K.T. Ramesh

Johns Hopkins University

Professor of Science & Engineering and Director, Hopkins Extreme Materials Institute (HEMI)

K.T. Ramesh, the Alonzo G. Decker, Jr. Professor of Science and Engineering at Johns Hopkins University, is the founding director of the Hopkins Extreme Materials Institute (HEMI). HEMI is dedicated to developing the science and technology needed to protect people, structures, and the planet during extreme events. Professor Ramesh is one of the world’s leading authorities on impact physics and extreme science. His current research focuses on AI in materials design, impact biomechanics including concussions, protection materials, hypersonics, the dynamic limits of life, and protecting the Earth from incoming asteroids. Ramesh is a professor in the Department of Mechanical Engineering, with appointments in the Department of Earth and Planetary Sciences and the Department of Materials Science and Engineering. He also is a member of the Principal Professional Staff at the Johns Hopkins Applied Physics Laboratory. He has written over 250 archival journal publications and is the author of the book “Nanomaterials: Mechanics and Mechanisms.” Professor Ramesh also has a particular interest in the ways in which creativity can be integrated into the sciences, arts, and engineering.

Sessions With K.T. Ramesh

Tuesday, 8 March

  • 01:30pm - 02:10pm (CST) / 08/mar/2022 07:30 pm - 08/mar/2022 08:10 pm

    Frontiers of AI: What's New at Johns Hopkins?
    Panel Digitalization/AI/Machine Learning/Robotics/Cybersecurity

    Artificial Intelligence (AI) is rapidly moving from the laboratory to application and promises to permeate every aspect of society and commerce. Recognizing the transformational aspects of this field, Johns Hopkins is making major internal investments to build programs around the assurance of AI, fundamental AI technologies, and domain-specific application. Core AI technologies function largely as black boxes, creating substantial risks in deployment—how should organizations think about these risks and manage them? Given that existing AI is built largely on data, what lessons are there to be learned for societal applications of AI from activities such as the COVID Portal?

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