活动

Organizers: Shenzhen Research Institute of Big Data (深圳市大数据研究院 in Chinese)

                     Shenzhen International Center for Industrial and Applied Mathematics (深圳国际工业与应用数学中心 in Chinese)

Speaker: Dr. Fan Zhang

Language: English

Talk title: Large-Scale Optimizations in ICT and Several Practical and Theoretical Problems

Talk abstract: Large-scale optimization has played an important role in practical information and communication technology (ICT) scenarios. Even though most of the practical optimizations are linear programming or mixed integer programming, the problem size could be over one million to even one billion, which makes such optimizations very challenging under the stringent requirement of solution time. In this talk, practical ICT scenarios covering optimizations in data communication and optical networks will be covered and several theoretical and practical problems will be introduced. The problems are from the following three perspectives: routing optimization under practical limitations, AI-aided optimization, and exploration of MILP lower bound. This talk serves as a platform to announce these open questions to public and we welcome anyone who is interested to work with us to conquer the challenges.

Bio: Fan Zhang (Michael) is currently with Hong Kong Theory Lab, Huawei Hong Kong Research Center as an optimization expert. He received the BEng (first class Hons) degree from Chu Kochen Honors College, Zhejiang University (ZJU), in 2010, and the Ph.D. degree from the Hong Kong University of Science and Technology (HKUST), in 2015. He joined Huawei Future Network Theory lab as a researcher in 2015 and is an optimization expert in Theory Lab since 2022. His research interest covers a wide range including large-scale optimization techniques, operational research with applications to networking problems, low complexity stochastic optimization, and networked control theory. Michael is now leading a diversified team with people from different backgrounds working on challenging and hardcore optimization problems originated from practical scenarios such as wide area network, optical network, wireless network, and data storage systems. He and his team has gained great experiences in designing customized algorithms for efficiently solving practical large-scale LPs/ILPs with number of variables or constraints to be over 1 million to billion. Some research outputs directly translate to solving practical problems, leading to increasing revenue or reducing costs, while some contribute to the development of Huawei’s generic solver (OptVerse) to enhance its performance.

Time: 10:00 – 11:00 AM, Jan. 9^th, 2023 （Beijing time）

Zoom Meeting ID: 993 5540 7029 (Passcode: 551804)