Jacob
Seidman, PhD

Jacob Seidman leads the Core AI team at Reality Defender where he investigates strategies for robust model performance and algorithmic model failure identification. His previous research includes work on machine learning for functional data, physics-informed machine learning, and robust optimization. He holds a Ph.D. in applied mathematics and computational science from the University of Pennsylvania and an A.B. in mathematics from Harvard.