the Northeastern University School of Law and the Northeastern University Department of Political Science in the College of Social Sciences and Humanities.

<span class="mw-page-title-main">Black in AI</span> Technology research organization

Black in AI, formally called the Black in AI Workshop, is a technology research organization and affinity group, founded by computer scientists Timnit Gebru and Rediet Abebe in 2017. It started as a conference workshop, later pivoting into an organization. Black in AI increases the presence and inclusion of Black people in the field of artificial intelligence (AI) by creating space for sharing ideas, fostering collaborations, mentorship, and advocacy.

Abeba Birhane is an Ethiopian-born cognitive scientist who works at the intersection of complex adaptive systems, machine learning, algorithmic bias, and critical race studies. Birhane's work with Vinay Prabhu uncovered that large-scale image datasets commonly used to develop AI systems, including ImageNet and 80 Million Tiny Images, carried racist and misogynistic labels and offensive images. She has been recognized by VentureBeat as a top innovator in computer vision and named as one of the 100 most influential persons in AI 2023 by TIME magazine.

References

  1. 1 2 3 Olga Russakovsky publications indexed by Google Scholar OOjs UI icon edit-ltr-progressive.svg
  2. Olga Russakovsky at DBLP Bibliography Server OOjs UI icon edit-ltr-progressive.svg
  3. 1 2 3 4 "Olga Russakovsky". Computer Science Department. Princeton University. Retrieved 2019-11-24.
  4. "Fei-Fei Li's Quest to Make Machines Better for Humanity". Wired. ISSN   1059-1028 . Retrieved 2019-11-24.
  5. 1 2 3 4 5 6 7 "Making Smart Machines Fair". Princeton Alumni Weekly. 2018-05-25. Retrieved 2019-11-24.
  6. "Olga Russakovsky". www.cs.princeton.edu. Retrieved 2019-11-24.
  7. Naughton, John (2019-11-23). "To secure a safer future for AI, we need the benefit of a female perspective | John Naughton". The Guardian. ISSN   0261-3077 . Retrieved 2019-11-24.
  8. 1 2 Friday; April 19; to 12:00pm, 2019-11:00am (2019-01-04). "Spring 2019 GRASP Seminar Series: Olga Russakovsky, Princeton University, "Computer vision meets fairness"". GRASP lab. Retrieved 2019-11-24.{{cite web}}: CS1 maint: numeric names: authors list (link)
  9. Smith, Craig S. (2019-11-19). "Dealing With Bias in Artificial Intelligence". The New York Times. ISSN   0362-4331 . Retrieved 2019-11-24.
  10. Friction, Natasha Mitchell for Science (2017-08-11). "Alexa, Siri, Cortana: Our virtual assistants say a lot about sexism". ABC News. Retrieved 2019-11-24.
  11. "Schmidt DataX Fund supports research projects that harness data science to speed up discovery". Princeton University. Retrieved 2019-11-24.
  12. Russakovsky, Olga. "Most AI researchers are the same type of people. Here's why this is a terrible thing". MIT Technology Review. Retrieved 2019-11-24.
  13. Russakovsky, Olga (2018-05-02). "AI4ALL: AI will change the world, but who will change AI?". O’Reilly Media. Retrieved 2019-11-24.
  14. "Dr. Olga Russakovsky". AI4ALL. Retrieved 2019-11-24.
  15. "Meet the Innovators Under 35 - AI Bias Roundtable - MIT Technology Review". MIT Technology Review Events. Retrieved 2019-11-24.
  16. Smiley, Lauren (2018-05-23). "The Future of AI Depends on High-School Girls". The Atlantic. Retrieved 2019-11-24.
  17. "AI and the rise of a software-based economy". Financial Times. 5 July 2018. Retrieved 2019-11-24.
  18. "Olga Russakovsky Ph.D. | Princeton AI4ALL". ai4all.princeton.edu. Retrieved 2019-11-24.
  19. "Princeton program empowers youth to shape the future of artificial intelligence". Princeton University. Retrieved 2019-11-24.
  20. Russakovsky, Olga; Deng, Jia; Su, Hao; Krause, Jonathan; Satheesh, Sanjeev; Ma, Sean; Huang, Zhiheng; Karpathy, Andrej; Khosla, Aditya; Bernstein, Michael; Berg, Alexander; Fei-Fei, Li (2015). "ImageNet Large Scale Visual Recognition Challenge". International Journal of Computer Vision. 115 (3): 211–252. doi:10.1007/s11263-015-0816-y. hdl: 1721.1/104944 . S2CID   2930547.
  21. "International Journal of Computer Vision". Springer. doi:10.1007/s11263-015-0816-y. hdl: 1721.1/104944 . S2CID   2930547.{{cite journal}}: Cite journal requires |journal= (help)
  22. "Russakovsky: Imagenet large scale visual recognition challenge". Google Scholar. Retrieved 30 November 2019.
  23. "The Leading Global Thinkers of 2015 - Foreign Policy". 2015globalthinkers.foreignpolicy.com. Retrieved 2019-11-24.
  24. "Mark Everingham Prize". IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence. Retrieved 2019-11-24.
  25. "Olga Russakovsky | Innovators Under 35". www.innovatorsunder35.com. Retrieved 2019-11-24.
  26. "Anita Borg Award (BECA)".