Kalyanmoy Deb is University Distinguished Professor and Koenig Endowed Chair Professor at Department of Electrical and Computer Engineering in Michigan State University, USA. Prof. Deb's research interests are in computational intelligence and machine learning, focusing in multi-criterion optimization and decision-making. He received his Bachelor's degree in Mechanical Engineering from Indian Institute of Technology Kharagpur, India and master's and doctoral degrees from University of Alabama, Tuscaloosa, USA. He received IEEE Evolutionary Computation Pioneer Award, Infosys Prize, TWAS Prize in Engineering Sciences, CajAstur Mamdani Prize, Distinguished Alumni Award, Edgeworth-Pareto award, Bhatnagar Prize, and Bessel Research award from Germany. He is fellow of ACM, ASME and IEEE. He has published over 640 research papers with Google Scholar citation of over 239.000 with h-index 146. He is in the editorial board on 11 major international journals. More information about his research contribution can be found from https://www.coin-lab.org.

Title: Machine-based Approach for Benchmarking Multi-Criterion Decision-Making Procedures

Abstract: Multi-criterion optimization problems give rise to a set of Pareto-optimal (PO) solutions. To choose a single preferred PO solution, multi-criterion decision-making (MCDM) procedures involve human decision-makers (DMs) providing preference elicitation in an iterative manner. Human involvement in the MCDM procedures prohibited computational researchers to get attracted to the field. In this talk, we shall introduce a trained machine learning (ML) based system to provide preference information akin to human DMs and demonstrate its working on a number of multi-objective optimization problems. The basic idea will be augmented with a number of pragmatic extensions. The Machine-DM concept should motivate computationally motivated conference participants to develop new and novel MCDM procedures, a matter which was not possible in the past fifty years of studies in the MCDM field.

Prof. Simon See is the Global Head, NVIDIA AI Technology Centre, Singapore. He is also a renowned Professor and Chief Scientific Computing Officer in Shanghai Jiao Tong University. Prof. Simon See is also the Adjunct Professor of the Department of Computer Science & Engineering, Mahindra University. He is currently involved in a number of smart city projects, especially in Singapore and China. His research interests are in the area of High Performance Computing, Big Data, Artificial Intelligence, machine learning, computational science, Applied Mathematics and simulation methodology. Prof. See is also leading some of the AI initiatives in Asia Pacific.

He has published over 200 papers in these areas and has won numerous awards in the field. Prof. See is also a member of SIAM, IEEE and IET. He is also a committee member of more than 50 conferences.

Prof. See graduated from University of Salford (UK) with a PhD in electrical engineering and numerical analysis in 1993. Prior to joining NVIDIA, Dr. See worked for SGI, DSO National Laboratory of Singapore, IBM, International Simulation Ltd (UK), Sun Microsystems and Oracle. He is also providing consultancy to a number of national research and supercomputing centres.

Distinguished Professor Chin-Teng Lin is a leading researcher in artificial intelligence (AI) and brain computer interfaces (BCI), developing systems of brain information processing and communication with machines. Lin studies the brain and behaviours, the physiological changes that occur when human cognitive functions are working, and ways to combine human physiological information with artificial intelligence to develop monitoring and feedback systems. He wants to improve the flow of information from humans to robots, so humans can make better decisions and respond to complex, stressful situations, and so that robots can better understand the status and intention of humans to augment human-machine cooperation. This is an emerging trend identified in the fifth Industrial Revolution to deliver a common good for humanity. Lin was the inventor of fuzzy neural networks (FNN) in 1992, introducing neural-network learning into fuzzy systems and incorporating human-like reasoning into neural networks. Since then, there have been about 500,000 articles about FNN published online.

Lin has since developed a series of FNN models with various learning capabilities suitable for different learning environments, as well as targets on multi-agent reinforcement learning for multi-drone coordination and cybersecurity. Lin joined UTS in 2016 as Co-Director of the Australian AI Institute (AAII) to advance AI and BCI. A highly published researcher in the fields of machine-intelligent systems and brain computer interface, Lin is also the founding director of the Computational Intelligence and Brain Computer Interface Lab at the Australian AI Institute, at UTS. The Lab is developing mobile sensing technology to measure brain activity using non-invasive methods, to assess human cognitive states. Lin led a large-scale 10-year project ($10 million) on Cognition and Neuroergonomics (2010-20) with the US Army Research Lab. The project explored advanced BCI technologies by studying the effects of vehicle motion and cognitive fatigue, and developing wearable EEG devices.

Since joined UTS in 2016, Prof Lin had been granted 5 ARC Discovery projects, 2 NHRMC projects, 1 CRC-P project, and 2 Defence innovation Hub projects. He has also attracted quite a lot of industry funding. In 2020, a team of researchers led by Lin, embarked on a two-year, $1.2 million project with the Department of Defence to examine how cutting-edge technologies could use brainwaves to command and control autonomous vehicles, integrating cognition neuroscience and device engineering to develop wearable technology. The success of this project led to the next phase of 2.5 years, $3.8 million project in 2023 to mature the developed technology in the previous phase. Lin was Editor-in-Chief of IEEE Transactions on Fuzzy Systems (2011-16) and is on the board of governors of several IEEE societies. He was elevated to be an IEEE Fellow for his contributions to biologically-inspired information systems in 2005, and was elevated to International Fuzzy Systems Association (IFSA) Fellow in 2012. In 2017, he received the IEEE Fuzzy Pioneer Award. With a BS degree from the NCTU, Taiwan (1986) and Masters (1989) and PhD (1992) in electrical engineering from Purdue University in the US, Lin also holds academic positions at the University of California and University of Nottingham.

Pankaj Dayama is a Senior Technical Staff Member and Master Inventor at IBM Research India. He is currently leading Blockchain and AI for Industry Solutions group at IRL. He is leading the charter to build solutions for resource planning and decision optimization in the context of manufacturing at IBM Research. His research interests are broadly in the areas of AI, Optimization, Game Theory, and Applied Cryptography with specific focus on building innovative solutions for manufacturing and supply chain.

Pankaj holds a Ph.D. in Mechanism Design from Indian Institute of Science, Bangalore. His thesis topic was on “Mechanism Design for Strategic Crowds, Markets, and Networks”. He has published about 25 papers in peer reviewed conferences and journals in premier CS venues and he also has more than 70 patents filed in the USPTO.