Anshumali Shrivastava, faculty member in Rice University’s Department of Computer Science, has been promoted to associate professor with tenure, effective July 1, 2021. He also has appointments in the Department of Electrical and Computer Engineering and the Department of Statistics.
“Anshu is not a rising star in the department… he's already one of our biggest stars,” exclaimed Chris Jermaine, chair of the Department of Computer Science. “Although Anshu is widely known as an expert in machine learning—and he is—he's really an algorithmist. He's most well-known for his work on scaling machine learning computations using randomization.”
“Promoted and tenured faculty have met rigorous standards and have established national and international reputations in their respective fields,” said Rice Provost Reginald DesRoches in the award announcement.
Shrivastava’s areas of research, teaching and expertise also incorporate scalable and sustainable deep learning, big data and graphs, and social networks mining. His work has been described by his colleagues as “cutting edge” and noted for both its “depth and breadth.”
“I am excited,” says Shrivastava. “Tenure is a significant milestone in the academic career. I am fortunate to have a supportive family and incredible students; this would not be possible without them.
“I must say, I had a very smooth and pleasant experience as a junior faculty—thanks to my colleagues who repeatedly assured me that my progress is on the right track.”
“When you look at how rigorous the tenure process at Rice is and the high expectations of one of the top-most computer science departments in the country, it is comforting to know that I have landed safely on the other side,” Shrivastava continues. “I look forward to seeing what I end up doing in the future.”
In discussing the research, CS chair Jermaine explains how Shrivastava’s work asks the question: How does one develop novel randomized algorithms that can make the largest and most expensive machine learning computations run quickly and with less computational power?
“This is such an important research area going forward,” Jermaine says. “People increasingly realize that huge models trained over the largest data sets tend to be the most accurate, at the same time they realize the environmental costs of training such models using power-hungry compute clusters.”
As a researcher, Shrivastava has partnered with industry leaders such as Amazon and Intel. In April, he and his team presented his latest artificial intelligence (AI) research at MLSys, the 2021 machine learning systems conference. This presentation demonstrated AI software that runs on commodity processors and trains deep neural networks 15 times faster than platforms based on graphics processors.
Shrivastava’s research into probabilistic bloom filters trained with artificial intelligence also garnered significant attention this past Fall with its ability to help combat the spread of social media misinformation. This new approach to scanning social media applies machine learning in a smarter way to improve the performance of Bloom filters. His team showed their Adaptive Learned Bloom Filter (Ada-BF) required 50% less memory to achieve the same level of performance as learned Bloom filters.
Just last month, Shrivastava was awarded the 2021 Young Faculty Research Award. He was also the recipient of a 2021 Adobe Data Science Research Award. In addition to a number of other significant awards, in 2018, Science News named him one of 10 Scientists to Watch, and he received an NSF CAREER Award in 2017.
Shrivastava received his Ph.D. in computer science from Cornell in 2015. He joined Rice’s faculty the same year.