Nicholas Larus-Stone

Nicholas Larus-Stone

San Francisco Bay Area
3K followers 500+ connections

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About

Passionate about the intersection of computer science and biology.

Activity

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Experience

  • Sphinx Bio Graphic

    Sphinx Bio

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    San Francisco Bay Area

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    Cambridge, United Kingdom

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    Santa Monica, California

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    San Francisco Bay Area

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    Cambridge, Massachusetts

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    Greater Boston Area

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    Cambridge, Massachusetts

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    Greater Boston Area

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    Greater Seattle Area

Education

  • Harvard University Graphic

    Harvard University

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    Activities and Societies: Captain of Harvard Club Squash, Director of External Relations for Harvard Computer Society, Director of Web Development for Freshman Intramurals, Harvard University PRISE Fellow, Peer Advising Fellow, Teaching Fellow, Phillips Brooks House Association Volunteer

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    Activities and Societies: Queens' College University Challenge, Lacrosse

Licenses & Certifications

Volunteer Experience

  • Volunteer

    Harvard Youth Leadership Initiative

    - 1 year 10 months

Publications

  • Systems Optimizations for Learning Certifiably Optimal Rule Lists

    SysML

    Other authors
    • Elaine Angelino
    • Margo Seltzer
    • Daniel Alabi
    • Aditya Saligrama
    • Vassilios Kaxiras
    • Cynthia Rudin

  • Learning Certifiably Optimal Rule Lists

    KDD 2017

    We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm provides the optimal solution, with a certificate of optimality. By leveraging algorithmic bounds, ecient data structures, and computational reuse, we achieve several orders of magnitude speedup in time and a massive reduction of memory consumption. We demonstrate that our approach produces optimal rule lists on practical problems in…

    We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm provides the optimal solution, with a certificate of optimality. By leveraging algorithmic bounds, ecient data structures, and computational reuse, we achieve several orders of magnitude speedup in time and a massive reduction of memory consumption. We demonstrate that our approach produces optimal rule lists on practical problems in seconds. This framework is a novel alternative to CART and other decision tree methods.

    Other authors
    See publication

Courses

  • Artificial Intelligence

    CS 182

  • Computational Transcriptomics

    SCRB152

  • Computer Security

    R209

  • Computing Hardware

    CS 141

  • Data Structures and Algorithms

    CS 124

  • Discrete Mathematics for Computer Science

    CS 20

  • Introduction to Computer Science I

    CS 50

  • Introduction to Computer Science II

    CS 51

  • Introduction to Probability

    Stat 110

  • Machine Learning

    CS 181

  • Modern Compiler Design

    L25

  • Multicore Semantics and Programming

    R204

  • Operating Systems

    CS 161

  • Probabilistic Machine Learning

    LE49

  • Supervised Reading and Research

    CS91r

  • Systems Programming and Machine Organization

    CS 61

  • Theory of Computation

    CS 121

  • Topics in Cryptography and Privacy

    CS227r

Honors & Awards

  • Phi Beta Kappa

    Harvard University

  • KPCB Fellow

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Languages

  • Spanish

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