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UW CSE alum Brandon Lucia profiled in “People of ACM”

Brandon LuciaThe latest edition of the Association for Computing Machinery’s “People of ACM” features a great conversation with UW CSE alum Brandon Lucia (Ph.D., ’13), now a member of the faculty at Carnegie Mellon University. “People of ACM” is a regular feature that highlights members whose personal and professional stories serve as an inspiration to the broader computing community and whose work is helping to advance computing as a science and as a profession.

As a Ph.D. student, Lucia worked with UW CSE professor Luis Ceze on research that spanned computer architecture, systems, and programming languages. For his dissertation, he developed new concurrency debugging techniques for concurrent and parallel software. The ACM profile focused on Lucia’s work on intermittent energy-harvesting computer systems and a related programming language, Chain, that his group will field-test on a tiny satellite circling in low Earth orbit sometime this year — and Lucia can hardly wait.

“Beyond the fact that sending things to space is cool, we are excited to see the scientific results of our deployment,” Lucia told the ACM. “Our satellite will send back invaluable reliability and energy profiles that uniquely characterize our Chain application in its actual orbital environment.”

When asked to predict what other areas would see major advances, Lucia predicted we would see new, dense, non-volatile memory technologies integrated with heterogeneous computing components using 3-D stacked fabrication, which he characterized as a disruption that could yield order of magnitude improvement in power and performance. Longer term, Lucia anticipates major advances in alternative computing technology.

“Biological computing and data storage are coming into their own, but with only the most basic programming interfaces and execution models with which to reason about a system’s behavior,” Lucia noted. “The behavior of a biological embedding — in DNA or protein networks — of today’s most sophisticated deep neural learning models yields a level of complexity that is beyond our current ability to reason.”

“One compelling future research problem is to define the programming and behavioral abstractions, system architectures, and behavioral specification techniques that enable future biological programmers to direct such stochastic, biological systems to carry out such complex computations,” he said.

Read the full article here.

Nice work, Brandon!

January 25, 2017