## Luis RademacherAssociate Professor of Mathematics Office: Mailing Address: Phone: 530-601-4444 x4007 E-mail: |

Theoretical computer science. Data science. Matrix computations. Machine learning. Convex geometry.

I received the NSF Early CAREER award in March 2014 for the proposal "CAREER: Transforming data analysis via new algorithms for feature extraction".

- NSF: "AF: Small: Geometry and High-dimensional Inference" (with co-PI Mikhail Belkin)
- NSF Early CAREER award: "CAREER: Transforming data analysis via new algorithms for feature extraction"

- The centroid body: algorithms and statistical estimation for
heavy-tailed distributions.

AMS Sectional Meeting, University of Georgia, March 2016. PDF

Oberwolfach, December 2015. - The more the merrier: the blessing of dimensionality for learning
large Gaussian mixtures

Banff International Research Station. Banff, Canada, 2014. PDF - Sections of convex bodies, statistical estimation and (in)stability.

American Institute of Mathematics, Palo Alto CA, 2013: PDF - Simplicial polytopes that maximize the isotropic constant are highly
symmetric.

AMS Sectional Meeting, Akron OH, 2012: PDF - Randomized algorithms for the approximation of matrices

FoCM 2011, Learning theory workshop: PDF

IMA, High dimensional phenomena: PDF

- Brett Leroux (PhD)
- Chang Shu (PhD)
- James Voss
(PhD, jointly advised with Mikhail Belkin, graduated, at Google)

- Joseph Anderson
(PhD, graduated, assistant professor at Salisbury University)

- Abhisek Kundu (MS, graduated)
- Jie Cui (MS, graduated)

- A simplicial polytope that maximizes the isotropic constant must be a
simplex.

Mathematika, 2015. Arxiv. - On the monotonicity of the expected volume of a random simplex.

Mathematika, 2012. Arxiv. Presentation at Oberwolfach: PPTX, PDF.

Computational experiments for the 3-dimensional case. - Expanders via random spanning trees.
(Alan Frieze, Navin Goyal, Santosh Vempala)

SIAM Journal on Computing, 2014.

SODA 2009. Video of workshop talk. - Heavy-tailed Independent
Component Analysis. (Joseph Anderson, Navin Goyal, Anupama Nandi)

FOCS 2015. - Matrix
Approximation
and Projective Clustering via Volume Sampling. (Amit Deshpande,
Santosh Vempala, Grant Wang)

Theory of Computing and SODA 2006. Short PDF presentation. - Efficient volume sampling
for row/column subset selection. (Amit Deshpande)

FOCS 2010. Video of talk. - The more, the merrier: the
blessing of dimensionality for learning large Gaussian mixtures.
(Joseph Anderson, Mikhail Belkin, Navin Goyal, James Voss)

COLT 2014. Video and slides of talk (by J. Anderson).