[Theory] REMINDER: [Talks at TTIC] 1/31 TTIC Colloquium: Venkat Chandrasekaran, Caltech

[Brandie Jones]

*When:*        Wednesday, January 31st at *10am CT* **Please note the time
change**


*Where:       *Talk will be given *live, in-person* at

                       TTIC, 6045 S. Kenwood Avenue

                       5th Floor, Room 530


*Virtually:*  via Panopto (livestream
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=15b7f55a-3b5b-421d-88fd-b0f2013c8146>
)


*Who: *         Venkat Chandrasekaran, Caltech

*Title:*          On False Positive Error
*Abstract:  *Controlling the false positive error in model selection is a
prominent paradigm for gathering evidence in data-driven science.  In model
selection problems such as variable selection and graph estimation, models
are characterized by an underlying Boolean structure such as presence or
absence of a variable or an edge.  Therefore, false positive error or false
negative error can be conveniently specified as the number of
variables/edges that are incorrectly included or excluded in an estimated
model.  However, the increasing complexity of modern datasets has been
accompanied by the use of sophisticated modeling paradigms in which
defining false positive error is a significant challenge.  For example,
models specified by structures such as partitions (for clustering),
permutations (for ranking), directed acyclic graphs (for causal inference),
or subspaces (for principal components analysis) are not characterized by a
simple Boolean logical structure, which leads to difficulties with
formalizing and controlling false positive error.  We present a generic
approach to endow a collection of models with partial order structure,
which leads to systematic approaches for defining natural generalizations
of false positive error and methodology for controlling this error.  (Joint
work with Armeen Taeb, Peter Bühlmann, Parikshit Shah).

*Short Bio: *Venkat Chandrasekaran is a Professor at Caltech in Computing
and Mathematical Sciences and in Electrical Engineering. He received a
Ph.D. in Electrical Engineering and Computer Science from MIT (2011), and
he received undergraduate degrees in Mathematics and in Electrical and
Computer Engineering from Rice University (2005). He was awarded the Jin-Au
Kong Dissertation Prize for the best doctoral thesis in Electrical
Engineering at MIT (2012), the Young Researcher Prize in Continuous
Optimization (2013), the Sloan Research Fellowship in Mathematics (2016),
and the INFORMS Optimization Society Prize for Young Researchers (2016).
His research interests lie in mathematical optimization and its interface
with topics in the information sciences.

*Host: Nati Srebro <nati at ttic.edu>*

--
*Brandie Jones *
*Executive **Administrative Assistant*
Toyota Technological Institute
6045 S. Kenwood Avenue
Chicago, IL  60637
www.ttic.edu
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