EKC2026

DIVISIONS

Basic Science (BS)

Basic Science has entered a new era in which unprecedented volumes of data, powerful computational resources, and advances in artificial intelligence are reshaping scientific discovery. This program brings together researchers working at the frontiers of mathematics, physics, astronomy, chemistry, biology, and related disciplines to explore the fundamental principles governing natural and complex systems. The sessions cover a broad spectrum of topics, including infectious disease forecasting, big-data astronomy, network science, equilibrium and nonequilibrium phenomena, scientific machine learning, generative modeling, and data-driven approaches to complexity. Although diverse in scope, these areas share a common objective: developing quantitative frameworks that enable deeper understanding, prediction, and control of phenomena across multiple spatial and temporal scales. By fostering interdisciplinary dialogue among scientists from diverse backgrounds, the program aims to bridge traditional scientific disciplines with emerging computational and data-centric methodologies. The presentations highlight how advances in theory, modeling, computation, and artificial intelligence are expanding the frontiers of basic science and creating new opportunities for discovery and innovation.

Programme Committee

DR. JHUN, Bukyoung (전부경)

Center for Critical Computational Studies, Goethe University Frankfurt

jhun@c3s.uni-frankfurt.de

Show Profile

Bukyoung Jhun is a statistical physicist and network scientist whose research focuses on the inference and reconstruction of complex networks and their connections to epidemic dynamics, higher-order interactions, and critical phenomena. He received his Ph.D. in Physics from Seoul National University, where he studied the prediction and control of complex contagion processes. His work encompasses network inference, hypergraph contagion, graph neural networks, and the latent geometry of complex networks.

DR. CHEON, Hyejeong (천혜정)

Kartverket (The Norwegian mapping authority)

hyejeong.l.cheon@gmail.com hyejeong.cheon@kartverket.no

Show Profile

Hyejeong Cheon is a senior engineer in the Geodesy Department at the Norwegian Mapping Authority (Kartverket), Norway’s official national agency responsible for surveying, mapping, cadastre and hydrographic data. Geodesy, the science of measuring and understanding Earth’s geometric shape, orientation in space and gravity field, provides the foundation for accurate positioning and navigation services. Her work focuses on GNSS data modelling, applying methods from data science, mathematics, statistics and machine learning. She holds a B.E. in Integrative Engineering with a specialization in Nanomaterials, a M.Sc. in Physics and a Ph.D. in Physics, both specialized in computational and programming-aided Physics.

[BS1-LH1] Infectious Disease Forecasting and Decision Support: Models, Data, and Emerging AI

Date / Time	2026-07-20 13:30 -- 15:00
Room	Pierre Baudis - Guillaumet1
Conveners / Chairs	PROF. LEE, Jeehyun Yonsei University Show Profile Professor Jeehyun Lee received her Ph.D. in Applied Mathematics from Iowa State University in 2001 under the supervision of Dr. Max Gunzburger. She worked for the MRSEC (materials research science and engineering center) at Carnegie Mellon University from 2001 to 2004 as a research associate. She has been a professor in Mathematics at Yonsei University since 2004 and joined CSE(computational science and engineering) in 2008. Her research area is numerical analysis and scientific computing involving control and optimization, mathematical biology, and epidemiology. She has been collaborating with medical doctors and policy makers on developing mathematical models to predict dynamics, to asses intervention strategies incㅣuding economic evaluation, and to derive optimal control of various diseases. DR. CHOI, Yoon Hong UK Health Security Agency Show Profile Dr Yoon Hong Choi is Lead Vaccine Preventable Infectious Diseases Modeller at UK Health Security Agency, London, UK. He has contributed to various modelling and cost-effectiveness projects of vaccine-preventable infectious diseases to provide scientific evidence to the Joint Committee of Vaccination and Immunisation and DHSC regarding the UK vaccination programme policies related to MMR, Pertussis, Human Papillomavirus and Streptococcus pneumoniae.
Synopsis	This session focuses on infectious disease modelling and forecasting for public health decision-making, with examples from South Korea and European contexts. It builds on the EKC2025 session and aligns with EKC2026 by critically examining where AI and machine learning are beginning to contribute, and where traditional mechanistic and statistical approaches remain essential. Presentations will cover mechanistic and statistical models used in practice, integration of diverse data sources (surveillance, genomics, serology, mobility, wastewater, clinical data), and approaches for scenario planning and intervention evaluation. A dedicated portion will address emerging AI/hybrid methods and the evidence needed for adoption, including validation, uncertainty, interpretability, bias, and governance. The session aims to provide a realistic view of current capabilities and a roadmap for responsible progress.
Speakers	PROF. MILLER, Elizabeth (London School Of Hygiene And Tropical Medicine) [ 13:00 - 13:20 ] Title: Modelling vaccine preventable diseases for national policy in the UK PROF. JUNG, Eunok (Konkuk University) [ 13:20 - 13:40 ] Title: From Model-Based Analysis to Policy: Optimization-Driven Infectious Disease Modeling in Korea PROF. LEE, Jacob (Hallym University Kangnam Sacred Heart Hospital) [ 13:40 - 14:00 ] Title: Application of Mathematical Modeling in Bioterrorism Response: Enhancing Government Preparedness for Smallpox and Anthrax in South Korea PROF. LEE, Jeehyun (Yonsei University) [ 14:00 - 14:15 ] Title: PINN applied to Infectious disease modeling DR. CHOI, Yoon Hong (Uk Health Security Agency) [ 14:15 - 14:30 ] Title: Cost effectiveness analysis of replacing PCV13 with PCV15 or PCV20 among England Infants Using Linked IPD–HES Data and Dynamic Modelling

