Arulselvan Madhavan
About Arulselvan Madhavan
Arulselvan Madhavan is a Machine Learning Engineer at Lightmatter in San Francisco, California, where he has worked since 2022. He has a background in software engineering with experience at companies like Chartboost and Intuit, and holds a Master's degree in Computer Science from Northeastern University.
Work at Lightmatter
Arulselvan Madhavan has been employed at Lightmatter as a Machine Learning Engineer since 2022. In this role, he focuses on deep learning and machine learning applications. His work is based in San Francisco, California, where he has contributed to various projects that leverage advanced computational techniques.
Previous Experience in Software Engineering
Prior to his current position, Madhavan worked at Chartboost as a Senior Software Engineer from 2018 to 2022. He also held the position of Software Engineer at Chartboost for 9 months in 2018. His earlier roles included Software Engineer Intern and Software Engineer Co-op at Intuit, where he worked on projects related to QuickBase and gained foundational experience in software development.
Educational Background
Madhavan earned a Master of Science (M.S.) in Computer Science from Northeastern University, studying from 2014 to 2016. He also holds a Bachelor of Engineering (B.E.) in Electronics and Communication Engineering from Valliammai Engineering College, where he studied from 2007 to 2011. His academic background has provided him with a strong foundation in computer science and engineering principles.
Specializations and Interests
Arulselvan Madhavan specializes in distributed computing and machine learning. He has a keen interest in functional programming, type theory, and category theory, which informs his approach to software development and engineering challenges.
Volunteer and Teaching Experience
Madhavan has contributed to the academic community as a Volunteer Programmer at Northeastern University, where he worked on various programming projects. He also served as a Graduate Teaching Assistant for Information Retrieval, assisting in the instruction of graduate-level courses.