Greg Mohler is the chief scientist of the Assured Software and Information Division in the GTRI CIPHER lab. His work focuses on the intersection of information theory and physical systems, with emphasis on deep neural networks, quantum information, and complexity theory. Mohler has led efforts on quantum machine learning and classical heuristics for combinatorial problems, fairness-based analysis of neural network robustness, novel machine learning implementations for circuit obfuscation, and synthetic data creation. Mohler is also well versed in applications of quantum information theory, particular quantum error correction, hardware-informed quantum resource estimation, and scalable quantum tomography. Previously, he worked in electromagnetic theory, focusing on analytical and computational modeling of nanoferromagnetic composites.