Engineer, Rand Corporation; Professor, Pardee RAND Graduate School
Osonde Osoba 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.
