She is a recipient of the Padma Shri, Shanti Swarup Bhatnagar Prize, Infosys Prize, and TWAS Prize. She is an IEEE Fellow, a Fellow of the Indian National Academies, and currently serves as the Director of the Indian Statistical Institute (ISI), the first woman to hold this position since the institute's founding in 1931. She is now in her second term as Director (2015-2020, 2020-2025). She received her B.Tech in Computer Science from Calcutta University, her M.Tech from IIT Kharagpur, and her Ph.D. from the Indian Statistical Institute. Her research interests include computational biology, soft and evolutionary computing, artificial intelligence and machine learning, data mining, and multi-objective optimization. She has published over 325 research papers and authored/edited 6 books with leading publishers such as Springer, World Scientific, and Wiley, with an h-index of 62. She is also a member of the Prime Minister's Science, Technology and Innovation Advisory Council (PM-STIAC) and was named an IEEE CIS Distinguished Lecturer for the term 2026-2028 in December 2025.

Title: Integrating Multimodal Multiobjective Optimization and Internet of Things for Smart Building Energy Management

Abstract: Intelligent and sustainable building energy management systems has been drawing significant attention in view of rapid urbanization and increase in energy demands. Towards this objective, data collected from Internet of Things, primarily an array of sensors, is processed and analyzed using advanced AI based approaches to extract meaningful patterns and a judicious policy of optimal energy management. In this talk we will first provide a brief overview of the area. Thereafter we will explore the integration of multiobjective optimization techniques with the data collected by a large number of sensors to propose optimal occupant action for enhanced comfort and higher operational efficiency. Motivating occupants to change their daily schedule to an optimal plan for the desired impact of actions can be time-consuming. To simplify this, a novel multimodal multiobjective optimization technique is utilized to propose a Pareto-optimal which closely resembles occupants' daily schedule. The talk will conclude with some results demonstrating the superiority of the proposed approach.

Prof. Ujjwal Maulik is a full Professor in the Department of Computer Science and Engineering at Jadavpur University, Kolkata, India, where he has been serving since 2004 and previously held the position of Department Head. He received his M.Tech in Computer Science (1992) and Ph.D. in Engineering (1997) from Jadavpur University, and his B.Tech in Computer Science (1989) and B.Sc. in Physics (1986) from Calcutta University.

His research interests include machine learning, pattern analysis, data science, bioinformatics and computational biology, multi-objective optimization, social networking, IoT, and autonomous vehicles. In these areas, he has published 10 books, over 400 research papers, supervised 25 doctoral students, and mentored several start-ups.

Prof. Maulik is a Fellow of multiple prestigious international societies: IEEE (elected 2020), IAPR (International Association for Pattern Recognition, 2018), INSA (Indian National Science Academy, 2021), INAE (Indian National Academy of Engineering, 2014), NASI (National Academy of Science India, 2021), and AAIA (Asia-Pacific Artificial Intelligence Association, 2022). He is also a Distinguished Member of the ACM (2020).

He has received several prestigious international fellowships, including the Alexander von Humboldt Fellowship (2010-2012), Senior Associate of ICTP (International Centre for Theoretical Physics, Italy, 2012-2018), and the Fulbright-Nehru Academic and Professional Excellence Award (2024-2025). He was also honored with the Shiksharatna Award by the Government of West Bengal (2021).

Prof. Maulik served as an IEEE CIS Distinguished Lecturer (2022) and is a Distinguished Speaker of both IEEE and ACM. He has worked as a visiting professor/scientist at numerous renowned institutions worldwide, including Los Alamos National Laboratory (USA), Stanford University (USA), German Cancer Research Center (Germany), Tsinghua University (China), and Sapienza University of Rome (Italy). He is currently conducting Fulbright research at New England Biolabs, Massachusetts, USA (starting April 2025), applying AI methods to analyze chromatin accessibility data.

Title: Integrating Artificial Intelligence, Big Data, and Cloud Computing for Next-Generation Intelligent Systems

Abstract: The rapid evolution of Artificial Intelligence (AI), Big Data, and Cloud Computing is transforming the landscape of modern intelligent systems. The integration of these technologies enables efficient data storage, scalable computation, real-time analytics, and intelligent decision-making across diverse application domains including Healthcare. This lecture explores the synergistic relationship between AI-driven algorithms, large-scale data processing, and cloud-based infrastructures in building next-generation intelligent systems. It highlights recent advancements and practical applications in healthcare. Furthermore, the discussion addresses critical challenges such as data security, privacy preservation, computational complexity, and ethical concerns associated with intelligent cloud ecosystems. The session aims to provide insights into how the convergence of AI, Big Data, and Cloud Computing is driving innovation and shaping the future of digital transformation.

Nianyin Zeng was born in Fujian Province, China, in 1986. He received the B.Eng. degree in electrical engineering and automation in 2008 and the Ph.D. degree in electrical engineering in 2013, both from Fuzhou University. From October 2012 to March 2013, he was a RA in the Department of Electrical and Electronic Engineering, the University of Hong Kong. From September 2017 to August 2018, he as an ISEF Fellow founded by the Korea Foundation for Advance Studies and also a Visiting Professor at the Korea Advance Institute of Science and Technology.

Currently, he is a Professor with the Department of Instrumental & Electrical Engineering of Xiamen University. His current research interests include intelligent data analysis, computational intelligent, time-series modeling and applications. He is the author or co-author of several technical papers and also a very active reviewer for many international journals and conferences.

Prof. Zeng is currently serving as Associate Editors for Neurocomputing, Evolutionary Intelligence, and Frontiers in Medical Technology, and also Editorial Board members for Computers in Biology and Medicine, Biomedical Engineering Online, and Mathematical Problems in Engineering.

