Arda Sahiner
About Arda Sahiner
Arda Sahiner is the Co-Founder and CTO of Arcus, where he focuses on building data infrastructure for large language models. He holds a PhD in Machine Learning from Stanford University and a Bachelor of Science in Electrical Engineering and Computer Science from the University of California, Berkeley.
Work at Arcus
Arda Sahiner serves as the Co-Founder and Chief Technology Officer (CTO) at Arcus since 2023. In this role, he focuses on building data infrastructure specifically designed for large language models (LLMs). His expertise in this area contributes to the development of advanced technologies that enhance data processing and model performance.
Education and Expertise
Arda Sahiner holds a Bachelor of Science degree in Electrical Engineering and Computer Science from the University of California, Berkeley, where he studied from 2015 to 2019. He furthered his education at Stanford University, earning a Doctor of Philosophy (PhD) in Machine Learning from 2019 to 2023. His academic background provides a solid foundation for his work in technology and research.
Background
Arda Sahiner completed his high school education at Thomas S. Wootton High School, graduating with a diploma in 2015. He began his academic journey at the University of California, Berkeley, where he also worked as a Teaching Assistant and Research Assistant from 2016 to 2019. His experience at Stanford University as a Graduate Student Researcher from 2019 to 2023 further developed his research skills.
Internship Experience
During his academic career, Arda Sahiner gained practical experience through internships. He worked as a Software Engineering Intern at UnitedMasters in 2017 for three months and as a Machine Learning Research Engineer Intern at Scale AI in 2022 for three months. These roles provided him with hands-on experience in software development and machine learning applications.
Research Focus
Arda Sahiner has conducted research aimed at enhancing the robustness, reliability, and interpretability of neural networks. His work in this area contributes to advancements in machine learning and artificial intelligence, particularly in improving the performance and understanding of complex models.