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Materials Science Seminar Series presents Professor Rose Cersonsky on Thursday, November 16, from 4 to 5 p.m. The seminar is hosted by Professor Jason Kawasaki and will be held in MS&E building room 265. Prof. Rose Cersonsky will be discussing Data-driven approaches to chemical and materials sciences: the importance of data selection, representation, and interpretability.
Like many other fields, there has been a recent and overwhelming wave of machine learning and artificial intelligence methods being employed in the chemical sciences. While these methods have the undoubted ability to drive innovation and capabilities, their application to chemical sciences requires a nuanced understanding of molecular representations and structure-property relationships.
In this talk, I will discuss the role of molecular featurization – how we transform atoms and molecules into mathematical signals appropriate for machine-learning thermodynamic quantities – and unsupervised analyses that allow us to easily understand and assess these so-called “featurizations” in the context of complex machine learning tasks. In doing so, I will demonstrate how linear methods – that constitute the simplest, most robust, and most transparent approaches to automatically processing large amounts of data – can be leveraged to understand molecular crystallization and aid in pharmaceutical engineering.
All methods discussed are available through the open-source scikit-matter (scikit-matter.readthedocs.io/) software, an official scikit-learn companion that implement methods born out of the materials and chemistry communities.
Rose K. Cersonsky received her Bachelor of Science degree in Materials Science and Engineering from the University of Connecticut in 2014. She then went on to obtain her PhD in Macromolecular Science and Engineering from the University of Michigan in 2019 working alongside Professor Sharon C. Glotzer. Rose’s doctoral thesis was titled “Designing Nanoparticles for Self-Assembly of Novel Materials,” for which she received, among other honors, the 2021 Victor K. LaMer Award from the Colloids Division of the American Chemical Society. She previously worked as a postdoctoral researcher in the Laboratory of Computational Science and Modeling (COSMO) at Ecole Polytechnique Federale de Lausanne (EPFL) in Lausanne, Switzerland before starting her position as an assistant professor of Chemical and Biological Engineering at the University of Wisconsin-Madison.
In addition to research, Rose has devoted herself to scientific service, leading and coordinating multiple outreach programs at both the University of Connecticut and the University of Michigan, and publishing work focused on community engagement and gender equity in educational journals.