Title: Research on Intelligent Visual Defect Detection Methods for Aero-engine Core Components under Complex Conditions

Abstract: Aero-engines, as highly complex precision systems, can suffer catastrophic consequences from minute defects in critical components like turbine and compressor blades. Traditional inspection methods often rely on manual expertise, suffering from low efficiency and high rates of missed/false detection. In contrast, AI-driven inspection offers non-contact operation, high precision, and automation potential, presenting a key solution to these bottlenecks. This report analyzes core scientific challenges and solutions for intelligent defect detection in aero-engines under complex and dynamic scenarios: (1) Addressing Data Limitations: Analyzing methods to process and enhance low-quality defect images captured in extreme environments, mitigating noise and overcoming insufficient labeled samples; (2) Overcoming Algorithmic Bottlenecks: Solving how to extract discriminative defect-related features from high-dimensional visual data to distinguish subtle, complex, and obscured defect features from complex background; (3) Resolving Deployment Constraints: Exploring lightweight, high-precision, low-latency defect detection under limited computational resources to achieve efficient performance trade-offs for in-line aero-engine quality inspection. Furthermore, the report introduces emerging technologies, discussing their industry-academia-research collaboration for intelligent aviation equipment maintenance.

Pietro Oliveto is a Professor of Computer Science at the Southern University of Science and Technology (SUSTech) Shenzhen, China. He received the Laurea degree and PhD degree in computer science respectively from the University of Catania, Italy in 2005 and from the University of Birmingham, UK in 2009. He has been EPSRC PhD+ Fellow (2009-2010) and EPSRC Postdoctoral Fellow (2010-2013) at the University of Birmingham, UK and Vice-Chancellor's Fellow (2013-2016) and EPSRC Early Career Fellow (2015-2020) at the University of Sheffield, UK. Before moving to SUSTech he was Chair in Algorithms at the Department of Computer Science, University of Sheffield, UK.

His main research interest is the performance analysis, in particular the time complexity, of bio-inspired computation techniques including evolutionary algorithms, genetic programming, artificial immune systems, hyper-heuristics and algorithm configurators. He is currently building a Theory of Artificial Intelligence Lab at SUSTech.

He has guest-edited journal special issues of Computer Science and Technology, Evolutionary Computation, Theoretical Computer Science, IEEE Transactions on Evolutionary Computation and Algorithmica. He has co-Chaired the IEEE symposium on Foundations of Computational Intelligence (FOCI) from 2015 to 2021 and has been co-program Chair of the ACM Conference on Foundations of Genetic Algorithms (FOGA 2021) and Theory Track co-chair at GECCO 2022, GECCO 2023 and GECCO 2026. He is part of the Steering Committee of the annual workshop on Theory of Randomized Search Heuristics (ThRaSH), was Leader of the Benchmarking Working Group of the EU-COST Action ImAppNIO, was member of the EPSRC Peer Review College and recently completed his term as Associate Editor of IEEE Transactions on Evolutionary Computation.

Title:Computational Complexity Analysis of Sexual Evolution for the Design of Better General Purpose Algorithms

Abstract:Large classes of the general-purpose optimisation algorithms at the heart of modern artificial intelligence, Inerrnet of Things and Cloud Computing technologies are inspired by models of Darwinian evolution. In this talk we show how the foundational computational complexity analysis of such algorithms leads to an understanding of their behaviour and performance. Such understanding in turn allows informed decisions on how to set their many parameters and how to improve the algorithms to allow for the obtainment of better solutions in shorter time. We provide two concrete examples of how such analyses can lead to counter intuitive insights into how to design sexual evolution inspired algorithms (using populations and recombination) and how to set their parameters such that they can considerably outperform their single trajectory and mutation only (asexual) counterparts at hillclimbing unimodal functions, and at escaping from local optima. We conclude the talk by presenting experimental results that confirm the superiority of the designed algorithms that was proven for benchmark functions with significant structures, for classical combinatorial optimisation problems with practical applications. 

He is a Professor of Practice in Computer Science at NYU Shanghai and a tenured Maître de Conférences at Sorbonne Université in Paris. He has over twenty years of international experience in teaching, research, and academic leadership across several continents. At NYU Shanghai, he built the undergraduate CS and Engineering curriculum and served in senior administrative roles, including Interim Dean and Associate Dean of Arts and Sciences.

His research bridges distributed algorithms and systems implementation, with applications in collective AI, distributed quantum computing, cloud infrastructure, and edge-to-cloud coordination.

He studies resilient and scalable distributed systems at the interface of theory and implementation. Current funded projects include ANR FrugalDiNet and NSFC Scalable Collective AI. Ongoing collaborations also explore distributed algorithms for quantum computing.

Title:The Network Already Knows: Leveraging Data-Plane Intelligence

Abstract：Distributed protocols spend a surprising amount of effort learning information that the network already possesses. Every day, datacenters generate vast amounts of telemetry about latency, congestion, and communication quality. Yet consensus protocols, failure detectors, and leader election algorithms typically ignore this information and instead rely on additional heartbeats, probes, and monitoring traffic to infer the same conditions indirectly.

This talk presents NetElect, a system that turns existing network telemetry into a resource for distributed coordination. By harvesting information already available in the data plane, NetElect improves leader election and failure detection without changing the underlying consensus protocol. The result is faster recovery, better performance, and lower overhead.

More broadly, I will argue that distributed systems are entering an era where the traditional separation between networking and coordination is becoming increasingly artificial. As datacenter observability improves and programmable infrastructure becomes commonplace, a new generation of telemetry-aware distributed services becomes possible.