In early December, Harsh Shrivastava became the first person to be awarded a doctorate in machine learning from Georgia Tech.
“It’s a special, happy feeling to be the first recipient of a ML Ph.D. degree. I am much obliged to my advisor, thesis committee members, friends and academic staff of Georgia Tech for their help and support throughout my Ph.D. years,” said Shrivastava.
Though machine learning has long been a research interest of the Institute, it wasn’t until 2016 that the Machine Learning Center at Georgia Tech (ML@GT) was created. The center began offering a doctorate program in 2017. As of Spring 2021, the interdisciplinary research center has grown to have more than 200 faculty members from all six colleges and 94 Ph.D. students. The center aims to research and develop innovative and sustainable technologies using machine learning and artificial intelligence (AI) that serve our community in socially and ethically responsible ways.
“The course structure of the ML Ph.D. program is very well designed to provide a good balance between the breadth and depth of knowledge required to pursue ML and related research. I really enjoyed meeting fellow grad students from various departments in our ML seminars and in the Coda workspace. The program helps foster a collaborative research environment at Georgia Tech,” said Shrivastava.
Shrivastava’s thesis research introduced two novel and generic techniques to design deep learning architectures that exist in natural language processing, healthcare, and computational biology.
“Harsh’s Ph.D. work breaks new ground in deep learning by biasing the learning in favor of prior knowledge specific to the domain or problem at hand, reducing the number of parameters and the training data needed to learn those parameters. I’m extremely proud of Harsh for his work at Georgia Tech and am excited for his bright career ahead of him,” said Srinivas Aluru, Shrivastava’s advisor and a professor in the School of Computational Science and Engineering.
With his Ph.D. in hand, Shrivastava will soon join Microsoft Redmond as a senior applied scientist where he will continue working on machine learning research to solve interesting real-world problems.
For his fellow machine learning classmates, he offers the following advice; “Everyone has a unique modus operandi to approach research problems. Stick to your own strategy instead of following someone else’s. I’d also try to do as many internships as possible to further broaden your perspective on solving research problems.”
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