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## Topics in Information and Inference Seminar

Caroline Uhler (MIT)

This seminar consists of a series of lectures each followed by a period of informal discussion and social. The topics are at the nexus of information theory, inference, causality, estimation, and non-convex optimization. The lectures are intended to be tutorial in nature with the goal of learning about interesting and exciting topics rather than merely hearing about the most recent results. The topics are driven by the interests of the speakers, and with the exception of the two lectures on…

Find out more »## Reverse hypercontractivity beats measure concentration for information theoretic converses

Jingbo Liu (MIT)

Abstract: Concentration of measure refers to a collection of tools and results from analysis and probability theory that have been used in many areas of pure and applied mathematics. Arguably, the first data science application of measure concentration (under the name ‘‘blowing-up lemma’’) is the proof of strong converses in multiuser information theory by Ahlswede, G'acs and K"orner in 1976. Since then, measure concentration has found applications in many other information theoretic problems, most notably the converse (impossibility) results in…

Find out more »## Stochastics and Statistics Seminar

Tselil Schramm (Harvard University)

MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.

Find out more »## Topics in Information and Inference Seminar

Lizhong Zheng (MIT)

This seminar consists of a series of lectures each followed by a period of informal discussion and social. The topics are at the nexus of information theory, inference, causality, estimation, and non-convex optimization. The lectures are intended to be tutorial in nature with the goal of learning about interesting and exciting topics rather than merely hearing about the most recent results. The topics are driven by the interests of the speakers, and with the exception of the two lectures on…

Find out more »## Stochastics and Statistics Seminar

John Duchi (Stanford University)

MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.

Find out more »## Data Science and Big Data Analytics: Making Data-Driven Decisions

Developed by 10 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 Oct. 15, 2018.

Find out more »## Topics in Information and Inference Seminar

Guy Bresler (MIT)

This seminar consists of a series of lectures each followed by a period of informal discussion and social. The topics are at the nexus of information theory, inference, causality, estimation, and non-convex optimization. The lectures are intended to be tutorial in nature with the goal of learning about interesting and exciting topics rather than merely hearing about the most recent results. The topics are driven by the interests of the speakers, and with the exception of the two lectures on…

Find out more »## Stochastics and Statistics Seminar

Aukosh Jagannath (Harvard University)

MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.

Find out more »## Topics in Information and Inference Seminar

Abbas El Gamal (Stanford University)

## Stochastics and Statistics Seminar

Alan Frieze (Carnegie Mellon University)

MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar

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