南山大学セミナー（２０２６年４月１４日（火））のご案内
hiru...@nanzan-u.ac.jp
IBISML研究会の皆様
重複してお受け取りの際はご容赦ください
南山大学の蛭川潤一と申します
開催が迫っての案内となりますが、以下の要領で、南山大学セミナー（２０２６年４月１４日（火））を開催いたします
海外ゲストによる講演を、２講演予定しております
是非、奮って、ご参加頂けましたら幸いです
事前申し込みは不要です
尚、講演会は対面形式で開催されます
オンラインでの配信は行いません
多くの皆様のご参加を、お待ちしております
--------------------------------------------------------------------
Nanzan Academic Seminar
Guest Lectures by
Dr. Thorsten Koch* and Dr. Ying Chen**
* Technische Universität Berlin
** National University of Singapore
Date: Tuesday, April 14th
Venue: Room S56, Building S, Nanzan University
Organizer: Takayuki Shiohama
-----------------------------------------------------------------
Program
13:35-13:40: Opening
13:40-15:10: Thorsten Koch
Title: Algorithmic Intelligence
Abstract: We are entering an era of algorithmic intelligence, in which computers routinely tackle tasks that once seemed to require human ingenuity. Daily headlines about advances in artificial intelligence and quantum computing can give the impression that “hard” problems are rapidly disappearing. In this talk, we examine what is meant by "hard-to-solve" in the context of combinatorial optimization, AI, GPUs, QUBO, and quantum computing. We present results from concrete applications, including Steiner tree problems in graphs and real-world gas network optimization. We conclude by reflecting on how emerging algorithmic paradigms and quantum computing may reshape our understanding of computational hardness in the years ahead.
15:10-15:30: Coffee Break
15:30-17:00: Ying Chen
Title: Inference and Decision-Making the Quantum-AI Era: An Algorithmic Intelligence Framework
Abstract: As we enter an era dominated by artificial intelligence and quantum computing, the role of applied math is more vital than ever — not only in making sense of data, but in shaping algorithms that drive intelligent decision-making under uncertainty. This talk presents an applied math lens on algorithmic intelligence, grounded in recent work on hybrid AI-quantum models, digital twins, and optimization in complex systems such as finance, healthcare, and logistics. I will share insights from applied collaborations involving AI-driven forecasting, reinforcement learning, and quantum circuit learning — highlighting how algorithmic intelligence thinking ensures robustness, interpretability, and transferability in modern learning systems.