Arun Chauhan
About Arun Chauhan
Arun Chauhan is a Software Engineer currently employed at SiMa.ai since 2023, with a background in academia and extensive experience in machine learning and compilers. He holds a PhD in Computer Science from Rice University and has previously served as an Assistant Professor at Indiana University Bloomington for nine years.
Work at SiMa.ai
Arun Chauhan has been employed as a Software Engineer at SiMa.ai since 2023. In this role, he focuses on the development and optimization of software solutions for machine learning applications. His work contributes to the company's mission of advancing machine learning technologies for edge devices.
Previous Academic Role
Prior to his current position, Arun Chauhan served as an Assistant Professor at Indiana University Bloomington from 2004 to 2013. During his nine years in this role, he engaged in teaching and research, contributing to the academic community and mentoring students in the field of computer science.
Education and Expertise
Arun Chauhan holds a Doctor of Philosophy (PhD) in Computer Science from Rice University. He also earned a Master of Technology (M.Tech.) in Computer Science and Engineering from the Indian Institute of Technology, Delhi, and a Bachelor of Technology (B.Tech.) in Electrical Engineering from the same institution. His educational background provides a strong foundation for his expertise in compilers and runtime systems, particularly in relation to machine learning accelerators.
Machine Learning Experience
Arun Chauhan has significant experience in productionizing machine learning on-device compilers across four generations of consumer devices. This experience highlights his technical skills and understanding of the complexities involved in deploying machine learning solutions in real-world applications.
Research Specialization
Arun Chauhan specializes in research related to compilers and runtime systems, with a particular focus on machine learning accelerators. His expertise in this area supports advancements in the efficiency and performance of machine learning applications.