May 18, 2023

The U.S. Department of Defense (DoD) recently announced the recipients of its Multidisciplinary University Research Initiative (MURI) awards for 2023. This year, MIT Department of Mechanical Engineering (MechE) professors George Barbasthasis and John Hart, MIT Department of Electrical Engineering and Computer Science (EECS) Assistant Professor Pulkit Agrawal, and MIT Department of Materials Science and Engineering Associate Professor Rob Macfarlane are principal investigators on projects selected for MURI Awards. Two others from MIT — Ila Fiete, a professor in the Department of Brain and Cognitive Sciences, and Aude Oliva, MIT director of the MIT–IBM Watson AI Lab, co-Lead of the MIT AI Hardware Program, director of strategic industry engagement in the MIT Schwarzman College of Computing, and a senior research scientist at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) — will be participating in these projects.

Fundamental limits of nanoscale X-ray microscopy in radiation-sensitive materials
George Barbasthasis

The project utilizes a novel approach for X-ray microscopy that enables higher resolution and dynamic studies by leveraging existing knowledge of specific objects, reducing harmful X-ray exposure; the aim is to apply this approach to investigate small machines, batteries, and cells, yielding valuable insights into battery function, sensor systems, and cellular responses to external factors.

Spatially programmed material properties via designed mesostructures
John Hart, Rob Macfarlane

The project aims to overcome the limitations of additive manufacturing by combining it with directed assembly at the nanoscale, using tailored building blocks and polymers, to achieve control over material architecture and ultimately create materials with enhanced thermal, electromagnetic, and optical properties for applications such as electronics cooling, communication systems, and high-performance cameras.

Neuro‐inspired distributed deep learning
Pulkit Agrawal, Ila Fiete, Aude Oliva

The project proposes an alternative approach to machine learning by avoiding data compression and instead combining data on-the-fly using memory retrieval, inspired by principles of how the brain encodes and retrieves information, aiming to improve generalization, lifelong learning, and integration of AI into real-world systems with practical algorithms for various tasks and sensory modalities.

Complete article from MIT News.