## December 2019

## Automating the Digitization of Historical Data on a Large Scale

Melissa Dell (Harvard University)

E18-304

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

## SES PhD Admissions Webinar

Ali Jadbabaie (MIT)

Learn about admission to the Social and Engineering Systems Doctoral Program. Webinars are led by a member of the IDSS faculty who introduces the program and answers your questions. Please register in advance.

Find out more »## LIDS Seminar – Rayadurgam Srikant (University of Illinois at Urbana-Champaign)

Rayadurgam Srikant (University of Illinois at Urbana-Champaign)

32-155

TBD Bio: ____________________________________ The LIDS Seminar Series features distinguished speakers who provide an overview of a research area, as well as exciting recent progress in that area. Intended for a broad audience, seminar topics span the areas of communications, computation, control, learning, networks, probability and statistics, optimization, and signal processing.

Find out more »## Automated Data Summarization for Scalability in Bayesian Inference

Tamara Broderick (MIT)

E18-304

Abstract: Many algorithms take prohibitively long to run on modern, large data sets. But even in complex data sets, many data points may be at least partially redundant for some task of interest. So one might instead construct and use a weighted subset of the data (called a “coreset”) that is much smaller than the original dataset. Typically running algorithms on a much smaller data set will take much less computing time, but it remains to understand whether the output…

Find out more »## A Causal Exposure Response Function with Local Adjustment for Confounding: A study of the health effects of long-term exposure to low levels of fine particulate matter

Francesca Dominici (Harvard University)

E18-304

Abstract: In the last two decades, ambient levels of air pollution have declined substantially. Yet, as mandated by the Clean Air Act, we must continue to address the following question: is exposure to levels of air pollution that are well below the National Ambient Air Quality Standards (NAAQS) harmful to human health? Furthermore, the highly contentious nature surrounding environmental regulations necessitates casting this question within a causal inference framework. Several parametric and semi-parametric regression modeling approaches have been used to…

Find out more »## Stability of a Fluid Model for Fair Bandwidth Sharing with General File Size Distributions

Ruth J Williams (University of California, San Diego)

E18-304

Abstract: Massoulie and Roberts introduced a stochastic model for a data communication network where file sizes are generally distributed and the network operates under a fair bandwidth sharing policy. It has been a standing problem to prove stability of this general model when the average load on the system is less than the network’s capacity. A crucial step in an approach to this problem is to prove stability of an associated measure-valued fluid model. We shall describe prior work on this question done under various strong assumptions and…

Find out more »## LIDS Seminar – Sujay Sanghavi (University of Texas at Austin)

Sujay Sanghavi (University of Texas at Austin)

32-155

TBD Bio: ____________________________________ The LIDS Seminar Series features distinguished speakers who provide an overview of a research area, as well as exciting recent progress in that area. Intended for a broad audience, seminar topics span the areas of communications, computation, control, learning, networks, probability and statistics, optimization, and signal processing.

Find out more »## Understanding machine learning with statistical physics

Lenka Zdeborová (Institute of Theoretical Physics, CNRS)

E18-304

Abstract: The affinity between statistical physics and machine learning has long history, this is reflected even in the machine learning terminology that is in part adopted from physics. Current theoretical challenges and open questions about deep learning and statistical learning call for unified account of the following three ingredients: (a) the dynamics of the learning algorithm, (b) the architecture of the neural networks, and (c) the structure of the data. Most existing theories are not taking in account all of those…

Find out more »## Artificial Bayesian Monte Carlo Integration: A Practical Resolution to the Bayesian (Normalizing Constant) Paradox

Xiao-Li Meng (Harvard University)

E18-304

Abstract: Advances in Markov chain Monte Carlo in the past 30 years have made Bayesian analysis a routine practice. However, there is virtually no practice of performing Monte Carlo integration from the Bayesian perspective; indeed,this problem has earned the “paradox” label in the context of computing normalizing constants (Wasserman, 2013). We first use the modeling-what-we-ignore idea of Kong et al. (2003) to explain that the crux of the paradox is not with the likelihood theory, which is essentially the same…

Find out more »## SES PhD Admissions Info Session

Ali Jadbabaie (MIT)

E18-304

Learn about admission to the Social and Engineering Systems Doctoral Program. Info session is hosted by a member of the IDSS faculty and an SES student who introduce the program and answer your questions. Please register in advance.

Find out more »