Real-world systems are composed of interconnected entities that collaborate to perform diverse yet interrelated tasks, often requiring sequential decision-making. Over the past decade, multi-task learning has emerged as a powerful paradigm for collaborative learning, significantly enhancing efficiency while enabling privacy-preserving knowledge sharing. One of the most promising approaches to learning-based control is dynamic sequential learning, such as reinforcement learning, which learns through interactions with the environment. However, reinforcement learning faces three critical challenges in real-world dynamical systems: data scarcity and heterogeneity, scalability and communication efficiency, and safety. Moreover, achieving provable guarantees in joint learning often requires leveraging underlying problem structures. This CAREER project will develop a unified approach to multi-task representation learning by leveraging the shared representations to offer a viable solution to these challenges, enabling privacy-preserved joint learning in dynamic environments. Research and education will be synergistically integrated to train students in the interdisciplinary field of data science and control, addressing the pressing need for skilled workforce development in this emerging area of societal importance. The central objective of this project is to develop provable methods for multi-task representation learning in bandit and reinforcement learning settings. At its core