| Date / Time | 2026-07-20 13:30 -- 15:00 |
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| Room | Pierre Baudis - Guillaumet1 |
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| 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.
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| Date / Time | 2026-07-21 13:30 -- 15:00 |
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| Room | Pierre Baudis - Guillaumet2 |
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| Synopsis | In alignment with EKC 2026's theme, ‘AI-Driven Future of Science and Technology’, this session explores how AI drives innovation across health, food, and nutrition systems from three distinct perspectives, reflecting the increasingly digitalized environments in which these systems operate.
The first perspective examines the scientific and technological foundations of AI-driven innovation. Across life sciences, data-driven approaches are reshaping health risk prediction, nutritional management, and food system monitoring, highlighting how digital transitions are unfolding across academia and industry.
The second perspective focuses on human perception and behavioral dynamics. For AI to be meaningfully integrated into human life, it is essential to understand how individuals, professionals, and consumers adopt and respond to AI-mediated technologies. This includes examining decision-making processes, risk perception, and trust in AI systems.
The third perspective addresses system-level governance and sustainability. Beyond individual adoption, AI deployment raises critical challenges related to ethical implementation, regulatory frameworks, data governance, and equitable access—particularly in the context of long-term public health.
By bridging these three perspectives, the session provides an interdisciplinary platform connecting technological development with life science and societal viewpoints. It welcomes contributions from researchers in health sciences, nutrition, food technology, data science, behavioral research, and policy studies, and aims to identify emerging research frontiers and translational strategies for responsible, human-centered AI in health, food, and nutrition. |
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| Date / Time | 2026-07-21 15:30 -- 18:30 |
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| Room | Pierre Baudis - Guillaumet2 |
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PROF. LIM, Youn-hee
Environmental Epidemiology Group, Section of Environmental Health, Department of Public Health, University of Copenhagen, Denm Show Profile |
| Synopsis | In line with EKC 2026 "AI-Driven Future of Science and Technology", this session explores how AI-driven data and integrative approaches are transforming health research across the life course.
Health outcomes are shaped by complex and dynamic interactions between environmental exposures, biological processes, and clinical and behavioral factors from early life to old age. Understanding these processes requires not only longitudinal perspectives but also the ability to integrate diverse data sources across scales—from environmental and population-level data to clinical and individual-level measurements.
Recent advances in geospatial technologies, satellite-based exposure assessment, large-scale health registers, and digital health applications provide a new research foundation for more precise exposure assessment, improved risk prediction, and deeper insights into disease mechanisms.
This session will share examples of research on interdisciplinary perspectives spanning environmental epidemiology, clinical research, and digital health. We seek to discuss how integrating environmental, clinical, and AI-driven data can advance our understanding of health trajectories and inform future prevention and precision health strategies. |
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| Date / Time | 2026-07-22 13:30 -- 15:00 |
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| Room | Pierre Baudis - Guillaumet2 |
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PROF. LEE, Seung Seo
Associate Professor of Chemical Biology and Medicinal Chemistry School of Chemistry, University of Southampton Show Profile |
| Synopsis | Modern biological research increasingly demands integration across traditional disciplinary boundaries, with artificial intelligence emerging as a transformative tool for enhancing experimental workflows and connecting diverse biological scales. This session explores how researchers across biological disciplines are incorporating AI into their work, whether through established computational-experimental pipelines or by exploring how AI might reshape their research approaches.
The most pressing challenges in biological sciences from antimicrobial resistance and drug discovery to systems-level understanding of disease mechanisms benefit from combining experimental expertise with computational capabilities. Artificial intelligence and machine learning offer opportunities to analyse complex datasets, predict molecular properties, identify patterns in high-dimensional biological data, guide experimental design, and accelerate hypothesis testing. This session welcomes any researchers who already have mature AI-integrated programs or intend to explore how computational tools might complement their experimental work. The session also invites presentations from the viewpoint of pure experimentalists with respect to AI revolution.
We invite contributions from any biological discipline: chemical biology, structural biology, microbiology, genomics, cell biology, pharmacology, systems biology, synthetic biology, or related fields. Presentations may showcase established computational-experimental workflows and discuss strategies for incorporating AI into traditional experimental programs. Furthermore, lessons from initial attempts at AI integration can be shared, or pure experimentalists’ view to AI integration may be discussed. Topics may address antimicrobial resistance, drug discovery, cancer biology, metabolic disorders, host-pathogen interactions, protein engineering, or fundamental biological mechanisms.
Join us to explore how biological scientists are incorporating AI into multidisciplinary research, expanding experimental capabilities and the questions we can address. |
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| Date / Time | 2026-07-22 15:30 -- 18:30 |
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| Room | Pierre Baudis - Guillaumet2 |
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| Synopsis | Aligned with the theme AI-driven Future of Science and Technology, this session will showcase transformative technologies redefining life science and health research. Beyond AI-powered analytics, topics will span spatial and single-cell multiomics, single-molecule imaging and sequencing, cryo-electron microscopy, advanced bioimaging, digital twins, organoids, high-throughput screening, and integrative computational modeling. Emphasis will be placed on how converging platforms generate multi-scale data—from molecules to organisms—and how AI enables their integration into predictive, mechanistic insights. Speakers will highlight breakthroughs that accelerate precision medicine, therapeutic discovery, and real-time health monitoring in an increasingly data-centric biomedical ecosystem. |
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