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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

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 Little-Hopfield networks. 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. 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 | arxiv.


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

Chris Hillar
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