Registration is Now Open
Integrating Machine Learning with Multiscale Modeling for Biomedical, Biological, and Behavioral Systems
2019 ML-MSM MeetingOctober 24-25, 2019NIH Main Campus - Bethesda, MD
With breakthrough technology developments throughout the past decades, biomedical, biological, and behavioral research is now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret those data. Multiscale modeling has advanced to one of the most successful strategies to integrate data across the scales and offer mechanistic insights; yet, multiscale modeling alone often fails to efficiently combine large data sets from several different sources. Machine learning is a powerful technique that can guide model building, accelerate multiscale and multiphysics computational algorithms, train models to learn from data, identify patterns, and inform decision making. While traditional machine learning tools perform these tasks with minimal human intervention, this meeting focuses on integrating machine learning methods with multiscale modeling methods guided by the fundamental principles of mathematics and physics. The objective of this meeting is to identify the perspectives, challenges, and opportunities of integrating machine learning with multiscale modeling (ML-MSM) in biomedical, biological, and behavioral systems. Specifically, we will address four approaches within ML-MSM modeling: ordinary differential equation based, partial differential equation based, theory-driven, and purely data-driven approaches. Attendees will discuss these approaches in the context of developing Digital Twins and addressing Human Safety.
With breakthrough technology developments throughout the past decades, biomedical, biological, and behavioral research is now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret those data. Multiscale modeling has advanced to one of the most successful strategies to integrate data across the scales and offer mechanistic insights; yet, multiscale modeling alone often fails to efficiently combine large data sets from several different sources. Machine learning is a powerful technique that can guide model building, accelerate multiscale and multiphysics computational algorithms, train models to learn from data, identify patterns, and inform decision making. While traditional machine learning tools perform these tasks with minimal human intervention, this meeting focuses on integrating machine learning methods with multiscale modeling methods guided by the fundamental principles of mathematics and physics. The objective of this meeting is to identify the perspectives, challenges, and opportunities of integrating machine learning with multiscale modeling (ML-MSM) in biomedical, biological, and behavioral systems. Specifically, we will address four approaches within ML-MSM modeling: ordinary differential equation based, partial differential equation based, theory-driven, and purely data-driven approaches. Attendees will discuss these approaches in the context of developing Digital Twins and addressing Human Safety.
Registration is now open.
Key Dates:
October 1, 2019 - Poster Abstracts Due -- submit your abstracts here (after you have registered)
October 1, 2019 - Last day to register online (on-site registration after this date) – Register early to participate in pre-meeting activities
October 22, 2019 - Lunch Orders Deadline-- Lunch Order Instructions
October 24, 2019 Dinner is limited to 90 seats -- Dinner Information (Register to save your seat and avoid the waiting list)
Click here for Hotel Room Block Reservations
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