RED Talks Video Archive

Dr. Matthew Hansen

Professor, Department of Geographical Sciences, University of Maryland

Date: March 4, 2020
Time: 5:00 PM
Location: SAS Hall, Room 2203
Talk Title: Monitoring Global Land Change

From deforestation to urbanization, the human footprint on the land surface is ever-expanding, converting natural land covers into land uses or intensifying current land uses. Land-use change results in loss of biodiversity, increased greenhouse gas emissions, alteration of hydrological systems, among other impacts. Rates of land-use change can be quantified using time-series earth observation data from satellites. A subset of satellite systems features global acquisition strategies with no or low-cost data access, and a consequent ability to monitor global land cover and land use extent and change. In terms of earth observation infrastructure, we are in a golden age of such satellite systems, including NASA/USGS Landsat satellites, the Sentinel series of the European Space Agency, and also commercial providers such as Planet. The integrated use of multi-source data dramatically improves monitoring capabilities, reducing the uncertainties around many important land dynamics, such as deforestation rates and crop area estimation. In this talk, a number of themes will be presented with a focus on our improving capabilities to accurately quantify global land change.

Matthew Hansen is a remote sensing scientist and a professor of BSOS-Geography at The University of Maryland. His research is focused on developing improved algorithms, data inputs, and thematic outputs which enable the mapping of land cover change at regional, continental, and global scales. Professor Hansen’s work as an Associate Team Member of NASA’s MODIS Land Science Team included the algorithmic development and product delivery of the MODIS Vegetation Continuous Field land cover layers.
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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.

Dr. Cynthia RudinDr. 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 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.
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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.)

Hendra, Ph.D., directs the Center for Data Insights at MDRC which is bringing state of the art data science methods to help harness data to improve programs and better serve populations in need. An expert in experimental design, data science, and workforce policy, he leads several national evaluations across numerous policy areas focused on low income populations. During his tenure at MDRC, Hendra has led the research on a range of welfare, housing, asset building, and workforce development projects. Hendra is also the Principle Investigator or Senior Adviser on several evaluations focused on employment, housing, criminal justice, financial inclusion, and substance abuse issues. Hendra has over two decades of experience teaching statistics and research methods at the graduate level and has been involved in national preparedness training initiatives. He has served on several expert, curriculum, and dissertation committees and has been a reviewer for several foundations and academic journals.
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Dr.Tamara G. Kolda

Sandia National Laboratories

Date: October 29, 2018
Time: 6:00 PM
Location: 2203 SAS Hall
Talk Title: Decomposition: A Mathematical Tool for Data Analysis

Tensors are multiway arrays, and these occur naturally in many data analysis. Consider a series of experiments tracking multiple sensors over time, resulting in a three-way tensor of the form experiment-by-sensor-by-time. Tensor decompositions are powerful tools for data analysis that can be used for data interpretation, dimensionality reduction, outlier detection, and estimation of missing data. In this talk, we consider the mathematical, algorithmic, and computational challenges of tensor methods and highlight their wide ranging utility with examples in neuroscience, chemical detection, social network analysis, and more. We discuss several new developments, including a new “generalized” version of tensor decomposition that allows for alternative statistically-motivated fitting functions.
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Dr. Guillermo Sapiro

Duke University

Date: October 15, 2018
Time: 5:00 PM
Location: Duke Energy Hall C/D, Hunt Library, Centennial Campus, NCSU
Talk Title: It’s Your Data and Your Decision: Learning Representations to Keep Your Privacy

It is becoming increasingly clear that users should own and control their data. Utility providers are also becoming more interested in guaranteeing data privacy. As such, users and utility providers should collaborate in data privacy, a paradigm that has not yet been developed in the privacy research community. We introduce this concept and present explicit architectures where the user controls what characteristics of the data she/he wants to share and what she/he wants to keep private. This is achieved by collaborative learning a sensitization function, either a deterministic or a stochastic one, that retains valuable information for the utility tasks but it also eliminates necessary information for the privacy ones. As illustration examples, we implement them using a plug-and-play approach, where no algorithm is changed at the system provider end, and an adversarial approach, where minor re-training of the privacy inferring engine is allowed. In both cases the learned sanitization function keeps the data in the original domain, thereby allowing the system to use the same algorithms it was using before for both original and privatized data. We show how we can maintain utility while fully protecting private information if the user chooses to do so, even when the first is harder than the second, as in the case here illustrated of identity detection while hiding gender. We also present examples showing how secure devices can be designed to protect the privacy of ordinary people around the device. This talk is based on joint work with M. Bertran, N. Martinez, A. Papadaki, Q. Qiu, M. Rodrigues.
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C. Titus BrownC. Titus Brown

