DAC Ph.D. student Sneha Mehta is an intern this summer at Netflix headquarters in Los Gatos, California.

Sneha Mehta has been selected to attend the Deep Learning and Reinforcement Learning Summer School (DLRLSS), from July 24 to August 2, in Edmonton, Alberta, Canada.

Mehta is a Ph.D. student in computer science at the Discovery Analytics Center, advised by Naren Ramakrishnan. In May, she began a summer internship as a data scientist at Netflix headquarters in Los Gatos, California, where she is researching novel methods to improve machine translation for subtitles.

“I applied to the Summer School because deep learning and reinforcement learning are very relevant to my work at problem solving both at DAC and at Netflix,” said Mehta. “Hearing directly from some of the pioneers in the field will be a great – and invaluable – experience.”

The 2019 DRLSS is hosted by the Canadian Institute For Advanced Research (CIFAR) and the Alberta Machine Intelligence Institute (Amii), with participation and support from the Vector Institute and the Institut québécois d’intelligence artificielle (Mila).

The program brings graduate students, post-docs, and professionals together to cover the foundational research, new developments, and real-world applications of deep learning and reinforcement learning.

Mehta will present a poster of her collaborative work at DAC, Event Detection using Hierarchical Multi-Aspect Attention which she presented at The Web Conference in May.

“The Summer School also provides a great opportunity to network with Ph.D. students, postdocs and industry professionals from all over the world who apply these technologies to a variety of fields, including recommendation systems, physics, 3D printing, marine biology, natural language processing, computer graphics, medical image analysis, neuroscience, epidemics, computer vision, and drug discovery,” Mehta said.

At the conclusion of the program, Mehta will continue her internship at Netflix until August 14, and then return to Virginia Tech for the Fall semester.