Keith A. Brown

Keith A. Brown is an Associate Professor in Mechanical Engineering at Boston University's College of Engineering with additional appointments in Materials Science & Engineering and Physics. He serves as Associate Chair for Graduate Programs in ME and leads the interdisciplinary KABLab research group.

Education:

• PhD, Harvard University

Dr. Brown's research centers on hierarchical soft matter systems including polymers and smart fluids. His group develops innovative approaches to accelerate materials research through nanocombinatorics, autonomous experimentation, and scanning probe lithography. Key focus areas include connecting nanoparticle properties to bulk smart fluid behavior, designing 3D-printed structures with programmed mechanics, and creating self-driving laboratories for materials discovery.

His recent publications (2024-2025) demonstrate a strong emphasis on autonomous experimentation systems integrating machine learning with physical research. This work spans energy-absorbing foam design, nanoscale fluid manipulation, and physics-informed modeling for mechanical systems, establishing new paradigms in accelerated materials development.

Scientific Awards:

• The Early Career Research Excellence Award, College of Engineering, 2021
• Professor of the Year, Mechanical Engineering, 2020
• Frontiers of Materials Award, The Minerals Metals and Materials Society (TMS), 2020
• Dean’s Catalyst Award (2018)
• Dean’s Catalyst Award (2020)
• Moorman-Simon Interdisciplinary Career Development Professor, 2016

Dr. Brown teaches undergraduate courses including Fluid Mechanics (ME 303), Introduction to Materials (ME 306), and Nanomanufacturing (ME/MS 576). His research is supported by:

• Federal Grants: AFOSR MURI, NSF Nanomanufacturing, ACS Petroleum Research Fund
• Foundations: Gordon and Betty Moore Foundation
• Industry: Google Faculty Research Award
• University: BU Dean's Catalyst Award, Nanotechnology Innovation Center

The KABLab employs interdisciplinary teams to develop novel instrumentation for hierarchical soft matter research, with particular expertise in autonomous experimentation platforms that combine scanning probe techniques with machine learning for accelerated materials discovery.