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February 2020

Diffusion K-means Clustering on Manifolds: provable exact recovery via semidefinite relaxations

February 14, 2020 @ 11:00 am - 12:00 pm

Xiaohui Chen (University of Illinois at Urbana-Champaign)

E18-304

Abstract: We introduce the diffusion K-means clustering method on Riemannian submanifolds, which maximizes the within-cluster connectedness based on the diffusion distance. The diffusion K-means constructs a random walk on the similarity graph with vertices as data points randomly sampled on the manifolds and edges as similarities given by a kernel that captures the local geometry of manifolds. Thus the diffusion K-means is a multi-scale clustering tool that is suitable for data with non-linear and non-Euclidean geometric features in mixed dimensions. Given…

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Risk-Sensitive Safety Analysis and Control for Trustworthy Autonomy

February 12, 2020 @ 12:00 pm - 1:00 pm

Margaret Chapman (UC Berkeley)

E18-304

Abstract: Methods for managing dynamic systems typically invoke one of two perspectives. In the worst-case perspective, the system is assumed to behave in the worst possible way; this perspective is used to provide formal safety guarantees. In the risk-neutral perspective, the system is assumed to behave as expected; this perspective is invoked in reinforcement learning and stochastic optimal control. While the worst-case perspective is useful for safety analysis, it can lead to unnecessarily conservative decisions, especially in settings where uncertainties…

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Webinar: Inside the MITx MicroMasters Program in Statistics and Data Science

February 12, 2020 @ 12:00 pm - 1:00 pm

Devavrat Shah, Karene Chu

online

Interested in starting your data science journey? Register for this special free virtual event. You'll receive a confirmation e-mail with further details about the webinar. Demand for professionals skilled in data, analytics, and machine learning is exploding. A recent report by IBM and Burning Glass states that there will be 364K new job openings in data-driven professions this year in the US alone. Data scientists bring value to organizations across industries because they are able to solve complex challenges with…

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Gaussian Differential Privacy, with Applications to Deep Learning

February 7, 2020 @ 11:00 am - 12:00 pm

Weijie Su (University of Pennsylvania)

E18-304

Abstract: Privacy-preserving data analysis has been put on a firm mathematical foundation since the introduction of differential privacy (DP) in 2006. This privacy definition, however, has some well-known weaknesses: notably, it does not tightly handle composition. This weakness has inspired several recent relaxations of differential privacy based on the Renyi divergences. We propose an alternative relaxation we term “f-DP”, which has a number of nice properties and avoids some of the difficulties associated with divergence based relaxations. First, f-DP preserves…

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Data Science and Big Data Analytics: Making Data-Driven Decisions

February 3, 2020

online

Developed by 11 MIT faculty members at IDSS, this seven-week course is specially designed for data scientists, business analysts, engineers and technical managers looking to learn strategies to harness data. Offered by MIT xPRO. Course begins February 3, 2020.

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January 2020

25th Annual LIDS Student Conference

January 29, 2020 @ 8:00 am - January 30, 2020 @ 5:00 pm

32-141

Welcome to the 24th annual LIDS Student Conference! The annual LIDS student conference provides an opportunity for graduate students to present their research to peers as well as to the community at large. The conference will be held on January 29 – 30, at MIT's Stata Center Rooms 32-141.

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December 2019

The Statistical Finite Element Method

December 11, 2019 @ 4:00 pm - 5:00 pm

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…

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Inferring the Evolutionary History of Tumors

December 6, 2019 @ 11:00 am - 12:00 pm

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…

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SES Dissertation Defense – Ian Schneider

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

Ian Schneider (MIT)

E18-304

Ian Schneider

Market Design Opportunities for an Evolving Power System

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Flexible Perturbation Models for Robustness to Misspecification

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

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…

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