Intelligent Information
Technologies for Industry

Lev Utkin

D.Sc. (Tech.), Professor, Peter the Great St. Petersburg Polytechnic University, Russia

Conditional treatment effect models under uncertainty of training data

Conditional treatment effect models estimate heterogeneous responses to interventions based on individual covariates, enabling personalized decision-making in clinical medicine, equipment maintenance, and economic policy. Estimating these effects typically involves dividing subjects (patients) into treatment and control groups and comparing their outcomes. However, a fundamental challenge in estimation is that an individual's counterfactual outcome is unobservable; we can only observe the outcome under either the treatment or the control condition, never both. Additionally, training data often contains uncertainty, particularly when target variables are interval-valued, such as in the presence of censored observations. To address these challenges, existing treatment effect estimation models are analyzed, and novel attention-based approaches that explicitly account for training data uncertainty are introduced.

Speaker BIO

Professor of the Higher School of Artificial Intelligence in Peter the Great St. Petersburg Polytechnic University, Saint-Petersburg, Russia. Professor, DSc. Head of the Research Laboratory of Neural Network Technologies and Artificial Intelligence in the same university. In 1986 he graduated from St. Petersburg State Electrotechnical University (former Leningrad Electrotechnical Institute). He holds a Ph.D. in Information Processing and Control Systems (1989) from the same university and a D.Sc. in Mathematical Modelling (2001) from St. Petersburg State Institute of Technology, Russia. Awarded an Alexander von Humboldt Foundation Fellowship (2001-2003). Member of the Society for Imprecise Probability Theory and Applications (SIPTA) and the International Society on Multiple Criteria Decision Making (ISMCDM). Author of more than 300 scientific publications, including AI journals: Neurocomputing, Neural Networks, Knowledge-Based Systems, Applied Soft Computing, AI in Medicine, etc. Research interests are focused on machine learning, imprecise probability theory, decision making.

Ilias Savvas

Professor, Dept. of Digital Systems, University of Thessaly, Greece

Quantum Without the Equations: Building Quantum Literacy in Education

Quantum Mechanics has reached full maturity, and Quantum Computing is advancing at a remarkable pace. With demonstrations of quantum supremacy and the first signs of practical quantum advantage, the field is opening the door to transformative applications: quantum machine learning for drug discovery and personalized medicine, secure quantum communications and the quantum internet, post-quantum cryptography, and far beyond. Yet a striking paradox persists. While quantum technologies accelerate, foundational education lags behind. In secondary schools, and even in early university years, students learn almost nothing about quantum science, and many approaches it with anxiety, intimidated by its dense mathematical and physical formalism. This keynote confronts that gap directly. It argues that the core ideas of the quantum world, superposition, entanglement, measurement, and the qubit, can be taught meaningfully through intuition, visual reasoning, and everyday analogies, with no equations required. The talk then turns to a decisive and often overlooked factor: the readiness and willingness of secondary-school teachers to accept, adopt, and deliver quantum content. Finally, it explores how Artificial Intelligence can serve as a constructive pedagogical ally, helping educators overcome technical anxiety, design intuitive learning materials, and create an accessible, fear-free environment. Together, these threads chart a practical path toward preparing the next generation for an imminent quantum era.

Speaker BIO

Professor in the Department of Digital Systems at the University of Thessaly, Greece. He holds a Ph.D. in Computer Science from University College Dublin (UCD), Ireland (2004), an M.Sc. in Computer Science from the University of Dundee, Scotland (1988), and a B.Sc. in Mathematics from the Aristotle University of Thessaloniki, Greece (1982). He served as Dean of the School of Technology at the University of Thessaly (2020-2024) and has held numerous academic leadership roles, including Head of Department and Director of an M.Sc. programme, across more than three decades of teaching and research. He is the co-author of the book “Hands-On Quantum Computing” (Tziolas Editions, 2022) and has supervised numerous doctoral and master’s theses, many in quantum-related fields such as post-quantum cryptography, quantum machine learning, and quantum computing literacy in secondary education. He is a keynote speaker at GQCE 2027 (Global Summit on Quantum Computing & Engineering, Berlin) and at IITI 2026. Research interests are focused on quantum computing, quantum machine learning for drug development and personalized medicine, cloud and distributed computing, parallel and distributed algorithms, data mining, and big data. Prof. Savvas is an internationally recognized keynote speaker. Alongside this keynote at IITI 2026, he is also a keynote speaker at GQCE 2027, the Global Summit on Quantum Computing & Engineering (Berlin, 23–25 June 2027), where he is featured among the plenary speakers Peter Shor, David Deutsch, and John Preskill (https://gsqce.com/speakers).

Xiaoshan Gao

Prosessor, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Kaiyuan International Mathematical Sciences Institute, China

MechMath Agent Team: An LLM Driven Agent for Mathematical Research

MechMath Agent Team (https://mechmath.github.io/) is a large language model (LLM) driven agent designed as a co-pilot throughout the full cycle of mathematical research, including organizing and utilizing mathematical knowledge, proving theorems in natural language, proving theorems using formal language in Lean, proposing new conjectures, and drafting research papers in LaTeX. In this talk, I will give an introduction to MechMath Agent Team and how to use it to tackle open mathematical problems. I will also give a general overview of AI reasoning and the capability of LLM-Agent to prove mathematical theorems.

Speaker BIO

Xiao-Shan Gao is professor at Academy of Mathematics and Systems Science of Chinese Academy of Sciences, executive director of National Center for Mathematics and Interdisciplinary Sciences at CAS, and director of Kaiyuan International Mathematical Sciences Institute. He works on automated reasoning, symbolic computation, mathematical theory of deep learning and LLM, and LLM based mathematical reasoning. He was awarded National Natural Science Second Prize, Wu Wen-Tsun Applied Mathematics Prize, and Wu Wen-Tsun Distinguished Contribution Award to Artificial Intelligence.

