摘要 Abstract 

本演講將概述我在計算機科學三個主要領域的研究經驗：圖最佳化演算法、密碼學，以及人工智慧相關演算法。雖然這些主題源自不同的應用領域，但它們皆由共同的理論基礎統一：演算法設計與計算複雜性。在圖最佳化方面，我們研究解決圖上的組合問題的高效演算法以及其基本計算極限。在密碼學方面，安全性建立於計算困難性以及高效攻擊不可行的基礎之上。在人工智慧領域，現代學習系統高度依賴大規模優化，並面臨新的計算效率與理論保證挑戰。透過這三個視角，本演講旨在說明演算法與複雜性理論如何作為統一框架，用於理解計算中的可行性、安全性以及智慧性。

This talk presents an overview of my research experience in three major areas of computer science: graph optimization algorithms, cryptography, and algorithms for artificial intelligence. Although these topics arise from different application domains, they are unified by a common theoretical foundation – algorithm design and computational complexity. In graph optimization, we study efficient algorithms and fundamental computational limits for solving combinatorial problems on graphs. In cryptography, security is built upon computational hardness and the infeasibility of efficient attacks. In artificial intelligence, modern learning systems rely heavily on large-scale optimization and face new challenges in computational efficiency and theoretical guarantees. Through these three perspectives, this talk aims to illustrate how algorithms and complexity theory serve as a unifying framework for understanding feasibility, security, and intelligence in computation.