Two Professors Win 2016 NSF CAREER Awards

CAREER grants are the NSF’s most prestigious award in support of the early career-development activities of junior faculty who most effectively integrate research and education within the context of the mission of their organization.

—Photo by Eileen Barroso

Prof. Matei Ciocarlie focuses on developing versatile manipulation and mobility in robotics, in particular on building dexterity into robotic hands. He will use the CAREER award to explore the science of manipulation, resulting in better tools to assess the mechanisms and methods for planning and executing manipulation tasks. He hopes that by optimizing hand designs jointly with planning and control algorithms, using combined quality metrics, he will be able to build versatile mechanisms that are ready for use right after assembly, and already programmed for a wide range of useful tasks. His concept is a departure from the so-called “build, then program” paradigm, where the hardware and software components of a manipulator are developed either sequentially or independently. His group is working on a range of applications that are part of everyday life, from versatile automation in manufacturing and logistics to mobile manipulation in unstructured environments to assistive and rehabilitation robotics in health care.

—Photo by Ryan John Lee

Prof. Nima Mesgarani’s CAREER project will explore a new research direction for his group: computational modeling of neural networks. The recent parallel breakthroughs in deep neural network models and neuroimaging techniques have significantly advanced the current state of artificial and biological computing. However, there has been little interaction between these two disciplines, resulting in simplistic models of neural systems with limited prediction, learning, and generalization abilities. Mesgarani’s goal is to create a coherent theoretical and mathematical framework to understand the computational role of distinctive features of biological neural networks, their contribution to the formation of robust signal representations, and to model and integrate them into the current artificial neural networks. These new bio-inspired models and algorithms will have adaptive and cognitive abilities, will better predict experimental observations, and will advance the knowledge of how the brain processes speech. In addition, the performance of these models should approach human abilities in tasks mimicking cognitive functions, and will motivate new experiments that can further impose realistic constraints on the models.

 —by Holly Evarts

Original article can be found here.

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