December 2019
The Statistical Finite Element Method
Mark Girolami (University of Cambridge)
E18-304
Abstract: The finite element method (FEM) is one of the great triumphs of modern day applied mathematics, numerical analysis and software development. Every area of the sciences and engineering has been positively impacted by the ability to model and study complex physical and natural systems described by systems of partial differential equations (PDE) via the FEM . In parallel the recent developments in sensor, measurement, and signalling technologies enables the phenomenological study of systems as diverse as protein signalling in the…
Find out more »Inferring the Evolutionary History of Tumors
Simon Tavaré (Columbia University)
E18-304
Abstract: Bulk sequencing of tumor DNA is a popular strategy for uncovering information about the spectrum of mutations arising in the tumor, and is often supplemented by multi-region sequencing, which provides a view of tumor heterogeneity. The statistical issues arise from the fact that bulk sequencing makes the determination of sub-clonal frequencies, and other quantities of interest, difficult. In this talk I will discuss this problem, beginning with its setting in population genetics. The data provide an estimate of the…
Find out more »SES Dissertation Defense – Ian Schneider
Ian Schneider (MIT)
E18-304
Market Design Opportunities for an Evolving Power System
Find out more »Flexible Perturbation Models for Robustness to Misspecification
Jeffrey Miller (Harvard University)
E18-304
Abstract: In many applications, there are natural statistical models with interpretable parameters that provide insight into questions of interest. While useful, these models are almost always wrong in the sense that they only approximate the true data generating process. In some cases, it is important to account for this model error when quantifying uncertainty in the parameters. We propose to model the distribution of the observed data as a perturbation of an idealized model of interest by using a nonparametric…
Find out more »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…
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