WHEN: Monday, November 5, 2018 @ 2:45pm
WHERE: Ryder Hall 155
TITLE: "Data Science, Humanely”
ABSTRACT: It is undeniable that machine learning has fundamentally changed what computers can do, especially as access to data sources and processing power continues to become easier. At the same time, the ability for us humans to actually make
sense of these techniques has not progressed at nearly the same pace. In this talk, I will present two recent projects from our group which bring methods from data mining and machine learning into novel visualization techniques. The first project, Gaussian
Cubes, provides interactive, low-latency visual exploration with models fit on hundreds of millions of samples. DimReader, on the other hand, shows how automatic differentiation -- the same technique that drives modern machine learning infrastructure such
as Torch and TensorFlow -- can be used to provide a much deeper understanding of popular dimensionality reduction methods like t-SNE. Time permitting, I will present some additional recent work in the recent field of fairness in machine learning and automated
decision making, specifically on runaway feedback loops and the assessment of black-box models.
BIO: Since 2014, Carlos Scheidegger is an assistant professor in the Department of Computer Science at the University of Arizona. He holds
a PhD in Computing from the University of Utah, where he worked on software infrastructure for scientific collaboration. His current
research interests are in large-scale data analysis, information visualization and, more broadly, what happens "when people meet data". His honors include multiple best paper awards and nominations, and an IBM student fellowship.
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Michelle A. Borkin, Ph.D.
Assistant Professor
College of Computer and Information Science
Northeastern University