CLASSIC Modern Contemporary

"Classic/Modern Contemporary" research explores the interplay between established methods and cutting-edge advancements across diverse fields. Current work focuses on leveraging classical techniques to improve the efficiency and performance of modern machine learning models, such as neural networks and large language models, as well as applying these modern tools to analyze and reinterpret classical datasets and problems in areas ranging from art history to robotics. This interdisciplinary approach yields insights into both the strengths and limitations of existing methods, leading to more efficient, robust, and interpretable solutions with applications in various domains.