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.

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.

Title: Time Series Semantic Intelligence for Low‑Latency Streaming Data from Physical Assets

Abstract: Physical and industrial systems now operate at unprecedented speeds, continuously generating high‑frequency time‑series data from sensors, machines, and processes. Yet, much of the intelligence applied to this data remains downstream (after storage, aggregation, and delay) creating a growing gap between how fast data is produced and how quickly meaning can be derived and acted upon.

This talk introduces TS Semantic Intelligence, a paradigm shift in how intelligence is applied to streaming time‑series data from physical assets. By moving intelligence closer to where data is generated, shifting left in the data pipeline, we enable real‑time context, insight, and decision support for high‑speed, low‑latency operational environments. Using lightweight Granite Time‑Series Foundation Models, semantic understanding such as anomaly detection, forecasting, similarity search, and contextual diagnostics can be performed directly on live data streams at scale.

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.

Dr. L. Venkata Subramaniam is a founder of Qbit Force Quantum Pvt. Ltd. and he is a former Head of IBM Quantum India. He earned his Ph.D. from IIT Delhi in 1999. He is a Master Inventor and he holds 40 patents and has authored over 150 research papers with more than 3,400 citations, in addition to architecting several advanced products during his tenure at IBM. His book, Quantum Nation, became an Amazon India bestseller.

Dr. Subramaniam was recognised among India’s 100 Most Influential People in AI by Analytics India Magazine and in the MIT Sloan Management Review India Top 100. He was also featured by Mint as one of the “Four Musketeers of India’s Quantum Leap”.

Title: Quantum Decade: India’s leap forward

Abstract: Quantum Decade: India's Leap Forward examines how India is emerging as a global leader in quantum computing during the coming decade. The session explores the growth of India's quantum ecosystem through the National Quantum Mission, quantum startups, research institutions, and state quantum initiatives. In the talk we will also explore key technical focus areas in algorithms, software and hardware, including the cryogenic infrastructure, control electronics, and quantum processors required to build scalable systems. The talk highlights how India can translate scientific research into indigenous quantum technologies, products, and companies that contribute to national competitiveness and economic growth.