Speaker: Wen Song, Shandong University, China
Date: 17 April 2025
Time: 4:00 - 5:00pm (Beijing Time, UTC+8)
Wen Song received his Bachelor’s and Master’s degrees from Shandong University, China, and the PhD degree from Nanyang Technological University, Singapore. He is currently an Associate Professor with Shandong University, China. His research interests include artificial intelligence, combinatorial optimization, planning and scheduling. His work on learning driven scheduling received the 2024 IEEE TII outstanding paper reward. He served as an Area Chair/Senior PC for top conferences such as ICML, ICLR and AAAI. He is an IEEE Senior Member.
Combinatorial optimization is one of the core technologies for intelligent decision-making. Traditional solution algorithms rely on expert experience, often entailing high development costs and relatively poor adaptability. Deep learning driven combinatorial optimization methods, leveraging the powerful representation learning capabilities of deep neural networks, aim to automatically acquire problem-specific knowledge from historical data in a data-driven manner to guide the solution process, offering the potential to overcome the limitations of traditional approaches. This talk will introduce the fundamental principles and main paradigms of this emerging research hotspot, along with the speaker's latest advances in the field and some insights into future directions.
The Webinar went very successful, with ~50 participants.
The video recording of the Webinar can be found here.
The slides of the Webinar can be found here.
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