Machine Learning for Molecular Sensing

Abstract

Mass spectrometry is a method that chemists use to identify unknown molecules. Spectra from unknown samples are compared against existing libraries of mass spectra; highly matching spectra are considered candidates for the identity of the molecule. I will discuss some work in using machine learning models to predict mass spectra to expand the coverage of libraries to improve the ability of identifying spectra through mass spectrometry.

The second project will discuss a more natural form of molecular sensing: olfaction. I will discuss some work my team has done in predicting human odor labels for individual molecules, and some of the resulting consequences.

Bio:

Jennifer is a research engineer with the Brain Research team in Cambridge, MA. She completed her PhD in Chemical Physics at Harvard University. Her primary research interests are applications of machine learning for small molecules. She has published research on applications of machine learning to reaction prediction, inverse design of molecules, mass spectrometry prediction, and olfaction.

Please join the meeting in Sococo VH 102, or Zoom.

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