[CSDM] Fwd: [Theory-Read] Fwd: [ORFE-Seminars] Princeton Optimization Seminar, Jelani Nelson, Today, Apr. 27, 4:30 PM, Sherrerd 101

[Avi Wigderson]

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Subject: 	[Theory-Read] Fwd: [ORFE-Seminars] Princeton Optimization 
Seminar, Jelani Nelson, Today, Apr. 27, 4:30 PM, Sherrerd 101
Date: 	Thu, 27 Apr 2017 13:21:03 +0000
From: 	Amir Ali Ahmadi <a_a_a at princeton.edu>
To: 	theory-read at lists.cs.princeton.edu 
<theory-read at lists.cs.princeton.edu>





Sent from my iPhone

Begin forwarded message:

> *From:* Carol Smith <carols at PRINCETON.EDU <mailto:carols at princeton.edu>>
> *Date:* April 27, 2017 at 8:51:51 AM EDT
> *To:* <ORFE-TALKS at Princeton.EDU <mailto:ORFE-TALKS at princeton.edu>>
> *Subject:* *[ORFE-Seminars] Princeton Optimization Seminar, Jelani 
> Nelson, Today, Apr. 27, 4:30 PM, Sherrerd 101*
> *Reply-To:* Carol Smith <carols at PRINCETON.EDU 
> <mailto:carols at princeton.edu>>
>
> *----- **_Princeton Optimization Seminar_**   -----*
>
> *DATE: *Today, April 27, 2017
>
> *TIME*: 4:30PM
>
> *LOCATION**: * Sherrerd Hall 101
>
> *SPEAKER:*Jelani Nelson, Harvard University
> *TITLE: *Optimality of the Johnson-Lindenstrauss Lemma
> *Abstract: *Dimensionality reduction in Euclidean space, as attainable 
> by the Johnson-Lindenstrauss lemma (also known as "random 
> projections"), has been a fundamental tool in algorithm design and 
> machine learning. The JL lemma states that any n points in Euclidean 
> space can be mapped to m-dimensional Euclidean space while preserving 
> all pairwise distances up to 1+epsilon, where m only needs to be on 
> the order of (log n) / epsilon^2, independent of the original 
> dimension. In this talk, I discuss our recent proof that the JL lemma 
> is optimal, in the sense that for any n there are point sets of size n 
> such that no embedding providing (1+epsilon)-distortion exists into a 
> dimension that is more than a constant factor better than what the JL 
> lemma guarantees. I will also discuss some subsequent work and future 
> directions. Joint work with Kasper Green Larsen (Aarhus University).
>
> *BIO:* Jelani Nelson is an Assistant Professor of Computer Science at 
> Harvard University. His main research interest is in algorithm design 
> and analysis, with recent focus on streaming algorithms, 
> dimensionality reduction, compressed sensing, and randomized linear 
> algebra algorithms. He completed his Ph.D. in computer science at MIT 
> in 2011, receiving the George M. Sprowls Award for best computer 
> science doctoral dissertations at MIT. He is the recipient of an NSF 
> CAREER Award, ONR Young Investigator Award, ONR Director of Research 
> Early Career Award, Alfred P. Sloan Research Fellowship, and 
> Presidential Early Career Award for Scientists and Engineers (PECASE).


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