Gopikrishna Rathinavel, DAC M.S. student in the Department of Computer Science

Gopikrishna Rathinavel was introduced to machine learning through the biotechnology courses he took as an undergraduate.

“Eager to learn more, I began attending lectures by industry experts in machine learning,” Rathinavel said. “Soon I was captivated by the potential that machine learning offers as a discipline. It can add valuable insights in any domain where there is some data to exploit.”

He also began following the work of Discovery Analytics Center Director Naren Ramakrishnan, who is now his advisor, and other Virginia Tech faculty.

“What intrigued me the most was the use of satellite images of hospital parking lots to monitor disease trends,” he said. “It was novel research and something that I was keen to learn more about.”

Rathinavel graduated from the Indian Institute of Technology (IIT) in Madras with a dual bachelor and master’s degree in technology and worked as a software engineer for four years before starting the master’s program in Virginia Tech’s Department of Computer Science in Fall 2019 and joining DAC.

“Cross collaboration and plenty of pioneering research between departments at Virginia Tech means more opportunities to tie in my knowledge of biotechnology with machine learning,” said Rathinavel.

“At DAC I am able to work on a number of projects in different fields like systems biology, social media analytics, and urban computing that tackle global-scale problems,” he said. “This provides a unique experience.”

Another plus in being part of the DAC community, Rathinavel said, is that everyone has something to offer. “It is a very natural learning atmosphere where we share our knowledge and are always ready to lend a helping hand and support each other.”

His current research includes generating predictive models based on events that occurred in the past, using data extracted from news articles relevant to specific corporate events such as business contracts, cyberattacks, and executive resignations.

“Patterns and anomalies found from these events could go a long way in helping experts in the industry make informed decisions,” he said.

Projected to graduate in 2021, he would like to work in industry in a research role that utilizes machine learning.