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Matt Haberland

Researcher at University of California, Los Angeles

Publications -  29
Citations -  20071

Matt Haberland is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Computer science & Python (programming language). The author has an hindex of 12, co-authored 23 publications receiving 9964 citations. Previous affiliations of Matt Haberland include Massachusetts Institute of Technology & California Polytechnic State University.

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Journal ArticleDOI

SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

TL;DR: SciPy as discussed by the authors is an open source scientific computing library for the Python programming language, which includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics.
Journal ArticleDOI

SciPy 1.0: fundamental algorithms for scientific computing in Python.

TL;DR: SciPy as discussed by the authors is an open-source scientific computing library for the Python programming language, which has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year.
Proceedings ArticleDOI

Tails in biomimetic design: Analysis, simulation, and experiment

TL;DR: This study demonstrates that a tail will help the MIT Cheetah achieve its goal of 30mph locomotion by 2014 and proves that for a given power and weight, tails can provide greater average torque than reaction wheels for the short times of interest in high speed running.
Proceedings ArticleDOI

The optimal swing-leg retraction rate for running

TL;DR: The results of this study show that swing-leg retraction can indeed improve the performance of running robots in all of the suggested areas, but the results also show that, for moderate and high running speeds, the optimal retraction rate for maximal disturbance rejection and stability is different from the ideal rate for minimal impact losses, peak forces, and foot slipping.