Khushbu Goel
About Khushbu Goel
Khushbu Goel is a Senior Software Engineer at Blaize, specializing in neural network optimization and performance benchmarking. She holds a Master's Degree in Computer Science from the International Institute of Information Technology and a B.Tech in Computer Science and Engineering from Guru Gobind Singh Indraprastha University.
Work at Blaize
Khushbu Goel has been employed at Blaize as a Senior Software Engineer since 2019. In this role, she focuses on writing various neural network layers and optimizing them for performance. She has designed a neural network framework based on the ONNX specification, which is utilized for performance benchmarking and supporting internal projects. Additionally, she developed a BLAS Library aimed at solving linear algebra problems, specifically optimized for Blaize's parallel architecture.
Education and Expertise
Khushbu Goel holds a Master’s Degree in Computer Science from the International Institute of Information Technology, where she studied from 2014 to 2016. Prior to this, she earned a B.Tech in Computer Science and Engineering from Guru Gobind Singh Indraprastha University, completing her studies from 2009 to 2013. She possesses strong skills in programming languages such as C and C++, as well as in data structures, algorithms, and computer organization.
Background
Khushbu Goel has a solid academic foundation in computer science, having completed both her undergraduate and graduate studies in the field. Her educational journey includes a focus on computer science and engineering, equipping her with the necessary knowledge and skills for her career in software engineering. She has been active in the tech industry since completing her education.
Technical Contributions
In her role at Blaize, Khushbu Goel has made significant technical contributions. She has focused on the development of neural network layers and their optimization, which is critical for enhancing performance in machine learning applications. Her work includes the design of a neural network framework aligned with the ONNX specification and the creation of a BLAS Library tailored for linear algebra tasks, optimized for the company's parallel architecture.