Support Vector Machines, Convolutions, and Careers in Data Science

Monday March 27 at 4pm in Olin 201. Speaker: Dr. Elly Farnell. Support vector machines are one of many techniques within the realm of classification problems in machine learning – given labeled training data that contains multiple classes (e.g. pictures of cats and dogs with associated labels), we define an algorithm to determine which class a novel data point belongs to (in the cats and dogs example, we’d like our algorithm to be able to tell us whether a new picture contains a cat or a dog). I’ll talk about theoretical results related to support vector machines that we recently published in Foundations of Data Science, which use a classical theorem from topology to characterize when a linear classifier is optimal. I’ll also talk about some of the work I do at Amazon Web Services (AWS) and will highlight how a particular type of function called a convolution has played a particularly useful role. Finally, as someone who has transitioned from academia to a data science career in industry, I’ll discuss what background and skills make a strong foundation to compete for and thrive in data science roles.

BIO: Elly Farnell is a Senior Research Scientist at Amazon Web Services (AWS), a subsidiary of Amazon that provides cloud-based resources to support a variety of customer use cases, such as storage, databases, and machine learning. Her research in AWS infrastructure involves modeling data center resources like power and cooling. She serves as a member of the global leadership team for the Amazon Women in Engineering affinity group, which seeks to make Amazon the best place to work for women and non-binary individuals in technical roles. In her past work, Elly was as a faculty member at Kenyon College, where she focused on developing teaching-related innovations and providing courses to prepare students for careers in business, industry, and government. She also worked in a research scientist position at Colorado State University on compressive sensing projects funded by Department of Defense and Department of Energy STTR grants. Elly completed her undergraduate degree in mathematics at Whitman College in 2004 and her master’s and Ph.D. in mathematics at Colorado State University in 2010.