Nathan Drenkow

Nathan Drenkow is a senior research scientist whose work aims to develop novel methods to measure and mitigate robustness risks for artificial intelligence systems in high-stakes applications. His work draws from multiple disciplines, including computer vision, machine learning, causal inference, and neuroscience, and he has led numerous research, test, and evaluation efforts in these areas. He is also an instructor of deep learning for computer vision for the Johns Hopkins University Engineering for Professionals program.