Kourosh Hakhamaneshi
About Kourosh Hakhamaneshi
Kourosh Hakhamaneshi is a Team Lead in AI at Anyscale, where he focuses on server-less endpoints and deep neural networks. He holds a PhD in Electrical Engineering and Computer Science from the University of California, Berkeley, and has experience in reinforcement learning and large-scale training systems.
Current Role at Anyscale
Kourosh Hakhamaneshi currently serves as Team Lead (AI) at Anyscale, a position he has held since 2022. In this role, he leads engineering efforts for server-less Anyscale endpoints, focusing on the training and serving of deep neural networks. His work emphasizes leveraging Ray to enhance business growth through large language model (LLM) initiatives. Hakhamaneshi's expertise in AI and machine learning plays a crucial role in advancing Anyscale's technological capabilities.
Previous Experience at Anyscale
Before becoming Team Lead, Kourosh Hakhamaneshi worked as a Software Engineer specializing in Reinforcement Learning (RL) and Machine Learning (ML) at Anyscale for six months in 2022. His contributions during this period helped lay the groundwork for his current leadership role, allowing him to apply his knowledge in practical applications within the company.
Educational Background
Kourosh Hakhamaneshi holds a PhD in Electrical Engineering and Computer Science from the University of California, Berkeley, where he studied from 2017 to 2022. Prior to that, he earned a Bachelor of Science in Electrical and Electronics Engineering from Sharif University of Technology, completing his studies from 2012 to 2017. He also obtained a Bachelor's degree in Economics from Sharif University of Technology, studying from 2012 to 2016. This diverse educational background supports his expertise in AI and engineering.
Work Experience in Engineering
Kourosh Hakhamaneshi has a solid foundation in engineering, having worked as a Design Engineer at Blue Cheetah Analog Design, Inc. from 2019 to 2020. He also gained experience as a Research Intern at Intel Labs for two months in 2021. His roles have involved significant contributions to the development of large-scale training and inference systems for LLMs and Reinforcement Learning, as well as research in unsupervised learning applications in robotics and automated design during his PhD.