2013 - 2014

2013 Oct 31

MIT ORC: Alex Belloni

4:15pm to 5:30pm

Location: 

E51-335
Uniform Inference After Model Selection
2013 Dec 05

IACS Sem: David Cox

4:00pm to 5:15pm

Location: 

Maxwell Dworkin G125
Can Neuroscience Help Us Build Better Computer Vision and Machine Learning Systems?
2014 Feb 06

MIT ORC: Rahul Mazumder

4:15pm to 5:30pm

Location: 

E62-650
Learning with Low Rank Matrices: Flexible Modeling and Scalable Computation
2014 Jan 24

StatColloq: John Duchi

4:15pm to 5:15pm

Location: 

Science Center Rm. 705
Computation, Communication, and Privacy Constraints on Statistical Estimation
2014 Mar 10

StatColloq: Rui Tuo

4:15pm to 5:15pm

Location: 

Science Center Rm. 705
A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties
2014 Mar 13

HSPHBiostat: Adam C. Siepel

12:30pm to 2:00pm

Location: 

FXB G12
New Methods for the Analysis of Human Population Genomic Data
2014 Mar 29

Symposium: Donald Rubin

1:00pm to 5:30pm

Location: 

Lessin Lecture Hall (G115), Maxwell Dworkin Bldg, Harvard Univ.
A Celebration of Donald Rubin on his 70th Birthday (sponsored by the Harvard Statistics Department)
2014 Apr 23

MIT CSAIL: Janet Wiener

4:00pm to 5:00pm

Location: 

Bldg 32 Kiva Conference Room, 4th Floor, G449
Scuba: Diving into Data at Facebook (part of the MIT Big Data Initiative)
2013 Oct 30

Future of Statistics Unconference

12:00pm to 1:00pm

Location: 

Google Hangouts & YouTube
There will be 10 minute talks on a wide variety of future directions in statistics by Hadley Wickham (Chief Scientist at RStudio and creator of ggplot2), Hongkai Ji (Johns Hopkins professor and Harvard Stat alumnus), Sinan Aral (MIT), Daniela Witten (Univ of Washington), Hilary Mason (Data Scientist in Residence at Accel & co-founder of HackNY) and Joe Blitzstein (Harvard Stat professor). Here is more publicity.
2014 Aug 07

AstroStatTalk: Jessi Cisewski

11:00am to 12:00pm

Location: 

Pratt conference room (G04), CfA
Approximate Bayesian Computing in Astronomy A standard Bayesian statistical analysis relies on the specification of a likelihood function. Unfortunately the likelihood is not always known or tractable. Approximate Bayesian computation (ABC) provides a framework for performing inference in cases where the likelihood is not available, but it is possible (and computationally efficient) to generate a sample from the forward process that mimics the data-generation process. I will introduce and discuss ABC with a goal of illustrating how it can be useful in astronomy. Throughout, astronomical... Read more about AstroStatTalk: Jessi Cisewski
2013 Oct 31

HSPHBiostat: Jesse Berlin

1:30pm to 3:30pm

Location: 

FXB G13
[Lagakos Distinguished Alumni Award] Perspectives of a Recovering Academic Biostatistician: Transitions and Lessons Learned about Statistics and Beyond

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