UC Davis

Date: September 11, 2018
Time: 6:00 PM
Location: 2203 SAS Hall
Talk Title: The Secret Life of Microbial Genomes

Recent advances in large scale sequencing of microbial DNA without culturing or isolation gives us easy and direct access to “wild” microbial metagenomes that are otherwise virtually impossible to study. Our recent work has focused on studying the ecology and genomics of microbial genomes and “population” pan-genomes in environmental samples, using tools and approaches developed in our lab and in collaboration with others. We see many ways in which microbial genomes do not particularly resemble our naive expectations, which is leading us into some productive confusion around known knowns, known unknowns, and unknown unknowns in environmental microbial systems.Our work relies heavily on novel methods and technical infrastructure development. In this talk I will present a series of vignettes on our techniques and some of the results, aimed at a general scientific audience.
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Dr. Ingrid DaubechiesDr. Ingrid Daubechies

Date: October 10, 2017
Location: Talley Student Union
Talk Title: Mathematicians Helping Art Historians and Art Conservators

In recent years, mathematical algorithms have helped art historians and art conservators putting together the thousands of fragments into which an unfortunate WWII bombing destroyed world famous frescoes by Mantegna, decide that certain paintings by masters were “roll mates” (their canvases were cut from the same bolt), virtually remove artifacts in preparation for a restoration campaign, and even get more insight into paintings hidden underneath a visible one. The presentation will review these applications, and give a glimpse into the mathematical aspects that make this possible.
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Dr. Elliot InmanDr. Elliot Inman

Date: September 27, 2016
Time: 7:00 PM
Location: Mountains Ballroom, Talley Student Union
Talk Title: Quantification: The Art of Making Data

In this Data Science Initiative talk and the DH Hill Makerspace workshops that follow, we will explore the art of making data – from the mechanics of setting up sensors and digitizing information to generating a data structure that will be useful for making sense of that information. We will experiment with a wide variety of data from recorded sound to human touch and even human emotions, building devices to help gather reliable, valid measures.
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Laura HaasDr. Laura Haas

Sandia National Laboratories

Date: October 18th, 2016
Time: 7:00 PM
Location: Duke Energy Hall, Hunt Library
Talk Title: The Power Behind The Throne: Information Integration in the Age of Data-Driven Discovery

Integrating data has always been a challenge. The information management community has made great progress in tackling this challenge, both on the theory and the practice. But in the last ten years, the world has changed dramatically. New platforms, devices and applications have made huge volumes of heterogeneous data available at speeds never contemplated before, while the quality of the available data has if anything degraded.
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Dr. Jeff Leek

Date: November 2nd, 2016
Time: 7:00 PM
Location: Mountains Ballroom, Talley Student Union
Talk Title: Is Most Published Research Really False?

The accuracy of published research is critical for scientists, physicians and patients who rely on these results. But the fundamental belief in the scientific literature was called into serious question by a paper suggesting most published medical research is false. This claim has launched an entire discipline focused on the crisis of reproducibility and replicability of science. In this talk I will discuss two major open problems inspired by this scientific crisis: how do we know when a study replicates and what is the rate of false discoveries in the scientific literature? In answering these questions I will argue that much of the crisis in science can be attributed to misunderstanding statistics.
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Dr. Deen Freelon

Date: March 15th, 2016
Time: 5:30 PM
Location: Mountains Ballroom, Talley Student Union
Talk Title: Toward a Framework for Inferring Individual-Level Characteristics from Digital Trace Data

Digital traces—records of online activity automatically recorded by the servers that undergird all online activity—allow us to explore age-old communication research questions in unprecedented ways. But one of the greatest challenges in doing so is managing the gap between the research’s conceptual focus and the set of readily available traces. Not every type of trace will be equally valuable from a particular research standpoint, and not every interesting concept will be measurable using the traces to which researchers have access.

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