[BS2] New era of Big Data Astronomy

Date / Time	2026-07-21 09:00 -- 10:30
Room	Pierre Baudis - Guillaumet1
Conveners / Chairs	MS. CHOI, Jeong Yun Heidelberg Institute for Theoretical Studies, Heidelberg University Show Profile Master’s degree in Astronomy from Yonsei University on “Effects of He and CNO enhanced Multiple Stellar Populations in determining the absolute ages of Globular Clusters”. Since late 2022, working at HITS in Heidelberg (Germany) as a PhD student with the focus of asteroseismology and red giant stars using data from NASA’s Kepler space mission. DR. MINJAE, Kim University College London (ERC research fellow & ESA project lead), Korean AeroSpace Administration (Deputy Director) Show Profile I am a planetary scientist dedicated to unraveling the mysteries of planet formation and evolution. My research focuses on the chemical and dynamical evolution in planetary systems, dust formation, and their roles in planetary environments and habitability in exoplanetary systems. Through the various projects, I am developing a comprehensive observational and theoretical framework to understand planet formation—addressing one of the most fundamental questions in modern astronomy: how do planetary systems, including our own, come to be? We combine cutting-edge numerical simulations with multi-wavelength observations to study the evolution of gas and dust in protoplanetary disks, the birthplaces of planets. My research also examines dust traces from debris discs around stars to reveal details about planetary system formation and evolution. I apply radiative transfer and SPH simulations to circumstellar disks around main-sequence stars and manage extensive datasets including the Herschel debris disk and MIDAS/Rosetta dust particle catalogues.
Synopsis	Over the past two decades, astronomy has undergone a remarkable transformation fuelled by advances in computing, detector technology, and instrumentation. Modern facilities now provide data with unprecedented spatial, spectral, and temporal resolution while large-scale survey telescopes continuously map the sky across multiple wavelength regimes. This has also unlocked previously inaccessible windows of the electromagnetic spectrum and entirely new messengers. Consequently, the limiting factor is no longer data acquisition but our ability to extract robust and meaningful insights from exponentially growing datasets. Our universe is full of fascinating and often surprising phenomena ranging from stellar interiors and compact objects to galaxy evolution and the large-scale structure of the cosmos. Understanding these phenomena demands not only deeper observations but also innovative methods and tools (e.g., artificial intelligence, data-driven astronomy, simulation, etc) capable of transforming raw data into physical understanding. In this session, we will discuss the development and deployment of these methodologies, empowering astronomers to navigate the data-rich landscape of modern astronomy and convert unprecedented observational volumes into transformative scientific knowledge.
Speakers	DR. JIN, Harim (Max Planck For Astrophysics) [ 09:05 - 09:20 ] Title: Chemical fingerprints of massive binary stars DR. RHEE, Jinsu (Institut D'astrophysique De Paris) [ 09:20 - 09:35 ] Title: Tracing Galaxy Evolution Across Cosmic Environments using Numerical Simulations MR. GIRODO, Yann (Isae-supaero) [ 09:35 - 09:50 ] Title: Monitoring the Sky : From Space Surveillance & Awareness to Observing Transient Astronomical Events MISS. SEO, Holly Hanbee (University Of St Andrews) [ 09:50 - 10:05 ] Title: Galaxy-Halo Connection in Frequency Space MS. KIM, Jee-ho (University Of Oxford) [ 10:05 - 10:20 ] Title: Intermediate-redshift ultra-luminous infrared galaxies as transitioning objects between main sequence and starburst galaxies MS. CHOI, Jeong Yun (Heidelberg University) [ 10:20 - 10:30 ] Title: When two stars oscillate like one: complex oscillation power spectra of red giants

