K. Jarrod Millman

Numerical Methods & Scientific Computing · 2007–2020

A researcher in the Science corpus: 8 papers; trusts 6 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Numerical Methods & Scientific Computing — 90 citations into it
• Artificial Intelligence — 78 citations into it; #11,838 of 35,345 by trust here, #2,350 by citations
• ML Systems & Hardware — 44 citations into it
• Computer Networks and Communications — 22 citations into it; #4,772 of 9,886 by trust here, #4,757 by citations
• Information Systems and Management — 21 citations into it; #1,612 of 3,842 by trust here, #57 by citations
• Computational Theory and Mathematics — 12 citations into it
• Education — 6 citations into it
• Experimental and Cognitive Psychology — 5 citations into it

Whom K. Jarrod Millman trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Brian E. Granger
2. Fernando Pérez
3. Richard E. Mayer
4. Matthew Brett
5. John D. Hunter
6. Friedrich Leisch
7. Kenneth E. Iverson
8. Andrew C. Worthington
9. Pearu Peterson
10. Daniel Lemire

Who trusts K. Jarrod Millman overall

1. Phillip Weinberg
2. Matthew Brett
3. José Miguel Soares
4. Michael Hanke
5. Krzysztof J. Gorgolewski
6. Fernando Pérez
7. Ross Barnowski
8. Marin Bukov
9. Yaroslav O. Halchenko
10. Jean-Baptiste Poline

Papers from K. Jarrod Millman's perspective

Ordered by how strongly the researchers K. Jarrod Millman trusts endorse each paper. Same corpus, a different order for every researcher.

1. Software Carpentry: Getting Scientists to Write Better Code by Making Them More Productive
2. How do scientists develop and use scientific software?
3. Julia: A Fresh Approach to Numerical Computing
4. Model-driven Level 3 BLAS Performance Optimization on Loongson 3A Processor
5. IPython: A System for Interactive Scientific Computing
6. Analysis of Functional Magnetic Resonance Imaging in Python
7. SymPy: Symbolic computing in Python
8. Gentleman R: R: A language for data analysis and graphics
9. Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. (American Psychologist)
10. Best Practices for Scientific Computing

Their papers

• Array programming with NumPy (2020) — 21,287 citations
• SciPy 1.0: fundamental algorithms for scientific computing in Python (2019) — 33,467 citations
• Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python (2020) — 1,246 citations
• SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python (2019) — 409 citations
• Developing Open-Source Scientific Practice * (2018) — 40 citations
• Teaching Computational Reproducibility for Neuroimaging (2018) — 19 citations
• Python for Scientists and Engineers (2011) — 855 citations
• Analysis of Functional Magnetic Resonance Imaging in Python (2007) — 79 citations