Thomas Asikis
Integrating machine learning, automation, and control forms a powerful alliance to tackle the intricate challenges modern societies face in their daily operations and long-term planning.
Thomas Asikis is a researcher specializing in integrating machine learning, optimization, game theory, and control into social, financial, and governance systems. Currently a postdoc in Game Theory at UZH, he actively contributes to the "Applications of Reinforcement Learning and Control on Markets" project. Thomas bridges machine learning research and practical applications with a Doctoral degree from ETH Zurich and industry experience, notably as a Senior Deep Learning Engineer at Aisot Technologies. His interests span computer science, information systems, and optimization algorithms, reflecting a holistic approach to complex challenges. Beyond technical skills, Thomas values social interaction, viewing it as essential for personal and professional growth.
