美国斯坦福大学管理科学与工程系及计算数学工程研究院李国鼎讲座教授
简介：
Yinyu Ye is currently the K.T. Li Professor of Engineering at Department of Management Science and Engineering and Institute of Computational and Mathematical Engineering, Stanford University. His current research topics include Continuous and Discrete Optimization, Data Science and Applications, Numerical Algorithm Design and Analyses, Algorithmic Game/Market Equilibrium. Operations Research and Management Science etc.; and he was one of the pioneers of Interior-Point Methods, Conic Linear Programming, Distributionally Robust Optimization, Online Linear Programming and Learning, Algorithm Analyses for Reinforcement Learning and Markov Decision Process, and etc. He has received several scientific awards including, including the 2009
John von Neumann Theory Prize
for fundamental sustained contributions to theory in Operations Research and the Management Sciences, the inaugural 2012
ISMP Tseng Lectureship Prize
for outstanding contribution to continuous optimization (every three years), the 2014
SIAM Optimization Prize
awarded (every three years), etc.. According to Google Scholar, his publications have been cited 58,000 times.
叶荫宇 (Yinyu Ye) 现任斯坦福大学管理科学与工程系及计算数学工程研究院李国鼎讲座教授。他的主要研究方向为连续和离散优化, 数据科学及应用， 数字算法设计及分析，算法博弈及市场均衡，运筹及管理科学等；他和其他科学家开创了内点优化算法，锥规划模型，分布式鲁棒优化，在线线性规划和学习，强化学习和马可夫过程算法分析等。他多次获得科学奖项: 包括2009约翰.冯.洛伊曼理论奖，国际数学规划2012 Tseng Lectureship Prize（每三年），2014美国应用数学学会优化奖（每三年）等。根据谷歌学术统计，目前他的文章被引用总计超过58,000次。
演讲标题：
AI and Mathematical Optimization: New progress in solving huge-scale mathematical programs and applications
摘要：
In recent years, with the rapid increase in the scale of data from real-world problems and AI training tasks, the corresponding decision-making and computational problems have also become larger and larger. The scale of some linear programs (in terms of non-zero elements) has reached the tens of billions, while the matrix size of some semi-define programs has reached hundreds of millions. These problems often also require high-precision solutions. In this presentation, we discuss some recent frontier explorations in response to these challenges, particularly advancements in first-order methods, interior-point algorithms, and fast solutions for large-scale problems under the GPU/CUDA architecture. In addition, we describe how to integrate the AI Large Language Models and OR Optimization Solvers and tackle some real-world applications.
摘要：
Graph neural networks have been found successful in solving combinatorial optimization problems. This talk starts with a simple example of solving the travelling salesman problem using graph neural networks. Then, we present an approach to multi-objective facility location using two graph neural networks with supervised training. Finally, we showcase how a graph neural network with negative message passing can be trained using unsupervised training for solving graph coloring problems. We conclude the talk with a summary and discussion of future work.
叶杰平
阿里云智能集团
副总裁
简介：
美国明尼苏达大学博士，IEEE Fellow，曾任美国密西根大学教授。曾担任多个国际顶级期刊编委及国际顶级会议程序委员会主席和领域主席，发表高质量学术论文近400篇 (H-index: 103)。曾先后荣获美国国家自然科学基金会生涯奖、CCF科学技术奖科技进步一等奖、国际运筹学领域顶级实践奖-瓦格纳运筹学杰出实践奖、KDD China技术转化奖、以及多个国际顶级会议最佳论文奖等。
演讲标题：
Large Language Models: An In-Depth Mechanism Analysis and Applications
摘要：
Large Language Models (LLMs) have emerged as pivotal tools in natural language processing, transforming the way machines understand, generate, and interact with human language. This presentation aims to explore the inner workings of LLMs, offering a comprehensive analysis of their underlying mechanisms, and showcasing their applications in multiple tasks.
研究成果发表于ANOR、COR、EJOR、MSOM、OR Spectrum、TRB、TRC、TRE、TS等运筹学和交通科学领域学术期刊。曾任美国运筹学与管理学研究协会（INFORMS）交通科学与物流分会（TSL）秘书长和INFORMS Journal on Computing（IJOC）期刊副编（AE）。现任Transportation Science（TS）期刊副编和Transportation Research Part E（TRE）期刊编委。
演讲标题：
Thoughts on the Design and Implementation of Column Generation Method for Crew Pairing Optimization
摘要：
In a forum intended for OR professionals, the topics are mostly on math models, algorithms and their applications. Through the example of crew pairing optimization,the purpose of this talk is to shed light on the design and implementation of optimization systems, or specifically the column generation method with a rule engine to encapsulate the business logics, so that the system can be easily set up to solve problems of great variation in business rules, and therefore serve for business purpose beyond just finding an optimization solution.
袁晓明
香港大学数学系教授
简介：
研究方向为优化算法与理论、云计算、最优控制、人工智能。Clarivate Analytics 高被引学者。带领香港大学与华为云的研究队伍进入2023年INFORMS Franz Edelman Award决赛。与华为云合作项目被评为2023年华为公司“公司级优秀合作项目奖”。
