I am a Leidos Technical Fellow supporting my company and client’s R&D and strategy challenges in the applied energy domain from the subsurface, material, to next-gen computing. My work is cross-disciplinary, collaborating with world-class teams from universities, national labs, and companies.
I enjoyed working on strategic planning and development. I spearheaded the value proposition of establishing a Science-based AI/ML Institute (SAMI) to prepare the National Energy Technology Laboratory to develop, deploy, and maintain AI/ML capabilities. SAMI was awarded and established to combine the knowledge of domain experts and AI/ML engineers to advance technologies across fields.
It is also exciting to work on R&D projects to advance technologies and applications. In the Science-Informed Machine Learning to Accelerate Real-Time Decisions (SMART) initiative, the team I led successfully developed a CO₂ saturation prediction model reaching 5,000x faster than the traditional simulation time. It is a significant improvement to make real-time and virtual learning possible.
Chung Shih, Ph.D., GISP
Achievements
Solve grand challenges through strategic planning, proposal efforts, and engagements.
Lead teams to develop artificial intelligence and machine learning R&D projects in various fields.
Directed teams and orchestrated analysis in carbon management, oil & gas, and applied energy fields.