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Osonde A. Osoba

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Engineer; Professor, Pardee RAND Graduate School
Santa Monica Office

Education

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

Media Resources

This researcher is available for interviews.

To arrange an interview, contact the RAND Office of Media Relations at (310) 451-6913, or email media@rand.org.

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Overview

Osonde Osoba (pronounced “oh-shOwn-day aw-shAw-bah”) is an engineer 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 accountability in algorithmic systems and artificial intelligence.

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 University of Rochester.

Research Focus

Pardee RAND Graduate School Courses

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(0), 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

Commentary

  • 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

Publications

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