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Tutor profile: Santhosh K.

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Santhosh K.
Incoming Google SWE | Masters in CSE at Indian Institute of Technology Madras
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Bio

About Santhosh

About Santhosh
Hello. I'm a final year Computer Science Masters student from the Indian Institute of Technology Madras, currently the class topper in my batch. I will be graduating and joining Google in July 2021. I have been trained rigorously in Programming and Math since high school. I've also worked as a teaching assistant for courses like Approximation Algorithms and stats for Data Science. I am a competitive coding hobbyist with a CodeForces rating of 2196 (Master). I've also undertaken several courses, projects and internships in DL, ML and NLP. I am...

Teaching Experience

Teaching Experience
I have worked as a teaching assistant for the following Masters level courses at Indian Institute of Technology Madras. 1) Approximation Algorithms (July-Nov 2020) 2) Statistics for Data Science (Jan-May 2021) I have also conducted several sessions for college juniors as part of the Programming Club and Computer Vision Club of the institute.
Education
Indian Institute of Technology, Madras
Computer Science and Engineering
Master's Degree
2016 - 2021

Work Experience

Work Experience
Rubrik Inc., Software Engineering Intern
2021 - 2021
• Built a support tool capable of detecting the presence of software hotpatches on a cloud cluster. Written using Ansible and python. • Implemented a Jenkins automated job to regularly produce an updated version of the tool compatible with newer software releases. • Decreased occurrences of potential upgrade regressions by incorporating the tool as a pre-check in the upgrade pipeline. • Received a Pre-Placement Offer based on performance during the internship.
Adobe, Deep Learning Research Intern
2020 - 2020
• Worked on incorporating emotional context into an end-to-end deep learning based text-to-speech systems. U.S. patent application filed under the title ”A Method for Expressive Text-to-Speech using Contextual Word-level Style Tokens”. • Implemented various improvements to the Tacotron 2 architecture on pytorch and trained them on single and multi-speaker datasets. • Human evaluations demonstrate the architecture’s capacity to capture emotional contexts like happy, sad, surprised, angry, etc.

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