2018 Application: Data-Driven Discovery and Design in Soft and Biological Materials
Sunday, January 7th through Saturday, January 13th, 2018
Email address *
Application Deadline: 10/31/17
Conference Description:
Data-driven modeling approaches and machine learning have opened new paradigms in the understanding, engineering, and design of soft and biological materials. This Aspen Winter Conference aims to convene theoretical, computational, and experimental researchers and practitioners in physics, materials science, bioengineering, and chemical science to advance interdisciplinary collaboration and understanding in data-enabled materials and molecular design.

The promise of materials design through machine learning is great, and practitioners worldwide are beginning to embrace this new modality to design and engineer peptides, proteins, DNA, colloids, organic photovoltaics and semi-conductors, polymers, and hydrogels.

This event will bring together experimental and theoretical researchers in soft materials and biology, along with experts in machine learning, statistics, and applied mathematics, to define and codify the key directions, objectives, and methodologies for this field, and determine how to best engage physical modeling tools and experimental characterization techniques with one another and with data-driven tools to guide and accelerate soft and biological materials discovery and design.

Scientific Organizing Committee:
Erik Luijten, Materials Science and Applied Math, Northwestern University
Gerard Wong, Bioengineering, UCLA
Andrew Ferguson*, Materials Science and Chemical Engineering, UIUC

*Denotes physicist in charge of diversity. The Aspen Center for Physics is committed to a significant participation of women and under-represented groups in all of its programs.

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