Science Lecture Series | Computational Modeling and Digital Twin of a Patient | 4/19/22
The College of Natural & Agricultural Sciences at the University of California, Riverside is proud to present the 2022 Science Lecture Series entitled Big Data Science. The third of this four-part is Tuesday, April 19, with Dr. Mark Alber, UC Riverside Distinguished Professor of Mathematics, with a presentation on Computational Modeling and Digital Twin of a Patient.
Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard.
This technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where clinicians have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. Multiscale computational modeling is a successful strategy to integrate multiscale, multiphysics data and uncover biological mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution
In this lecture, Dr. Alber will demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that can provide new insights into disease mechanisms, help identify new targets and patient specific treatment strategies, and inform decision making for the benefit of human health [1,2].
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1. Mark Alber, Adrian Buganza Tepole, William R. Cannon, Suvranu De, Salvador Dura-Bernal, Krishna Garikipati, George Karniadakis, William W. Lytton, Paris Perdikaris, Linda Petzold & Ellen Kuhl, Integrating machine learning and multiscale modeling - perspectives, challenges, and opportunities in the biological biomedical, and behavioral sciences, npj Digital Medicine, 2:115 (2019).
2. Grace C. Y. Peng, Mark Alber, Adrian Buganza Tepole, William R. Cannon, Suvranu De, Salvador Dura-Bernal, Krishna Garikipati, George Karniadakis, William W. Lytton, Paris Perdikaris, Linda Petzold & Ellen Kuhl [2020], Multiscale Modeling Meets Machine Learning: What Can We Learn? Archives of Computational Methods in Engineering 28, 1017–1037 (2021).
The College of Natural & Agricultural Sciences at the University of California, Riverside is proud to present the 2022 Science Lecture Series entitled Big Data Science. The third of this four-part is Tuesday, April 19, with Dr. Mark Alber, UC Riverside Distinguished Professor of Mathematics, with a presentation on Computational Modeling and Digital Twin of a Patient.
Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard.
This technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where clinicians have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. Multiscale computational modeling is a successful strategy to integrate multiscale, multiphysics data and uncover biological mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution
In this lecture, Dr. Alber will demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that can provide new insights into disease mechanisms, help identify new targets and patient specific treatment strategies, and inform decision making for the benefit of human health [1,2].
_____
1. Mark Alber, Adrian Buganza Tepole, William R. Cannon, Suvranu De, Salvador Dura-Bernal, Krishna Garikipati, George Karniadakis, William W. Lytton, Paris Perdikaris, Linda Petzold & Ellen Kuhl, Integrating machine learning and multiscale modeling – perspectives, challenges, and opportunities in the biological biomedical, and behavioral sciences, npj Digital Medicine, 2:115 (2019).
2. Grace C. Y. Peng, Mark Alber, Adrian Buganza Tepole, William R. Cannon, Suvranu De, Salvador Dura-Bernal, Krishna Garikipati, George Karniadakis, William W. Lytton, Paris Perdikaris, Linda Petzold & Ellen Kuhl [2020], Multiscale Modeling Meets Machine Learning: What Can We Learn? Archives of Computational Methods in Engineering 28, 1017–1037 (2021).
