Osonde A. Osoba

Photo of Osonde Osoba
Co-Director, Center for Scalable Computing and Analysis; Professor, Pardee RAND Graduate School; Information Scientist;
Santa Monica Office


Ph.D. & M.S. in electrical engineering, University of Southern California; B.S. in electrical and computer engineering, University of Rochester

Media Resources

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Osonde Osoba (pronounced “oh-shOwn-day aw-shAw-bah”) is an information scientist at the RAND Corporation and a professor at the Pardee RAND Graduate School. He has a background in the design and optimization of machine learning algorithms. He has applied his machine learning expertise to diverse policy areas such as health, defense, and technology policy. His more recent focus has been on data privacy and fairness in artificial intelligence and algorithmic systems more generally. Osoba also serves as a co-director for RAND's center for Scalable Computing and Analysis (SCAN).

Prior to joining RAND, he was a researcher at the University of Southern California (USC). His research there focused on improving the speed and robustness of popular statistical algorithms like the expectation-maximization (EM) and backpropagation algorithms used in applications like automatic speech recognition. He also made contributions on the robustness and accuracy of approximate Bayesian inference schemes. Osoba received his Ph.D. in electrical engineering from USC and his B.S. in electrical and computer engineering from the University of Rochester.

Selected Publications

Osonde A. Osoba, Bart Kosko, "Fuzzy Cognitive Maps of Public Support for Insurgency and Terrorism," Journal of Defense Modeling and Simulation, 14(1), 2017

K. Audhkhasi, O. Osoba, and B. Kosko, "Noise-Enhanced Convolutional Neural Networks," Neural Networks, 78, 2016

Osonde Osoba, Bart Kosko, "Noise-enhanced clustering and competitive learning algorithms," Neural Networks, 37, 2013

Osonde Osoba, Sanya Mitaim, and Bart Kosko., "The noisy expectation–maximization algorithm," Fluctuation and Noise Letters, 12(03), 2013


  • Face detection and recognition

    Keeping Artificial Intelligence Accountable to Humans

    Artificial intelligence (AI) systems are often only as intelligent and fair as the data used to train them. To enable AI that frees humans from bias instead of reinforcing it, experts and regulators must think more deeply not only about what AI can do, but what it should do—and then teach it how.

    Aug 20, 2018 TechCrunch

  • Osonde Osoba in a RAND panel discussion in Pittsburgh, Pennsylvania, February 20, 2018

    The Human Side of Artificial Intelligence: Q&A with Osonde Osoba

    Osonde Osoba has been exploring AI since age 15. He says it's less about the intelligence and more about being able to capture how humans think. He is developing AI to improve planning and is also studying fairness in algorithmic decisionmaking in insurance pricing and criminal justice.

    May 1, 2018

  • Digital silhouettes of people

    Rethinking Data Privacy

    Society benefits from the exchange of large-scale data in many ways. Anonymization is the usual mechanism for addressing the privacy of data subjects. Unfortunately, anonymization is broken.

    Oct 5, 2016 Inside Sources