David Jonathan Miller

David Jonathan Miller is a Professor in Electrical Engineering with a focus on Machine Learning, Neural Networks, and Security in AI. His work addresses critical challenges in adversarial machine learning, including backdoor attacks, data poisoning, and Trojan mitigation.

• Active Grants: National Science Foundation (EAGER: Clustering & Generative Modeling of 3D Object Representations, 2024-2026; SBIR Phase I: Certification of DNNs Against Backdoor Attacks, 2022-2023).
• Key Collaborations: Co-PI with George Kesidis on multiple NSF grants.

Research Trends: Recent publications emphasize robust deep learning techniques, including activation clipping, mixture models, and maximum margin statistics to counteract adversarial threats. His work aligns with SDG-related efforts to secure cyber-physical systems.

Scientific Output: Over 200 research articles and 10 grants, with 3,297 citations and an h-index of 29, reflecting sustained contributions to machine learning security since 1992.