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Event Information

5th Prediction Science Seminar

January 7, 2022(Fri.)9:30-11:00 (JST)

To join the seminar, please contact the Prediction Science Seminar Office: prediction-seminar[remove here]@ml.riken.jp


Time Content Speaker
9:30-10:30 Neural Processes for Large-Scale Spatiotemporal Data Prof. Rose Yu
(Computer Science and Engineering, UC San Diego)
10:30-11:00 Discussion -


Neural Processes for Large-Scale Spatiotemporal DataProf. Rose Yu (Computer Science and Engineering, UC San Diego)

Earthquakes prediction, COVID-19 forecasts, and weather projection require accurate modeling of large-scale spatiotemporal data. Traditional approaches either rely on strong modeling assumptions, or are too slow for real-time decision-making. Neural processes significantly enhance the expressivity and scalability of existing methods with deep neural networks. In this talk, I will discuss two examples of neural processes. (1) Deep Spatiotemporal Point Process (DeepSTPP), a deep dynamics model that integrates spatiotemporal point processes. Our method is flexible, efficient, and can accurately forecast irregularly sampled events over space and time. (2) Interactive Neural Process (INP), a Bayesian active learning framework to proactively learn a deep learning surrogate model and accelerate stochastic simulation. Our framework can faithfully imitate the behavior of a complex infectious disease simulator with a small number of examples, enabling rapid simulation and scenario exploration. I will demonstrate the use case of these models on earthquake prediction and COVID-19 scenario creation.


  • Prediction Science Laboratory (RIKEN CPR)


  • Data Assimilation Research Team (R-CCS)
  • RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program
  • Environmental Metabolic Analysis Research Team (RIKEN CSRS)
  • Computational Climate Science Research Team (R-CCS)
  • Medical Data Deep Learning Team (R-IH)
  • Medical Data Mathematical Reasoning Team (R-IH)
  • Laboratory for Physical Biology (RIKEN BDR)

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