From Jonathan D. Lettvin
Jonathan D. Lettvin
Big Data Mathematics
I attack/solve "insoluble" Big Data problems and generate unexpected successes (see my resumé for examples). I have loved big data since before it was named. I use novel mathematical methods where databasing would obscure features. I think about it, visualize it, detect events in it, and discover monetizable value in it where others see only noise.
To achieve my results, I develop high-quality, high-performance code that is small, fast, correct, complete, and secure. My code is targeted for zero-bugs, unit-tests, edge-cases, 100% coverage, and orders of magnitude performance boosts. I comment and document heavily. I prefer to collaborate, but work very well autonomously.
I have experience with many languages, editors, platforms, etc... Learning new tools rarely improves my problem-solving abilities and usually takes time away from solving really interesting problems with the tools I already have. Learning Python was one of those rare exceptions because a strong scientific community has evolved excellent, optimized, and easily used libraries. In the spirit of avoiding premature optimizations, Python is my preferred prototyping language.
When performance issues arise, I switch to C++ and Intel assembly. I use Virtualbox to sandbox environments. My preferred development environment includes git, mediawiki, make, and gvim. I'm interested in gpu and map/reduce. I am experienced in architecting and implementing operating systems, editors, compilers, data ingesters, lattice math, and feature detection.
My clients/employers include Carbonite, Lotus, IBM, NASA, MIT, and many small high tech startups. I have patents, and publications, and have contributed to project success in many arenas. These include canonicalization, virus search, high speed lexing, discrete convolution/correlation, efficiency calculations, dimensional conversion, and automated generation of code and papers.
My personal goal is to answer Cajal's three questions about nervous systems (ISBN 0-19-507401-7 Histology of the Nervous System): "Practitioners will only be able to claim that a valid explanation of a histological observation has been provided if three questions can be answered satisfactorily: what is the functional role of the arrangement in the animal; what mechanisms underlie this function; and what sequence of chemical and mechanical events during evolution and development gave rise to these mechanisms." Santiago Ramón y Cajal
Principally, I work on the first two questions: I model observed groups of shaped neurons. I model observed signal propagation and expression. I replicate observed functional roles. As a personal Python OpenCV Big Data project I research and develop neuron-shaped mathematical transforms, as discrete 3D convolution/correlation kernels achieving far sub-pixel image feature detection, using methods learned during my MIT Physics training and early experience in a wet neuroscience lab. ignore old page
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The unknown only becomes known by one who makes mistakes. For example, all advances in science are achieved by violating inviolable rules, whether intentionally or not. It is a common theme to be found in the nobel lectures by the prize winners (http://www.nobelprize.org). As I like to say "You know the value of your mistake by the size of the army mounted against you".