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Past Events › IDSS Distinguished Seminar Series

A monthly lecture series featuring prominent global leaders and academics sharing research in areas that are impacted by the emergence of big data.

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October 2019

Theoretical Foundations of Active Machine Learning

October 7, 2019 @ 4:00 pm - 5:00 pm

Rob Nowak (University of Wisconsin - Madison)

E18-304

Title: Theoretical Foundations of Active Machine Learning Abstract: The field of Machine Learning (ML) has advanced considerably in recent years, but mostly in well-defined domains using huge amounts of human-labeled training data. Machines can recognize objects in images and translate text, but they must be trained with more images and text than a person can see in nearly a lifetime.  The computational complexity of training has been offset by recent technological advances, but the cost of training data is measured in…

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September 2019

Selection and Endogenous Bias in Studies of Health Behaviors

September 30, 2019 @ 4:00 pm - 5:00 pm

Emily Oster (Brown University)

E18-304

Abstract: Studies of health behaviors using observational data are prone to bias from selection in behavior choices. How important are these biases? Are they dynamic - that is, are they influenced by the recommendations we make? Are there formal assumptions under which we can use information we have about selection on observed variables to learn about the possible bias from unobserved selection? About the Speaker: Emily Oster is a professor of economics. Prior to coming to Brown she was an…

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May 2019

Design and Analysis of Two-Stage Randomized Experiments

May 7, 2019 @ 4:00 pm - 5:00 pm

Kosuke Imai (Harvard University)

E18-304

Abstract: In many social science experiments, subjects often interact with each other and as a result, one unit's treatment can influence the outcome of another unit. Over the last decade, a significant progress has been made towards causal inference in the presence of such interference between units. In this talk, we will discuss two-stage randomized experiments, which enable the identification of the average spillover effects as well as that of the average direct effect of one's own treatment. In particular,…

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April 2019

A Particulate Solution: Data Science in the Fight to Stop Air Pollution and Climate Change | IDSS Distinguished Speaker Seminar

April 2, 2019 @ 4:00 pm - 5:00 pm

Francesca Dominici (Harvard University)

E18-304

Abstract: What if I told you I had evidence of a serious threat to American national security – a terrorist attack in which a jumbo jet will be hijacked and crashed every 12 days. Thousands will continue to die unless we act now. This is the question before us today – but the threat doesn’t come from terrorists. The threat comes from climate change and air pollution. We have developed an artificial neural network model that uses on-the-ground air-monitoring data…

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March 2019

A Theory for Representation Learning via Contrastive Objectives

March 5, 2019 @ 4:00 pm - 5:00 pm

Sanjeev Arora (Princeton University)

32-155

Abstract: Representation learning seeks to represent complicated data (images, text etc.) using a vector embedding, which can then be used to solve complicated new classification tasks using simple methods like a linear classifier. Learning such embeddings is an important type of unsupervised learning (learning from unlabeled data) today. Several recent methods leverage pairs of "semantically similar" data points (eg sentences occuring next to each other in a text corpus). We call such methods contrastive learning (another term would be "like…

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February 2019

Collective Decision Making: Theory and Experiments

February 5, 2019 @ 4:00 pm - 5:00 pm

Leeat Yariv (Princeton University)

32-155

Abstract: Ranging from jury decisions to political elections, situations in which groups of individuals determine a collective outcome are ubiquitous. There are two important observations that pertain to almost all collective processes observed in reality. First, decisions are commonly preceded by some form of communication among individual decision makers, such as jury deliberations, or election polls. Second, even when looking at a particular context, say U.S. civil jurisdiction, there is great variance in the type of institutions that are employed…

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December 2018

The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility

December 3, 2018 @ 4:00 pm - 5:00 pm

Raj Chetty (Harvard University)

32-155

Abstract: We construct a publicly available atlas of children’s outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population. For each tract, we estimate children’s earnings distributions, incarceration rates, and other outcomes in adulthood by parental income, race, and gender. These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up. We find that children’s outcomes vary sharply across nearby areas: for children of parents at…

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November 2018

The Regression Discontinuity Design: Methods and Applications

November 5, 2018 @ 4:00 pm - 5:00 pm

Rocio Titiunik (University of Michigan)

E18-304

Abstract: The Regression Discontinuity (RD) design is one of the most widely used non-experimental strategies for the study of treatment effects in the social, behavioral, biomedical, and statistical sciences. In this design, units are assigned a score and a treatment is offered if the value of that score exceeds a known threshold---and withheld otherwise. In this talk, I will discuss the assumptions under which the RD design can be used to learn about treatment effects, and how to make valid…

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October 2018

Can machine learning survive the artificial intelligence revolution?

October 16, 2018 @ 4:00 pm - 5:00 pm

Francis Bach (INRIA)

32-141

  Abstract: Data and algorithms are ubiquitous in all scientific, industrial and personal domains. Data now come in multiple forms (text, image, video, web, sensors, etc.), are massive, and require more and more complex processing beyond their mere indexation or the computation of simple statistics, such as recognizing objects in images or translating texts. For all of these tasks, commonly referred to as artificial intelligence (AI), significant recent progress has allowed algorithms to reach performances that were deemed unreachable a…

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September 2018

Science for Policy 2.0

September 11, 2018 @ 4:00 pm - 5:00 pm

Vladimír Šucha (Joint Research Centre)

32-141

We live in an increasingly polarized present, looking to a complex and uncertain future while basing our legislative decisions on systems of the past. We need the processes and structures that underpin our political decision-making to be aligned with the complexities of the 21st century. Such changes cannot be undertaken by a technocratic elite, potentially disenfranchising citizens further from their governing institutions. Rather, political institutions must seek to improve transparency, openness, and accountability. The great divide between science and policy…

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