[BS3] Complexity and Data Science

Date / Time	2026-07-21 15:30 -- 17:00
Room	Pierre Baudis - Guillaumet1
Conveners / Chairs	DR. KIM, Jung-ho Universitat Rovira i Virgili Show Profile Jung-Ho Kim is a Postdoctoral Researcher of the Department of Computer Engineering and Mathematics (DEIM), Universitat Rovira i Virgili, Tarragona, Spain. He received his Ph.D. in statistical physics from Korea University. His research interests include statistical physics, complex systems, and complex networks. DR. JHUN, Bukyoung Center for Critical Computational Studies, Goethe University Frankfurt Show Profile Bukyoung Jhun is a statistical physicist and network scientist whose research focuses on the inference and reconstruction of complex networks and their connections to epidemic dynamics, higher-order interactions, and critical phenomena. He received his Ph.D. in Physics from Seoul National University, where he studied the prediction and control of complex contagion processes. His work encompasses network inference, hypergraph contagion, graph neural networks, and the latent geometry of complex networks.
Synopsis	This session explores emerging frontiers at the intersection of complexity science, network theory, machine learning, and data-driven modeling. The presentations cover a broad range of topics, including higher-order interactions in complex systems, hyperbolic representation learning, generative models and scientific machine learning, and reaction-network construction for self-assembling molecular systems. Together, these studies demonstrate how modern data science and computational methodologies are transforming our ability to uncover hidden structures, infer governing principles, and predict behaviors across diverse natural and engineered systems. By integrating mathematical foundations, network analysis, artificial intelligence, and large-scale computational approaches, the session highlights novel frameworks for understanding complexity in high-dimensional and interconnected systems. It aims to foster interdisciplinary discussions among researchers working on data-driven discovery, machine learning, complex networks, and computational science.
Speakers	PROF. KAHNG, Byungnam (Institute For Grid Modernization, KENTECH) [ 15:30 - 16:00 ] Title: Multiple equilibria in complex scale-free networks MR. YANG, Ezekiel (EDF) [ 16:00 - 16:15 ] Title: Overview of Isolated Microgrids: Value of Forecasting, AI‑Enhanced EMS, and Stability Lessons from High‑Renewable Operation MS. JIYEON, Lee (Korea Institute of Civil Engineering and Building Technology) [ 16:15 - 16:30 ] Title: Development of an NLP-Based Methodology for Matching Between Carbon Neutrality Policies and K-SDGs MR. SHIN, Donghan (University of Oxford) [ 16:30 - 16:45 ] Title: CageVoyager: Constructing Self-Assembly Reaction Networks for Metallo-Organic Cages DR. JHUN, Bukyoung (Center For Critical Computational Studies, Goethe-universität Frankfurt) [ 16:45 - 17:00 ] Title: Hyperbolic Embedding via Riemannian Iterative Optimization

[BS4] Generative models and Scientific Machine Learning based on Mathematics

Date / Time	2026-07-21 17:00 -- 18:30
Room	Pierre Baudis - Guillaumet1
Conveners / Chairs	DR. WOO, Youngho National Institute for Mathematical Sciences
Synopsis	This session introduces the idea of Mathematics-based AI, which combines data-driven AI with mathematical theory to improve reliability, stability, and real-time performance. As AI becomes a key part of industrial and medical systems, high accuracy alone is no longer enough. It is also important to understand how a model works and to provide clear mathematical support for its performance and safety. In this context, the National Institute for Mathematical Sciences (NIMS) presents its view on trustworthy and explainable AI based on its experience in industrial mathematics and computational science. The session focuses on recent progress in generative models and Scientific Machine Learning (SciML), which are promising tools for analyzing high-dimensional medical data and for reconstructing complex biological phenomena. However, many current AI methods still have important limits, such as high computational cost, slow inference, weak interpretability, and limited clinical trust. These problems are especially serious in cardiac and cerebral blood flow analysis. Such tasks must handle complex fluid motion described by the Navier–Stokes equations, incomplete noninvasive imaging data, patient-specific anatomy, and changing boundary conditions. Because of these challenges, black-box deep learning alone is often not enough for direct clinical use. This session explores how mathematically grounded generative models and SciML can be combined to build more accurate, efficient, and trustworthy medical AI systems. The main goal is to show that future medical AI should be developed not only as a powerful prediction tool, but also as a mathematically principled framework that is interpretable, robust, and clinically meaningful.
Speakers	DR. HA, Taeyoung (National Institute For Mathematical Sciences) [ 17:00 - 17:20 ] Title: Mathematics-Based Scientific Machine Learning DR. LEE, Sunju (National Institute For Mathematical Sciences) [ 17:20 - 17:35 ] Title: Multimodal data fusion via scientific machine learning for groundwater resources analysis PROF. PARK, Won-kwang (Department Of Information Security, Cryptology, And Mathematics, Kookmin University) [ 17:35 - 17:50 ] Title: Real-Time Detection of Unknown Objects From Scattering Parameter Data Using Factorization Method DR. LEE, Mi-kyung (Critical Diseases Diagnostics Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology) [ 17:50 - 18:10 ] Title: Nanopore-based sensing of biomolecular interactions and machine learning-aided signal classification DR. GIM, Minjung (National Institute For Mathematical Sciences) [ 18:10 - 18:25 ] Title: Unbalanced Optimal Transport and Flow Matching for Label-Free Long-Tailed Generation

