[Colloquium] REMINDER: Talks at TTIC: Madeleine Udell, Stanford

[Dawn Ellis]

When:     Tuesday, March 10, 2015 at 11am

Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526

Who:       Madeleine Udell, Stanford

Title:       Generalized Low Rank Models

Abstract:

Principal components analysis (PCA) is a well-known technique for
approximating
a data set represented by a matrix by a low rank matrix.
Here, we extend the idea of PCA to handle arbitrary data sets consisting
of numerical, Boolean, categorical, ordinal, and other data types.
This framework encompasses many well known techniques in data analysis,
such as nonnegative matrix factorization, matrix completion, sparse and
robust PCA, k-means, k-SVD, and maximum margin matrix factorization.
The method handles heterogeneous data sets,
and leads to coherent schemes for compressing, denoising, and imputing
missing entries across all data types simultaneously.
It also admits a number of interesting interpretations of the low rank
factors,
which allow clustering of examples or of features.
We propose several parallel algorithms
for fitting generalized low rank models,
and describe implementations and numerical results.

Bio:

Madeleine Udell is a PhD candidate at Stanford University in Computational
& Mathematical Engineering, working with Professor Stephen Boyd. Her
research focus is on modeling and solving large-scale optimization problems
and on finding and exploiting structure in high dimensional data, with
applications in marketing, demographic modeling, and medical informatics.
Her recent work on generalized low rank models (GLRMs) extends principal
components analysis (PCA) to embed tabular
data sets with heterogeneous (numerical, Boolean, categorical, and ordinal)
types into a low dimensional space, providing a coherent framework for
compressing, denoising, and imputing missing entries. She has developed of
a number of open source libraries for modeling and solving optimization
problems, including Convex.jl, one of the top ten tools in the new Julia
language for technical computing, and is a member of the JuliaOpt
organization, which curates high quality optimization software.

She received a B.S. degree in Mathematics and Physics, summa cum laude,
with honors in mathematics and in physics, from Yale University. At
Stanford, she was awarded a NSF Graduate Fellowship, a Gabilan Graduate
Fellowship, and a Gerald J. Lieberman Fellowship. She has taught courses in
Discrete Mathematics and Algorithms, and in Convex Optimization, and led a
team of TAs for an online course with over 10,000 students. Selected as the
doctoral student member of Stanford's School of Engineering Future
Committee, she is currently working to develop a road-map for the future of
engineering at Stanford over the next 10--20 years.

Host:  Nati Srebro, nati at ttic.edu



-- 
*Dawn Ellis*
Administrative Coordinator,
Bookkeeper
773-834-1757
dellis at ttic.edu

TTIC
6045 S. Kenwood Ave.
Chicago, IL. 60637
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