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Efficient derivative-free Bayesian inference for large-scale inverse problems

Jiaoyang Huang (University of Pennsylvania)

Abstract: We consider Bayesian inference for large-scale inverse problems, where computational challenges arise from the need for the repeated evaluations of an expensive forward model, which is often given as a black box or is impractical to differentiate. In this talk I will propose a new derivative-free algorithm Unscented Kalman Inversion, which utilizes the ideas from Kalman filter, to efficiently solve these inverse problems. First, I will explain some basics about Variational Inference under general metric tensors. In particular, under the…

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Disinformation and free speech: perspectives on the future of information

Hayden Library and online

A panel of experts from a range of disciplines will share their perspectives on how fact, fiction, and opinion converge, diverge, and occasionally collide. Based on their research, the speakers will share their views on how access to accurate information aligns with free speech; how we can help people evaluate information; and much more. Panelists: Adam Berinsky, Mitsui Professor of Political Science and Director of the MIT Political Experiments Research Lab David Karger, Professor of Computer Science and member of…

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MIT Policy Hackathon 2022: A New (Re)generation

MIT Policy Hackathon is a 48-hour hackathon convened by students from MIT’s Institute for Data, Systems, and Society and the MIT Technology and Policy Program that aims to address some of today’s most relevant societal challenges. Applications received by September 25 will be give priority. Applications received up until October 1, will be reviewed on a rolling basis until the remaining spots are filled. Apply today!

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Geometric EDA for Random Objects

Paromita Dubey (University of Southern California)

Abstract: In this talk I will propose new tools for the exploratory data analysis of data objects taking values in a general separable metric space. First, I will introduce depth profiles, where the depth profile of a point ω in the metric space refers to the distribution of the distances between ω and the data objects. I will describe how depth profiles can be harnessed to define transport ranks, which capture the centrality of each element in the metric space…

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MIT Computing Virtual Grad Fair


Join IDSS's Social and Engineering Systems Doctoral Program (SES), Technology and Policy Program (TPP), and Interdisciplinary Doctoral Program in Statistics (IDPS) at the inaugural MIT Schwarzman College of Computing Virtual Grad School Fair: Academic Programs Showcase. The MIT Schwarzman College of Computing is home to some of the world’s most well-known programs in their field, including a variety of computationally-intensive graduate programs. In these specialized programs, students and faculty address challenging multifaceted problems using data, computational methods, and a host of…

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