Professor LAU Hoong Chuin, Singapore Management University
Title: Machine Learning meets Combinatorial Optimization. Real World Applications in Routing and Scheduling
Traditionally Machine Learning belongs to the field of AI, while Combinatorial Optimization is a key area in Operations Research. The line is blurring today as the communities come together to tackle new problems in both disciplines. In this talk, Professor Lau Hoong Chuin will discuss his recent works on tackling dynamic planning problems arising in urban settings – last-mile logistics and emergency response.
Professor TOJO, Satoshi, Associate Professor of School of Information Science at JAIST (1995-2000)
Title: Can AI acquire creativity ?
The development of tonal music has once completed 100–200 years ago. The established style at that time was an emotional melody accompanied by harmonic chords, that is exactly the current style of popular music. This means that we still cannot escape from our old traditions. In this lecture, Professor TOJO Satoshi put on airs of Hilbert (1862–1943), who posed 10 unsolved mathematical problems in a conference in Paris in 1900 to show a future direction of mathematics.
Professor Su Nguyen, Senior Lecturer in Business Analytics & AI and Algorithm Lead at the Centre for Data Analytics and Cognition (CDAC), La Trobe University, Australia
Title: Machine Learning in Business Optimisation
The complexity of optimisation models has increased significantly because of new sophisticated business models and the ever-changing digital landscape. Solving optimisation models quickly and accurately is challenging. To tackle these challenges, recent research has explored the applications of machine learning to design or enhance optimisation algorithms. Dr. Su Nguyen will provide an overview of this emerging field and discuss different ways in which machine learning can support optimisation.
Professor Nitesh V. Chawla, Frank Freimann Professor of Computer Science & Engineering, University of Notre Dame
Professor Nitesh Chawla is passionate about data science for the common good.
His research is making fundamental advances in artificial intelligence, data science, and network science, and is motivated by the question of how technology can advance the common good through interdisciplinary research. His research is not only at the frontier of fundamental methods and algorithms but is also making interdisciplinary advances through collaborations with faculty at the University of Notre Dame and community, national, and international partners.