Christopher Hillar


Address:
Redwood
Center for Theoretical Neuroscience
University of California, Berkeley
575A Evans Hall, MC 3198
Berkeley, CA 947203198
Office: 573 Evans Hall
Phone: (510) 6427252
FAX: (510) 6427206
Email: chillarberkeley.edu
Ph.D. U.C. Berkeley (Mathematics,
2001  2005)
B.S. Yale University (Mathematics,
Computer Science, 2000)
Currently: Assistant Project Scientist, Redwood Center (through the Helen Wills Neuroscience Institute) and UCSF (Tecott Lab). Partially supported by the Simons Foundation and an NSF grant.
NSF Joint Institutes
Postdoctoral Fellowship: I was awarded
this
at the Mathematical Sciences Research Institute
with the following proposal.
MSRI is awesome.
NSA Grant: I was awarded an NSA
Young Investigators Grant. My proposal can
be found here.
Careers in Math: If you've ever wondered what you could do with a math degree (undergrad or otherwise), I highly recommend you check out SIAM's careers in math brochure


Theoretical Neuroscience: I am a mathematician working at the Redwood Center for Theoretical Neuroscience
at the University of California, Berkeley.
Recent Research
New: (with Andre Wibisono) We have a manuscript up: Maximum entropy distributions on graphs. pdf  arxiv
New: (with Ngoc Tran and Kilian Koepsell) Robust exponential binary pattern storage in LittleHopfield networks. pdf  arxiv
New: With LekHeng Lim, we finished our paper about the computational intractibility of some
problems involving tensors. Most tensor problems
are NPHard, Journal of the ACM.  pdf  arxiv
With Shaowei Lin and Andre Wibisono, we finished a paper on inverses of symmetric diagonally dominant matrices, with applications to numerical linear algebra and random graph distributions. Inverses of symmetric, diagonally dominant positive matrices, submitted  pdf
(with Jascha SohlDickstein
and Kilian Koepsell) Efficient and optimal binary LittleHopfield associative memory storage using minimum probability flow, 2011, NIPS (DISCML), 2012. pdf  arxiv.
With Friedrich Sommer,
we are working on sparse coding combined with compressed
sensing as a principle of brain communication and compression. (NIPS
2010 accepted spotlight submission pdf)
Theory paper preprint: When can dictionary learning uniquely recover sparse data from subsamples? pdf  arxiv.


Research Interests: Structured polynomial
systems, computational algebraic geometry, combinatorics,
matrix analysis, applications of mathematics to
neuroscience 

Chris
Hillar 

