Applied Data Science Invited Speakers

The Applied Data Science Invited Talks will provide a venue for leading experts in the world of applied data mining and knowledge discovery. These invited talks will feature highly influential speakers who have directly contributed to successful data mining applications in their respective fields. The talks and discussions will focus on innovative and leading-edge, large-scale industry or government applications of data mining in areas such as finance, health-care, bio-informatics, public policy, infrastructure, telecommunications, social media and computational advertising.

Keynote: Kristian Lum

Kristian Lum


Fairness, Accountability, and Transparency in predictive models in criminal justice

The related topics of fairness, accountability, and transparency in predictive modeling have seen increased attention over the last several years. One application area where these topics are particularly important is criminal justice. In this talk, I will give an overview of my work in this area, highlighting specific examples including predictive policing and pre-trial risk assessment that illustrate the importance of each of these concepts.

Kristian Lum is the Lead Statistician at the Human Rights Data Analysis Group (HRDAG), where she leads the HRDAG project on criminal justice in the United States. Previously, Kristian worked as a research assistant professor in the Virginia Bioinformatics Institute at Virginia Tech and as a data scientist at DataPad, a small technology start-up. Kristian’s research primarily focuses on examining the uses of machine learning in the criminal justice system and has concretely demonstrated the potential for machine learning-based predictive policing models to reinforce and, in some cases, amplify historical racial biases in law enforcement. She has also applied a diverse set of methodologies to better understand the criminal justice system: causal inference methods to explore the causal impact of setting bail on the likelihood of pleading or being found guilty; and agent-based modeling methods derived from epidemiology to study the disease-like spread of incarceration through a social influence network. Additionally, Kristian’s work encompasses the development of new statistical methods that explicitly incorporate fairness considerations and advancing HRDAG’s core statistical methodology—record-linkage and capture-recapture methods for estimating the number of undocumented conflict casualties. She is the primary author of the dga package, open source software for population estimation for the R computing environment. Kristian received an MS and PhD from the Department of Statistical Science at Duke University and a BA in Mathematics and Statistics from Rice University.