Hassan Karim

About

Hassan Karim is a leading researcher at the intersection of machine learning security and cyber-physical systems, with a primary focus on developing trustworthy AI for multi-domain operations. His most significant contribution is the introduction of SFMLOps (Security Framework for Machine Learning Operations), a comprehensive and novel approach to securing MLOps pipelines in complex operational environments. This framework, detailed in his highly cited 2023 work "End-to-end trustworthy ML for multidomain operations," provides a systematic methodology for benchmarking security in mobile cyber-physical systems, including applications such as quadruped reconnaissance robots. By addressing the critical challenge of end-to-end trustworthiness in ML pipelines, Karim's work has established foundational principles for deploying secure AI in defense and robotics contexts. His research bridges the gap between theoretical security frameworks and practical implementation in autonomous systems, making him a key figure in the emerging field of secure MLOps. Karim's contributions are particularly valuable for researchers and engineers working on deploying machine learning in high-stakes, multi-domain environments where security and reliability are paramount.

Research Focus

Key Achievements

Top Papers

1. 1
   End-to-end trustworthy ML for multidomain operations

   2 citations · 2023

Key Collaborators

Contact & Links