[BS5] Networks and their applications

Date / Time	2026-07-22 13:30 -- 15:00
Room	Pierre Baudis - Guillaumet1
Conveners / Chairs	DR. HA, Gyeong-gyun Middlesex University / Imperial College London Show Profile Gyeong-Gyun Ha earned his PhD in Mathematics from King's College London in 2025. Prior to his doctoral studies, he spent four years at Bitnine Ltd., a graph database vendor, where he developed various applications leveraging network science techniques. Currently, he is conducting theoretical research on complex networks at Middlesex University and Imperial College London. His research utilises network science as a primary lens to decode the underlying architecture of real-world systems, encompassing the development of both rigorous mathematical frameworks and the specialised software required to analyse them.
Synopsis	The modern world is increasingly defined by the intricate connectivity between individual components, necessitating rigorous mathematical frameworks to understand their functional properties. This session focuses on the structural analysis and dynamical processes of networked systems, emphasising the role of topology in determining system-wide behaviour. We explore the application of graph theory, stochastic processes, and dynamical systems to model and optimise the flow of information, energy, and resources across various architectures. The session will delve into key topics such as network robustness, synchronisation, and the stability of interconnected infrastructures. By highlighting methodological advancements in discrete mathematics and relational modelling, we aim to address practical challenges in critical infrastructure, communication systems, and biological pathways. Through this interdisciplinary dialogue, we will examine how structural constraints and topological features influence the evolution and performance of networks, providing a robust platform for researchers in applied mathematics, engineering, and relational science.
Speakers	DR. SUN, Hanlin (Nordita) [ 13:30 - 14:00 ] Title: The dynamic nature of percolation on networks with triadic interactions PROF. YUN, Jinhyuk (Soongsil University / Mpi-sp) [ 14:00 - 14:30 ] Title: Context-Aware Multimodal AI Uncovers Hidden Trajectories in Five Centuries of Artistic Change MR. PARK, Chanju (Swansea University) [ 14:30 - 14:50 ] Title: Spectral Phase Transitions During Neural Network Training MR. KWON, Yong (Center For Sensors And Devices, Fondazione Bruno Kessler, And Department Of Physics, University Of Trento) [ 14:50 - 15:10 ] Title: Quantum Simulation and Computing with Integrated Photonic Devices: Concepts and Experiments

[BS6] Physics of Equilibrium and Nonequilibrium Systems

Date / Time	2026-07-22 15:30 -- 17:00
Room	Pierre Baudis - Guillaumet1
Conveners / Chairs	DR. JHUN, Bukyoung Center for Critical Computational Studies, Goethe University Frankfurt Show Profile Bukyoung Jhun is a statistical physicist and network scientist whose research focuses on the inference and reconstruction of complex networks and their connections to epidemic dynamics, higher-order interactions, and critical phenomena. He received his Ph.D. in Physics from Seoul National University, where he studied the prediction and control of complex contagion processes. His work encompasses network inference, hypergraph contagion, graph neural networks, and the latent geometry of complex networks.
Synopsis	This session focuses on fundamental physical principles governing equilibrium and nonequilibrium phenomena in complex systems. Topics include synchronization in decentralized power grids, phase transitions and critical phenomena in scale-free networks, quantum physics of nonequilibrium systems, and other emergent behaviors arising from interacting many-body systems. Bringing together perspectives from statistical physics, nonlinear dynamics, network science, and computational chemistry, the session provides insights into the mechanisms underlying stability, phase transitions, and self-organization in complex physical systems. The presentations collectively emphasize both theoretical advances and computational approaches that deepen our understanding of equilibrium states and dynamical processes across a wide range of scientific and engineering applications.
Speakers	PROF. JEUNG, Gwang-hi (Aix-marseille University) [ 15:30 - 15:50 ] Title: Quantum chemical study for the shock-front reactions dedicated to the late Prof. Chul Park PROF. SIRE, Clément (CNRS/Université De Toulouse) [ 15:50 - 16:20 ] Title: Generative models and Scientific Machine Learning based on Mathematics DR. HA, Gyeong-gyun (Middlesex University / Imperial College London) [ 16:20 - 16:40 ] Title: Beyond Pairwise Interactions: Uncovering Hidden Communities in Complex Systems with Higher-Order Interactions DR. KIM, Jung-ho (Universitat Rovira I Virgili) [ 16:40 - 17:00 ] Title: Decentralization can hinder frequency synchronization in power grids through multiple phase transitions

PROGRAMME