Dr. Dimitrios Lambrinos
About Dr. Dimitrios Lambrinos
Dr. Dimitrios Lambrinos is an IT Business Architect at UBS AG in Zürich, Switzerland, with over 20 years of experience in the IT sector. He has a strong background in AI, machine learning, and software development, complemented by extensive academic qualifications including a PhD from the University of Zurich.
Work at UBS
Dr. Dimitrios Lambrinos has been employed at UBS AG as an IT Business Architect since 2016. In this role, he has contributed to various projects and initiatives within the organization, leveraging his extensive background in technology and business architecture. Prior to his current position, he served as a Director at UBS AG for 15 years, from 2001 to 2016. His long tenure at UBS highlights his deep understanding of the financial services sector and his ability to navigate complex IT landscapes.
Education and Expertise
Dr. Lambrinos possesses a strong educational background with a PhD from the University of Zurich, completed between 1993 and 1998. He also holds an MSc from the University of Sheffield, obtained in 1991, and a Diploma from the University of Ioannina, achieved in 1990. His expertise encompasses application performance management, infrastructure capacity management, and software development. He has specialized knowledge in compute and storage clouds, as well as a robust skill set in embedded systems.
Background
Dr. Lambrinos began his academic career at the University of Zurich, where he worked as a Research Associate and later as a Postdoctoral Researcher at the AILab from 1992 to 2001. His research focused on areas such as artificial intelligence, machine learning, robotics, and neuroscience. This foundation in advanced technology and research has informed his subsequent roles in the corporate sector, particularly in IT architecture and management.
Achievements
Throughout his career, Dr. Lambrinos has demonstrated a capacity for working with global interdisciplinary teams. His experience spans various domains, including application performance management and stress testing. His contributions to both academia and industry reflect a commitment to advancing technology and improving system performance. His work has involved significant projects that integrate his knowledge of AI and machine learning within business contexts.