理论中心前沿系列讲座 | 线上讲座：智能启发式是计算的未来
2023-10-25 | 作者：微软亚洲研究院
微软亚洲研究院理论中心前沿系列讲座第十四期，将于 10 月 30 日（周一）上午 10:30 - 11:30 与你相见。
本期，我们请到了南加利福尼亚大学计算机科学与数学系的教授滕尚华，带来以 “‘Intelligent Heuristics Are the Future of Computing’” 为主题的讲座分享，欢迎通过 Teams 参会！
理论中心前沿系列讲座是微软亚洲研究院的常设系列讲座，将邀请全球站在理论研究前沿的研究者介绍他们的研究发现，主题涵盖大数据、人工智能以及其他相关领域的理论进展。讲座以线上直播与线下研讨的形式呈现，通过这一系列讲座，我们期待与各位一起探索当前理论研究的前沿发现，并建立一个活跃的理论研究社区。
欢迎对理论研究感兴趣的老师同学们参与讲座并加入社区（加入方式见后文），共同推动理论研究进步，加强跨学科研究合作，助力打破 AI 发展瓶颈，实现计算机技术实质性发展！
参加方式
欢迎您通过 Teams 参会并与讲者互动
会议链接：https://b.d4t.cn/WGUNKj
会议 ID：230 503 468 958
会议密码：57GCv6
会议时间：10 月 30 日（周一）上午 10:30 - 11:30
讲座信息
Shang-Hua Teng is a University Professor and Seely G. Mudd Professor of Computer Science and Mathematics at USC. He is a fellow of SIAM, ACM, and Alfred P. Sloan Foundation, and has twice won the Gödel Prize, first in 2008, for developing smoothed analysis, and then in 2015, for designing the breakthrough scalable Laplacian solver. Citing him as, “one of the most original theoretical computer scientists in the world”, the Simons Foundation named him a 2014 Simons Investigator to pursue long-term curiosity-driven fundamental research. He also received the 2009 Fulkerson Prize, 2021 ACM STOC Test of Time Award (for smoothed analysis), 2022 ACM SIGecom Test of Time Award (for settling the complexity of computing a Nash equilibrium), 2011 ACM STOC Best Paper Award (for improving maximum-flow minimum-cut algorithms), and 2023 Science & Technology Award for Overseas Chinese from the China Computer Federation. In addition, he and collaborators developed the first optimal well-shaped Delaunay mesh generation algorithms for arbitrary three-dimensional domains, settled the Rousseeuw-Hubert regression-depth conjecture in robust statistics, and resolved two long-standing complexity-theoretical questions regarding the Sprague-Grundy theorem in combinatorial game theory. For his industry work with Xerox, NASA, Intel, IBM, Akamai, and Microsoft, he received fifteen patents in areas including compiler optimization, Internet technology, and social networks. Dedicated to teaching his daughter to speak Chinese as the sole Chinese-speaking parent in an otherwise English-speaking family and environment, he has also become fascinated with children's bilingual learning.
报告题目：
“Intelligent Heuristics Are the Future of Computing”
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.
上期讲座回顾
在上期讲座中，来自牛津大学数学系的 Terry Lyons 教授带来了以 “The Mathematics of Complex Streamed Data” 为主题的讲座分享，探讨复杂流数据的数学奥秘，介绍粗糙路径理论的内涵及现实应用。
上期讲座内容回顾：
若想了解往期讲座详情，请访问：
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关于微软亚洲研究院理论中心
2021 年 12 月，微软亚洲研究院理论中心正式成立，期待通过搭建国际学术交流与合作枢纽，促进理论研究与大数据和人工智能技术的深度融合，在推动理论研究进步的同时，加强跨学科研究合作，助力打破 AI 发展瓶颈，实现计算机技术实质性发展。目前，理论中心已经汇集了微软亚洲研究院内部不同团队和研究背景的成员，聚焦于解决包括深度学习、强化学习、动力系统学习和数据驱动优化等领域的基础性问题。
想了解关于理论中心的更多信息，请访问
https://www.microsoft.com/en-us/research/group/msr-asia-theory-center/
