Loading Events

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.

Events Search and Views Navigation

Event Views Navigation

December 2020

Mass Incarceration and the Challenge of Social Research

December 7, 2020 @ 4:00 pm - 5:00 pm

Bruce Western (Columbia University)


IDSS will host Prof. Bruce Western as part of the Distinguished Speaker Seminar series. Prof. Westerns research has examined the causes, scope, and consequences of the historic growth in U.S. prison populations. He is Co-Director of the Justice Lab at Columbia University.

Find out more »

November 2020

An Introduction to Proximal Causal Learning

November 2, 2020 @ 4:00 pm - 5:00 pm

Eric Tchetgen Tchetgen (University of Pennsylvania)


Please join us on November 2, 2020 at 4pm for the Distinguished Speaker Seminar with Eric J. Tchetgen Tchetgen, Luddy Family President’s Distinguished Professor and Professor of Statistics at the University of Pennsylvania.

Find out more »

October 2020

Social Networks and the Market for News

October 5, 2020 @ 4:00 pm - 5:00 pm

Rachel Kranton (Duke University)


Please join us on October 5, 2020 at 4pm for the IDSS Distinguished Speaker Seminar with Rachel Kranton, James B. Duke Distinguished Professor of Economics at Duke University.

Find out more »

May 2020

The Ethical Algorithm

May 19, 2020 @ 4:00 pm - 5:00 pm

Michael Kearns (University of Pennsylvania)


https://youtu.be/IATv0m5U5z8 Title: The Ethical Algorithm Abstract: Many recent mainstream media articles and popular books have raised alarms over anti-social algorithmic behavior, especially regarding machine learning and artificial intelligence. The concerns include leaks of sensitive personal data by predictive models, algorithmic discrimination as a side-effect of machine learning, and inscrutable decisions made by complex models. While standard and legitimate responses to these phenomena include calls for stronger and better laws and regulations, researchers in machine learning, statistics and related areas are…

Find out more »

April 2020

[POSTPONED] The Blessings of Multiple Causes

April 13, 2020 @ 4:00 pm - 5:00 pm

David Blei (Columbia University)


*Please note: this event has been POSTPONED until Fall 2020* See MIT’s COVID-19 policies for more details.   Title: The Blessings of Multiple Causes Abstract: Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods require that we observe all confounders, variables that affect both the causal variables and the outcome variables. But whether we have observed all confounders is a famously untestable assumption. We describe the deconfounder, a way to do causal…

Find out more »

[POSTPONED] Guido Imbens – The Applied Econometrics Professor and Professor of Economics, Graduate School of Business, Stanford University

April 7, 2020 @ 4:00 pm - 5:00 pm


IDSS will host Prof. Guido Imbens as part of the Distinguished Speaker Seminar series. Prof. Guido Imbens’ primary field of interest is Econometrics. Research topics in which he is interested include: causality, program evaluation, identification, Bayesian methods, semi-parametric methods, instrumental variables.

Find out more »

March 2020

Does Revolution Work? Evidence from Nepal

March 3, 2020 @ 4:00 pm - 5:00 pm

Rohini Pande (Yale University)


The last half century has seen the adoption of  democratic institutions in much of the developing world. However, the conditions under which de jure democratization leads to the representation of historically disadvantaged groups remains debated as do the implications of descriptive representation for policy inclusion. Using detailed administrative and survey data from Nepal, we examine political selection in a new democracy, the implications for policy inclusion and the role of conflict in affecting political transformation. I situate these findings in the context of…

Find out more »

December 2019

Automating the Digitization of Historical Data on a Large Scale

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

Melissa Dell (Harvard University)


https://youtu.be/mnM7ePr6xqM Over the past two centuries, we have transitioned from an overwhelmingly agricultural world to one with vastly different patterns of economic organization. This transition has been remarkably uneven across space and time, and has important implications for some of the most central challenges facing societies today. Deepening our understanding of the determinants of economic transformation requires data on the long-run trajectories of individuals and firms. However, these data overwhelmingly remain trapped in hard copy, with cost estimates for manual…

Find out more »

November 2019

Causal Inference in the Age of Big Data

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

Jasjeet Sekhon (UC Berkeley)


The rise of massive data sets that provide fine-grained information about human beings and their behavior offers unprecedented opportunities for evaluating the effectiveness of social, behavioral, and medical treatments. With the availability of fine-grained data, researchers and policymakers are increasingly unsatisfied with estimates of average treatment effects based on experimental samples that are unrepresentative of populations of interest. Instead, they seek to target treatments to particular populations and subgroups. Because of these inferential challenges, Machine Learning (ML) is now being…

Find out more »

October 2019

Theoretical Foundations of Active Machine Learning

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

Rob Nowak (University of Wisconsin - Madison)


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…

Find out more »
+ Export Events

© MIT Institute for Data, Systems, and Society | 77 Massachusetts Avenue | Cambridge, MA 02139-4307 | 617-253-1764 |