Zongben Xu

Dean of the Xi'an Institute of Mathematics and Mathematical Technology, Professor of Xi'an Jiaotong University, China

A Limit Theory of Foundation Models:
A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws

Emergent intelligence has played a major role in the modern AI, manifesting as previously unobserved capabilities enabled by scaling model parameters. While existing studies primarily rely on empirical observations to characterize these phenomena, a rigorous theoretical framework remains underexplored. This study attempts to develop a mathematical approach to formulate emergent intelligence from the perspective of limit theory. See full description here.

Speaker BIO

Zongben Xu，received PhD degree in Mathematics in 1987 from Xi`an Jiaotong University, China. In 1998, he was a postdoctoral Research Fellow in the Department of Mathematics, The University of Strathclyde. He worked as a Research Fellow in the Department of Computer Science and Engineering from 1992 to 1994, and 1996 to 1997, at The Chinese University of Hong Kong; a visiting professor in the University of Essex in 2001, and Napoli University in 2002. He has been with the School of Mathematics and Statistics, Xi`an Jiaotong University since 1982, where he served as a professor of mathematics and computer science, Dean of Sciences (1997-2003), VP of the university (2003-2014) and Chief Scientist of National Basic Research Program of China (973 Project,2006-2016). He is currently the director of National Lab for Big Data Analytics, the Dean of Xi’an Academy of Mathematics and Mathematical Technologies, and the Director of Xi’an Academy of Big Data and Artificial Intelligence）.

Professor Xu makes several important services for government and professional societies currently, including Consultant member of National Big Data Development Commission, the New Generation AI Development Commission and the National Natural Science Foundation of China. He is VP of Industrial and Applied Mathematics Society of China (CSIAM), the director of Big Data and AI Committee of CSIAM. He is also the Editor-in-chief of Journal of Data Science & Management, the Monograph Series on Data Science & AI Research (China Science Publishing & Media Ltd.) and the Textbook Series on Data Science and Big Data Technology (Higher Education Press of China).

Professor Xu has published over 380 academic papers on nonlinear functional analysis, optimization, machine learning and artificial intelligence, most of which are in international journals. His current research interests include mathematical theory of foundation models, Nonlinear functional analysis and Data Science. Professor Xu has gotten many academic awards, say, the National Natural Science Award (2007, 2023), the National Scientific and Technological Advance Award (2011) of China，CSIAM Su Buchin Applied Mathematics Award (2008), Tan Kah Kee Science Award ( in Information Science, 2018) and Hua Loo-Keng Prize of Mathematics (2022). He also get the Richard Price Award (Data Science) in 2018 from International Academy of Information Technology and Quantitative Management (IAITQM). He delivered a 45 minute talk at the International Congress of Mathematicians in 2010 upon the invitation of the congress committee. He was elected as a member of Chinese Academy of Science in 2011.

Lei Guo

Director, National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, China

Learning and Adaptation in Uncertain Dynamical Systems

Learning and adaptation are fundamental to the modeling and control of uncertain dynamical systems, and they are also cornerstones of intelligent systems. While statistical learning has underpinned much of the progress in machine learning, its theoretical foundations often rest on idealized assumptions about data, such as independent and identically distributed (i.i.d.) samples. These assumptions are typically not valid for dynamical systems, especially those under feedback control, where the input-output data exhibit strong temporal correlations and inherent nonstationarity. This motivates the development of a more general theoretical framework that can account for the complex data characteristics intrinsic to dynamical systems. This talk addresses these challenges by presenting some of our recent advances in learning and adaptation for stochastic dynamical systems. We introduce general and verifiable data conditions that can guarantee global stability and estimation performance for adaptive learning and filtering algorithms with either diminishing or non-diminishing adaptation gains. Topics covered include gradient-based and Newton-type adaptive methods, distributed learning and filtering in networked systems, integrated offline-online learning frameworks. We also consider practical challenges such as saturated or limited-value measurements, finite data size, input-state constraints, and time-varying system parameters. The lecture concludes with a discussion of emerging research at the nexus of control and AI.

Speaker BIO

Lei Guo received his Bachelor's degree in Mathematics from Shandong University in 1982 and his Ph.D. in Control Theory from the Chinese Academy of Sciences (CAS) in 1987. He is currently a professor at the Academy of Mathematics and Systems Science, CAS, and serves as the director of the National Center for Mathematics and Interdisciplinary Sciences, CAS. He is a Fellow of IEEE, a Member of CAS, a Foreign Member of the Royal Swedish Academy of Engineering Sciences, a Fellow of the Third World Academy of Sciences (TWAS), and a Fellow of IFAC. He was awarded the IFAC Congress Young Author Prize in 1993, the IFAC Outstanding Service Award in 2011, an honorary doctorate from the Royal Institute of Technology (KTH, Sweden) in 2014, and the Hendrik W. Bode Lecture Prize from the IEEE Control Systems Society (CSS) in 2019. He has served as President of the Asian Control Association (ACA), President of the China Society for Industrial and Applied Mathematics (CSIAM), General Co-Chair of the 48th IEEE CDC, and Distinguished Lecturer of both IFAC and IEEE-CSS. His research interests include stochastic systems, system identification, machine learning, adaptive control, adaptive filtering, adaptive game theory, control of uncertain nonlinear systems, feedback capability, multi-agent systems, and game-based control systems.