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American Institute of Mathematics, Pasadena, California

This workshop, sponsored by AIM and the NSF, will focus on advancing mathematical modeling frameworks and theory to address the urgent challenges posed by declining pollinator populations. Insect pollination is vital to terrestrial ecosystems and agriculture, with honeybee pollination in the United States alone valued at over $12 billion annually. Yet, substantial evidence shows that pollinator populations are in sharp decline, with honeybee colony losses reaching unprecedented levels — threatening agricultural sustainability and food security. These declines result from complex interactions among environmental factors (e.g., shifting climate patterns) and biological processes (e.g., exposure to agrochemicals such as insecticides and fungicides, parasitic infections, diseases, and habitat degradation).

Applied Mathematics (in general),    Biology

Keystone Symposia

Biological discovery is undergoing a transformation driven by the convergence of advanced imaging technologies, data science, and computational modeling. This Keystone Symposium will address the critical need to integrate heterogeneous imaging modalities, aided by computational and Deep Learning-driven approaches, to derive mechanistic and predictive insights from complex biological data. The meeting aims to foster dialogue among imaging scientists, computational biologists, data scientists, and systems biologists to define emerging frontiers in data integration, standardization, accessibility, and visualization. Previous conferences have often centered on individual structural biology techniques but have rarely emphasized interdisciplinary approaches. In contrast, this meeting contains a carefully curated program that prioritizes research presentations explicitly focused on integrative approaches, ensuring that the symposium moves beyond acknowledgement to actionable convergence.

Biochemistry, Advanced Imaging, Computational Biology, Structural Biology, Data Science, Artificial Intelligence, Deep Learning, Interdisciplinary Science, Data Integration, Predictive Biology

Biology,    Molecular Biology