Osonde A. Osoba

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 associate 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 expertise to diverse policy topics such as epidemiology, defense acquisition & science 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.

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):17-32, 2017

K. Audhkhasi, O. Osoba, and B. Kosko, "Noise-Enhanced Convolutional Neural Networks," Neural Networks, 78:15-23, 2016

Osonde Osoba, Bart Kosko, "Noise-enhanced clustering and competitive learning algorithms," Neural Networks, 37:132-140, 2013

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

Commentary

  • 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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