Dr. Steven Koonin
Director, Center for Urban Sciences and Progress at New York University
Date: November 20, 2019
Time: 5:00 PM
Location: Talley Student Union Coastal Ballroom
Talk Title: Urban Data
For the first time in history, more than half of the world’s population lives in urban areas; in just a few more decades, the world’s population will exceed 9 billion, 70 percent of whom will live in cities. Enabling those cities to deliver services effectively, efficiently, and sustainably while keeping their citizens safe, healthy, prosperous, and well-informed will be among the most important undertakings in this century. I will review how work on data acquisition, integration and analysis at NYU’s Center for Urban Science and Progress is leading to a better understanding (and hence improvement) of urban systems. Novel analyses of persistent synoptic imagery will be an important part of the story.
Steven E. Koonin, a University Professor at New York University, was the founding director of NYU’s Center for Urban Science and Progress from 2012-2018. Before joining NYU, Dr. Koonin served as the second Under Secretary for Science at the U.S. Department of Energy from May 2009 through November 2011. In that capacity, he oversaw technical activities across the Department’s science, energy, and security activities and led the Department’s first Quadrennial Technology Review for energy. Before joining the government, Dr. Koonin spent five years as Chief Scientist for BP plc, where he focused on alternative and renewable energy technologies. Dr. Koonin was a professor of theoretical physics at California Institute of Technology (Caltech) from 1975-2006 and was the Institute’s Provost for almost a decade. He is a member of the U.S. National Academy of Sciences and the JASON advisory group. Dr. Koonin holds a B.S. in Physics from Caltech and a Ph.D. in Theoretical Physics from MIT (1975) and is a trustee of the Institute for Defense Analyses.
Past 2019 Events
Dr. Cynthia Rudin
Professor of Computer Science, Electrical and Computer Engineering, and Statistical Science, Duke University
Date: November 6, 2019
Time: 4:00 PM
Location: James B. Hunt Library Duke Energy Hall
Talk Title: Secrecy, Criminal Justice, and Variable Importance
The US justice system often uses a combination of (biased) human decision makers and complicated black box proprietary algorithms for high stakes decisions that deeply affect individuals. All of this is still happening, despite the fact that for several years, we have known that interpretable machine learning models were just as accurate as any complicated machine learning methods for predicting criminal recidivism. It is much easier to debate the fairness of an interpretable model than a proprietary model. The most popular proprietary model, COMPAS, was accused by the ProPublica group of being racially biased in 2016, but their analysis was flawed and the true story is much more complicated; their analysis relies on a flawed definition of variable importance that was used to identify the race variable as being important.
In this talk, I will start by introducing a very general form of variable importance, called model class reliance. Model class reliance measures how important a variable is to any sufficiently accurate predictive model within a class. I will use this and other data-centered tools to provide our own investigation of whether COMPAS depends on race, and what else it depends on. Through this analysis, we find another problem with using complicated proprietary models, which is that they seem to be often miscomputed. An easy fix to all of this is to use interpretable (transparent) models instead of complicated or proprietary models in criminal justice.
Cynthia Rudin is a professor of computer science, electrical and computer engineering, statistics, and mathematics at Duke University, and directs the Prediction Analysis Lab. Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo, and a PhD in applied and computational mathematics from Princeton University. She is a three time winner of the INFORMS Innovative Applications in Analytics Award. She holds an NSF CAREER award, was named as one of the “Top 40 Under 40” by Poets and Quants in 2015, and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. She is past chair of the INFORMS Data Mining Section, and past chair of the Statistical Learning and Data Science section of the American Statistical Association. She also serves on (or has served on) committees for DARPA, the National Institute of Justice, the National Academy of Sciences (for both statistics and criminology/law), and AAAI. She is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics. She will be the Thomas Langford Lecturer at Duke University during the 2019-2020 academic year.
Dr. Richard Hendra
Director, MDRC Center for Data Insights
Date: October 23, 2019
Time: 5:00 PM
Location: Talley Student Union Coastal Ballroom
Talk Title: Data Science to Improve Social Programs
Richard Hendra directs MDRC’s Center for Data Insights. At this talk he will describe how MDRC, a social policy research organization, uses data science to complement MDRC’s mission of building knowledge and solutions to some of the nations toughest social policy challenges. All of these efforts have in common a goal of leveraging already collected data to derive actionable insights to help improve well-being among low income individuals and families.
Various topics and initiatives will be discussed including:
- a nonprofit initiative that focuses on leveraging MIS data to improve program targeting.
- a national effort to improve data analytics capacity and infrastructure in the TANF system.
- efforts to derive data insights retrospectively to inform future programs through long term analysis of past studies.
- the creation of an ‘end to end’ methodology blending operational, data, and behavioral science.
Hendra will also discuss how data science fits within long term learning agendas (as a complement to the causal inference studies that MDRC is known for.)
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