Overview

Optimization is the backbone of modern scientific and engineering breakthroughs driving advancements in fields as diverse as machine learning, data science, an economics. This short course is designed to take graduate-level students on a captivating journey through the fascinating world of optimization, from its historical roots and foundational principles to cutting-edge techniques shaping today’s research frontiers in various fields.

We will explore the foundational concepts of convex optimization and gradually progress to advanced topics such as stochastic optimization, state-of-the-art acceleration methods, and distributed optimization, accompanied with examples from emerging applications in data science, machine learning, and beyond. Practical examples will complement the theoretical insights, offering participants a holistic and engaging learning experience. 

By the end of this course, participants will have developed a robust understanding of the vast and dynamic field of optimization. Equipped with deep insights, advance foundational knowledge, and practical tools, they will be prepared to explore and contribute to a wide range of research areas, including machine learning, economics, networking, and more. Through a blend of engaging theory and illustrative applications, this course will illuminate the relevance of optimization across disciplines and inspire students to explore innovative research frontiers. 

Join us to dive into the world of optimization and discover the boundless opportunities it unlocks to shape the future through research and innovation!