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Raphael Arkady   Meyer

Rising PhD Student, Computer Science

    Research Interests

    Statistical Learning Theory

    Foundations of Data Science

    Probability Theory

    Numerical Linear Algebra

    Education

    NYU Tandon, Brooklyn NY

    Pursuing PhD, Computer Science

    Research Fellow, Starting Fall 2019

    Purdue University, West Lafayette, IN
    May2019

    Pursuing Bachelors, Computer Science Honors

    Senior Research Assistant, 3.72/4.00 GPA

    Concentrations in Foundations of CS, Computational Science, and Machine Intelligence

    Minors in Math and Electrical Engineering

    Work experience

    Research Publications

    Fall 2018

    Statistics & Kernel Sums, To Appear at ICML 2019

    Novel Combination of Optimization Theory and Statistical Learning Theory

    Justifies Common Assumptions made in Multiple Kernel Learning

    Work with Prof. Jean Honorio at Purdue University

    Meyer, Raphael Arkady, and Jean Honorio. "On the Statistical Efficiency of Optimal Kernel Sum Classifiers." arXiv preprint arXiv:1901.09087 (2019). Accepted to ICML 2019.

    Fall 2016

    Secure OR Evaluation, ISIT 2017 Publication

    Designing Tight Lower-bound Techniques for Secure Computation

    Multi-Party Computation, Randomized Protocols, Tight Lower Bounds

    Jhanji, Amisha, Hemanta K. Maji, and Raphael Arkady Meyer. "Characterizing optimal security and round-complexity for secure OR evaluation." Information Theory (ISIT), 2017 IEEE International Symposium on. IEEE, 2017.

    Teaching

    FALL 2018

    Teaching Assistant, CS 381: Algorithmic Analysis

    Internships

    Summer 2017

    V3 DCS Scheduler, Bloomberg L.P., Software Engineering Intern

    Recognized, Tested, and Proved Inefficiencies with Existing Distributed Scheduler

    Integrated New Service to Observe System Load and be able to Learn Smart Solutions

    Cleared Technical Debt by Resolving bugs, Collecting Metrics, Automating Workflows

    SUMMER 2016

    FINRA Trace API, Bloomberg L.P., Software Engineering Intern

    Integrated various Database, PubSub, and API platforms to provide a new format of data

    Iteratively designed to guarantee the API we produce matches Client Expectations

    Learned to code Effective, Maintainable, and Production-Worthy code

    Graduate Coursework

    Learning Theory

    Hands-on Learning Theory (CS 590 HLT, 1 of 2 students admitted)

    Computational Methods in Optimization (CS 520)

    Convex and Discrete Optimization (CS 690 SML)

    Statistical Machine Learning (CS 578)

    Applied Machine Learning

    Deep Learning & Symbolic Reasoning (CS 590 DLS)

    Deep Learning (CS 690 DL, 1 of 20 students admitted)

    Applied Regression Analysis (STAT 512)

    Statistical Methods (STAT 511)

    Data Mining (CS 573)

    Numerical Linear Algebra

    Random Algorithms for Numerical Linear Algebra (CS 590 RND)

    Linear Algebra Applications (MA 511)

    Numerical Linear Algebra (CS 515)

    Theory of CS

    Mathematical Tookit For CS (CS 590 MTK)

    Theory of Computation (CS 584)

    Algorithm Analysis (CS 580)

    Awards & Honors

    Student Travel Grant, International Conference on Machine Learning (ICML), (June 2019)

    Finalist, 2018 CRA Outstanding Undergraduate Researcher Awards, CRA (2018)

    Student Travel Grant, IEEE International Symposium on Information Theory (July 2017)

    External Conference Reviewer, IEEE International Symposium on Information Theory (July 2017)

    Outstanding Sophomore of the Year, Purdue Computer Science (2016-17)

    Silver Medal, Giant Slalom, Ecole de Ski Français, Meribelle, France (2016)

    Qualcomm Rookie Team of the Year and Top Ten Hacks, Boilermake Hackathon (2015)

    Certificate of Cuisine, Cordon Bleu, School of Gourmet Cuisine, Paris, France (2015)

    Bronze Medal, Duke of Edinburgh Program (2012)