Ed Raff

Dr. Edward Raff is a distinguished figure in the field of computer science, currently serving as a Chief Scientist at Booz Allen Hamilton. In addition, he holds a position as a visiting assistant professor in the Computer Science and Electrical Engineering department at the University of Maryland-Baltimore County (UMBC). His academic journey culminated in a Ph.D. in Computer Science in 2018, where he concentrated on malware detection and cybersecurity, areas that continue to be central to his research interests. Dr. Raff is the author of the forthcoming book "Inside Deep Learning," which is designed to be a practical guide for developers interested in deep learning, eschewing complex mathematical proofs in favor of accessible explanations. His work in deep learning is complemented by his extensive research in malware detection, reproducibility in machine learning, and the critical areas of fairness and bias detection in machine learning models. His expertise also extends to high-performance computing, making him a versatile researcher in the field. Throughout his career, Dr. Raff has contributed significantly to the academic community with over 40 peer-reviewed publications. His scholarly work has been recognized with three best paper awards, underscoring the impact and quality of his research. His commitment to advancing knowledge is also evident in his teaching roles at UMBC, where he has been actively involved in the Computer Science and Data Science programs. In his teaching, Dr. Raff engages students by integrating the development of his book and course materials into the curriculum, providing a dynamic and interactive learning experience. This approach not only enriches the students' understanding but also keeps the course content at the cutting edge of current research and industry practices. Dr. Raff's contributions to the field are not limited to academia; his role at Booz Allen Hamilton allows him to apply his research insights to real-world challenges, bridging the gap between theoretical research and practical application. His work continues to influence both the academic and professional landscapes, making him a key figure in the ongoing development of cybersecurity and deep learning technologies.