Optimization algorithms have been used extensively in machine learning, operations research, and decision sciences. The goal of optimization is to determine the optimal or near-optimal solutions for computational problems such as finding weights and architecture of an artificial neural network, designing a supply chain network, and allocating jobs and machines in a factory. In this presentation, Dr Su Nguyen will cover a number of fundamental issues in optimization such as problem formulation and solution methods, and introduce Evolutionary Computation, a flexible approach to dealing with complex and large-scale optimization problems. He will also discuss some practical applications and recent advances in the field of optimization and evolutionary computation.
About Dr. Su Nguyen,
Su Nguyen received the B.E. degree in Industrial and Systems Engineering from the Ho Chi Minh City University of Technology, Vietnam, in 2006, the M.E. degree in Industrial Engineering and Management from the Asian Institute of Technology (AIT), Bangkok, Thailand, in 2008, and the Ph.D. degree in Artificial Intelligence and Data Analytics from Victoria University of Wellington (VUW), Wellington, New Zealand, in 2013. He is currently a David Myers Research Fellow attached to the Centre for Research in Data Analytics and Cognition, La Trobe University, Australia.
Su Nguyen has taken different research positions focusing on quantitative methods for operations management. He was a Research Associate in Industrial and Manufacturing Engineering at the School of Engineering and Technology, AIT from 2009 to 2010 and the Research Assistant at VUW from 2011 to 2013. He was a postdoctoral research fellow at VUW from 2013 to 2016, focusing on automated design of production scheduling heuristics. From 2014 to 2016, he was also the lecturer at International University, VNU-HCMC and Hoa Sen University in Vietnam.
His primary research interests include computational intelligence, optimization, machine learning, large-scale simulation, and their applications in operations management and social media analytics. His current research is focusing on combining the power of advanced machine learning and optimisation methods to tackle challenging real-world problems. He is the guest editor of the special issue on “Automated Design and Adaption of Heuristics for Scheduling and Combinatorial Optimization”, published in Genetic Programming and Evolvable Machines, and the co-organizer of top conferences in the field. He is also the reviewer of high-quality journals in computational intelligence, operations research, and production/transportation research.
