Keynote Presentations

Leveraging large-scale data and accelerated computing systems, statistical learning has led to significant paradigm shifts in many scientific disciplines. Grand challenges in science have been tackled with exciting synergy between disciplinary science, physics-based simulations via high-performance computing, and powerful learning methods.

In this talk, Fei Sha will describe two vignettes of our research in this theme: probabilistic generative AI technology for uncertainty quantification, and closure modeling. He will also demonstrate how those technologies can be effectively applied to weather and climate, addressing crucial problems in those areas.

Note: The research work presented in this talk is based on joint and interdisciplinary research work of several teams at Google Research.

Recently tick ranges have been expanding due in part to rising temperatures as consequences of climate change, increasing the risk of tick-borne illnesses, and necessitating practical ways of managing tick populations. Prescribed fires is a commonly used land management practice that is time and cost efficient. This lecture will discuss the effects of prescribed fire intensity (low and high) and the duration between burns on the prevalence of tick-borne illnesses as temperature rises, and explore the effect of prescribed burns on the establishment of ticks into new areas. The results indicate that prescribed fire intensity has a larger impact in reducing disease prevalence than the frequency between burns. Infrequent burns, however, are ineffective at preventing tick establishment since populations can recover quickly following a burn, while frequent long-term prescribed burns can slow and possibly prevent tick establishment into new areas.