Indian-American Computer Scientist Anshumali Shrivastava Wins NSF CAREER Award
An Indian-American computer scientist, Anshumali Shrivastava has won National Science Foundation (NSF)'s prestigious CAREER award for his research on redesigning current machine-learning processes. He is an Assistant Professor, Department of Computer Science at William Marsh Rice University, commonly referred to as Rice University or Rice which is a private research university located in Houston, Texas. Anshumali Shrivastava was one of the recipients of the CAREER awards given to about 400 scholars each year across all disciplines to support the research and educational development of young scholars likely to become leaders in their fields.
Anshumali Shrivastava hopes to come up with clever algorithmic strategies to enable faster, more scalable computations required by big data and machine-learning technologies, thanks to this CAREER Award from the National Science Foundation, says a release from the university.
Faculty Early Career Development Program (CAREER) awards support the research and educational development of young scholars likely to become leaders in their fields. The five-year grants, which are among the most competitive awarded by the NSF, are given to some 400 scholars each year across all disciplines.
"My research leverages the existing algorithmic advances for pushing machine learning to the extreme scale," said Shrivastava, an assistant professor of computer science who has joint appointments in electrical and computer engineering and in statistics.
"I design 'hashing and sketching algorithms,' a class of randomized algorithms that can process humongous datasets in seconds.
"Most of the machine-learning algorithms still in use were developed from the 1960s through the 1980s," he said.
"They were not designed with computational complexity in mind. They focused on finding the 'right' measurements from the data. Most of the measurements are now quite expensive to compute. With big data, we're realizing that standard techniques fail to address new constraints of computations, energy, memory and other resources." he said.
Shrivastava earned an integrated M.S. and B.S. in mathematics and computing from the Indian Institute of Technology, Kharagpur, in 2008 and a Ph.D. in computer science from Cornell University in 2015, the same year he joined the Rice faculty.
"Can we redesign current machine-learning processes that rely only on operations that are efficient and do not affect the outputs significantly? That is the central question in my research," he said.
(With Inputs from PTI)
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