Fwd: Oral Exam Announcement - SAM GROSS

[Sam Gross]

Hi all!

Just wanted to let you know after 5 long years (and something like 13 or 14 years in Oberlin House), I am finally going to defend my thesis! 

A few of you mentioned that you might be interested in attending, so the details are below (the rest of you I included because I thought you might like to hear about my transition from cave mode to real people mode).  Please don't inconvenience yourself to come, and maybe bring a book or something if you do to distract your while I talk about the boring math stuff. 

I'd imagine that of the 1hr talk, about 30 min will be understandable to everyone (high level discussion, intro to concepts that I am including for lay audience, acknowledgements), about 10 min will be understandable for people who know what an F-Test is, and the remaining 20 minutes will be for the stats people.

There will be snacks before-hand and I am under the impression that at least a few people from Oberlin will be there if you don't want to be the only person making inappropriate comments in the back of the room.

Best,

-Sam

PS  Sorry if you receive this multiple times.  I have no idea how gmail will treat a bcc to all of the oberlin lists...

---------- Forwarded message ----------
From: Susie Ementon <sus...@stanford.edu>
Date: Wed, May 13, 2015 at 8:41 AM
Subject: Oral Exam Announcement - SAM GROSS
To: stat...@lists.stanford.edu

Dear Statistics Community,

Please see below for the complete details on Sam Gross' oral exam. This is the final dissertation defense.

Date: Wednesday, May 20, 2015
Time: 4:15pm
Location: Sequoia 200 (sequoia hall is searchable on googlemaps)
Title: A Selective Approach to Internal Inference

Abstract: A common goal in modern biostatistics is to form a biomarker signature from high dimensional gene expression data that is predictive of some outcome of interest.  After learning this biomarker signature, an important question to answer is how well it predicts the response compared to classical predictors.  This is challenging, because the biomarker signature is an internal predictor -- one that has been learned using the same dataset on which we want to evaluate it's significance.  We propose a new method for approaching this problem based on the technique of selective inference.  Simulations show that our method is able to properly control the level of the test, and that in certain settings we have more power than sample splitting.

Advisor: Robert Tibshirani

Key words: selective inference, biostatistics, high dimensional statistics, lasso, sample splitting, pre-validation

 

Susie Ementon 

Student Services Manager 

Department of Statistics

Sequoia Hall, Room 222

Stanford University

650.723.1796 

sus...@stanford.edu

 

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