金融工程研究中心学术报告：Intelligent Heuristics Are the Future of Computing

报 告 人： 滕尚华  教授 报告时间：2024年4月2日9:30-11:30 报告地点：苏州大学本部览秀楼105学术报告厅   报告摘要: Back in 1988, the partial game trees explored by computer chess programs were among the largest search structures in real-world computing. Because the game tree is too large to be fully evaluated, chess programs must make heuristic strategic decisions based on partial information, making it an illustrative subject for teaching AI search. In one of his lectures that year on AI search for games and puzzles, Professor Hans Berliner — a pioneer of computer chess programs — stated:   “Intelligent heuristics are the future of computing.” As a student in the field of the theory of computation, I was naturally perplexed but fascinated by this perspective. I had been trained to believe that “Algorithms and computational complexity theory are the foundation of computer science.” However, as it happens, my attempts to understand heuristics in computing have subsequently played a significant role in my career as a theoretical computer scientist. I have come to realize that Berliner’s postulation is a far-reaching worldview, particularly in the age of big, rich, complex, and multifaceted data and models, when computing has ubiquitous interactions with science, engineering, humanity, and society. In this talk, I will share some of my experiences on the subject of heuristics in computing, presenting examples of theoretical attempts to understand the behavior of heuristics on real data, as well as efforts to design practical heuristics with desirable theoretical characterizations. My hope is that these theoretical insights from past heuristics — such as spectral partitioning, multilevel methods, evolutionary algorithms, and simplex methods — can shed light on and further inspire a deeper understanding of the current and future techniques in AI and data mining.     报告人简介：滕尚华 (Shang-Hua Teng) 是南加州大学计算机科学和数学系教授兼 Seely G. Mudd 教授。 他是 SIAM、ACM 和 Alfred P. Sloan 基金会的会员，并两次获得哥德尔奖，第一次是在 2008 年，因开发平滑分析，然后在 2015 年，因设计突破性的可扩展拉普拉斯求解器。 西蒙斯基金会称他为“世界上最具原创性的理论计算机科学家之一”，并任命他为 2014 年西蒙斯研究员，以从事长期好奇心驱动的基础研究。 他还获得了2009年富克森奖、2023年中国计算机学会华侨科技奖、2022年ACM SIGecom时间测试奖（解决计算纳什均衡的复杂性）、2021年ACM STOC时间测试奖（解决纳什均衡的复杂性）、2021年ACM STOC时间测试奖（ 平滑分析），2020 年 Phi Kappa Phi 教师认可奖（2020 年），因其著作《数据和网络分析的可扩展算法》，2011 年 ACM STOC 最佳论文奖（改进最大流最小割算法）。 此外，他和合作者开发了第一个针对任意三维域的最佳形状良好的 Delaunay 网格生成算法，解决了鲁棒统计中的 Rousseeuw-Hubert 回归深度猜想，并解决了两个长期存在的关于 Sprague 的复杂性理论问题 -组合博弈论中的格伦迪定理。 由于他与 Xerox、NASA、Intel、IBM、Akamai 和 Microsoft 的行业合作，他在编译器优化、互联网技术和社交网络等领域获得了 15 项专利。 作为英语家庭和环境中唯一说中文的父母，他致力于教女儿说中文，他也对孩子的双语学习着迷。