Thomas Angsten

Thomas Angsten has worked in a variety of roles in the Machine Learning and Data Science fields. In 2020, they began working as a Machine Learning Engineer for Cushion. From 2018 to 2020, they worked as a Machine Learning Engineer for Square's eCommerce Team. From 2016 to 2018, they participated in the Career Development Initiative in the Physical Sciences (CDIPS) Data Science Workshop, where they collaborated with two teammates to develop a machine learning algorithm for forecasting product demand. From 2015 to 2016, they served as a Mentor for the Center for Sustainable Materials Chemistry Summer Research Program. From 2013 to 2015, they were a Graduate Student Researcher at the University of California, Berkeley, applying First-principles Density Functional Theory to the modeling of elastic, polar, and electronic properties of inorganic crystals. From 2011 to 2013, they were a Student Researcher at UW-Madison, producing a large database of diffusion energetics for elements and binary alloys.

Thomas Angsten received their Bachelor's degree in Materials Science and Engineering from the University of Wisconsin-Madison in 2013. Thomas then went on to pursue a Doctor of Philosophy in Materials Science and Engineering from the University of California, Berkeley, which they completed in 2018.