Data science, AI and ML for Active and Living Systems
FOCUS · F18 · ID: 2155850
Presentations
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Controlling Colloidal Assembly & Reconfiguration
ORAL · Invited
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Presenters
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Michael A Bevan
- Johns Hopkins University
Authors
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Michael A Bevan
- Johns Hopkins University
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Quantifying dynamics of soft and active matter with microscopy and machine learning
ORAL
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Presenters
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Gildardo Martinez
- University of San Diego
Authors
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Gildardo Martinez
- University of San Diego
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Justin Siu
- University of San Diego
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Dylan Gage
- University of San Diego
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Emma Kao
- University of San Diego
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Juan Carlos Avila
- University of San Diego
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Ruilin You
- University of San Diego
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Ryan J McGorty
- University of San Deigo
- University of San Diego
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Learning active nematohydrodynamics with SINDy-PI
ORAL
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Presenters
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Chris Amey
- Brandeis University
Authors
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Chris Amey
- Brandeis University
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Michael F Hagan
- Brandeis University
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Aparna Baskaran
- Brandeis University
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Grant Rotskoff
ORAL · Invited
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Publication: arXiv:2306.10778, arxiv:2205.01205
Presenters
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Grant M Rotskoff
- Stanford University
- Stanford Univ
Authors
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Grant M Rotskoff
- Stanford University
- Stanford Univ
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Learning cell division strategies across diverse organisms
ORAL
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Presenters
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Shijie Zhang
- Massachusetts Institute of Technology
Authors
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Shijie Zhang
- Massachusetts Institute of Technology
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Chenyi Fei
- Massachusetts Institute of Technology
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Jorn Dunkel
- Massachusetts Institute of Technology
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A Spectral Approach for Learning Spatiotemporal Neural Differential Equations
ORAL
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Publication: https://arxiv.org/pdf/2309.16131.pdf
Presenters
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Mingtao Xia
- New York University
Authors
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Mingtao Xia
- New York University
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Xiangting Li
- University of California, Los Angeles
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Qijing Shen
- Oxford University
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Tom Chou
- University of California, Los Angeles
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Controlling Assembly and Encoding in Active Matter Using Light Patterns
ORAL
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Presenters
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Jerome Delhommelle
- University of Massachusetts, Lowell
Authors
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Jerome Delhommelle
- University of Massachusetts, Lowell
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Caroline Desgranges
- University of Massachusetts Lowell
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Improved KCNQ2 gene missense variant interpretation with artificial intelligence.
ORAL
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Publication: Improved KCNQ2 gene missense variant interpretation with artificial intelligence
Alba Saez-Matia, Arantza Muguruza-Montero, Sara M-Alicante, Eider Núñez, Rafael Ramis, Óscar R. Ballesteros, Markel G Ibarluzea, Carmen Fons, View ORCID ProfileAritz Leonardo, Aitor Bergara, Alvaro Villarroel
https://doi.org/10.1101/2022.10.20.513007Presenters
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Aritz Leonardo
- University of the Basque Country UPV/EHU
Authors
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Aritz Leonardo
- University of the Basque Country UPV/EHU
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Aitor Bergara
- Donostia International Physics Center
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Alvaro Villarroel
- Biofisika institute
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Markel García Ibarluzea
- Donostia International Physcis Center
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Rafael Ramis Cortés
- Donostia International Physcis Center
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Alba Sáez-Matía
- biofisika
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Eider Núñez
- Biofisika institute
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Uncovering interpretable low-dimensional geometric structures in gene expression using curvature regularized variational autoencoders
ORAL
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Presenters
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Jason Z Kim
- Cornell University
Authors
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Jason Z Kim
- Cornell University
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Nicolas Perrin-Gilbert
- Curie Institute
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Paul Klein
- Curie Institute
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Erkan Narmanli
- Curie Institute
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Chris Myers
- Cornell University
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Itai Cohen
- Cornell University
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Joshua J Waterfall
- Curie Institute
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James P Sethna
- Cornell University
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Convolutional Neural Network Analysis of Molecular Docking for Cancer Drug Discovery
ORAL
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Presenters
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Gaige Riggs
- Missouri State University
Authors
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Gaige Riggs
- Missouri State University
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Ridwan Sakidja
- Missouri State University
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Combining Neural Networks and Principal Component Analysis
ORAL
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Publication: D. Yevick, K. Suszek, in preparation (Arxiv)
Presenters
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Karolina Suszek
- University of Waterloo
Authors
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David O Yevick
- University of Waterloo
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Karolina Suszek
- University of Waterloo
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