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Christopher Hillar





Address:
Redwood Center for Theoretical Neuroscience
University of California, Berkeley
575A Evans Hall, MC 3198
Berkeley, CA 94720-3198
Office: 573 Evans Hall
Phone
: (510) 642-7252
FAX: (510) 642-7206
Email: chillarberkeley.edu (Note: old email address not working lately, so please resend to this one anything recent).

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 Lek-Heng Lim, we finished our paper about the computational intractibility of some problems involving tensors. Most tensor problems are NP-Hard, 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. Tight bounds on the infinity norm of inverses of symmetric diagonally dominant positive matrices, submitted. pdf

(with Jascha Sohl-Dickstein and Kilian Koepsell) Efficient and optimal binary Little-Hopfield 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.


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

Chris Hillar
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