An Indian-American computer scientist has won National Science Foundation's prestigious CAREER award for his research on redesigning current machine-learning processes. 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. "My research leverages the existing algorithmic advances for pushing machine learning to the extreme scale," said Shrivastava, an assistant professor of computer science at Rice university.
Shrivastava hopes to come up with clever algorithmic strategies to enable faster, more scalable computations required by big data and machine-learning technologies. "I design 'hashing and sketching algorithms', a class of randomised 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 realising that standard techniques fail to address new constraints of computations, energy, memory and other resources," he said.
Shrivastava earned an integrated MS and BS in mathematics and computing from the Indian Institute of Technology, Kharagpur in 2008 and a PhD 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. CAREER awards are the National Science Foundation's most prestigious awards exemplifying the role of teacher-scholars through research, education and the integration of education and research. The award comes with a federal grant for research and education activities for five consecutive years
