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 at IBM Software Innovation Lab, where he leads the Industrial Automation mission. His work focuses on developing advanced techniques for time series analysis, tailored for industrial applications and IT operations (ITOps). He is passionate about leveraging technology to solve real-world problems, with a strong emphasis on validating innovations and driving their adoption into production systems.

Pankaj has authored over 30 research papers in leading international conferences and journals and holds more than 90 filed patents with the USPTO. Prior to joining IBM, he was a Research Scientist at General Motors R&D, India. He received his M.S. and Ph.D. in Computer Science from the Indian Institute of Science (IISc), Bangalore.

Pradeep Kumar Jilagam is Director of Systems Architecture & Workload Engineering for Mobile & Client Business Unit at Micron and Adjunct Faculty at BITS Pilani, Hyderabad. He has over 21 years of experience in semiconductor and systems engineering across mobile, client compute, data center, IoT, XR, automotive, and digital media platforms. His work spans AI mobile/edge inferencing, memory and storage, SoC architecture, hardware-software co-design, and system-level analysis across power, performance, memory, and thermal dimensions. His work has predominantly focused on driving next-generation memory and storage solutions, especially for edge AI, impacting product definition, ecosystem enablement, product launch, and thought leadership. He has also contributed to public technical thought leadership in AI systems and memory, including co-authoring Micron’s AI PC memory white paper. His interests include Edge AI, memory intelligence, hardware acceleration, system architecture, and industry-academia collaboration.

Pradeep holds a Masters from BITS Pilani and a Bachelors from the University campus of JNTU, Hyderabad, and has completed the Senior Executive Development Programme (CXO Programme) at XLRI Jamshedpur.

Title: Breaking the Edge AI Memory Wall: Architecting Intelligence Beyond Compute

Shalabh Bhatnagar received a Bachelor’s in Physics Hons from the University of Delhi in 1988, and his Master’s and Ph.D in Electrical Engineering from the Indian Institute of Science Bengaluru in 1992 and 1998. He was a Postdoctoral Fellow at the Institute for Systems Research, University of Maryland, USA, during 1997 to 2000 and at the Vrije Universiteit, Amsterdam, Netherlands, during 2000 to 2001. He joined the Indian Institute of Science in the Computer Science and Automation department in December 2001 where he became a Professor in June 2011 and a Professor (HAG) in 2021. In August 2025, he became the inaugural Prof. B.S. Sonde Chair Professor of the EECS Division at IISc.

His Research interests are in the theory of stochastic control, stochastic optimization, stochastic approximation algorithms, reinforcement learning as well as their various applications in science and engineering. He is a Fellow of the Institution for Electrical and Electronics Engineers (IEEE), USA; the International Academy of Artificial Intelligence Sciences (AAIS); the Indian National Science Academy (INSA); the Indian Academy of Sciences (IASc); the National Academy of Sciences, India (NASI); the Indian National Academy of Engineering (INAE). He was a J.C.Bose National Fellow and is now a J.C.Bose Research Grant Awardee. He has been an Associate Editor for IEEE Control Systems Letters, Systems and Control Letters, and the IEEE Transactions on Automation Science and Engineering.