James E. Johndrow

James E. Johndrow
  • Assistant Professor of Statistics

Contact Information

  • office Address:

    455 Jon M. Huntsman Hall
    3730 Walnut Street
    Philadelphia, PA 19104

Research Interests: Markov chain Monte Carolo, scalable Bayesian computation, high dimensional statistics, algorithmic fairness

Overview

Education

Ph.D. in Statistical Science, Duke University, 2015
M.S. in Statistical Science, Duke University, 2012
B.A. in Chemistry, Amherst College, 2003

Academic Positions Held

Stein Fellow/Lecturer, Department of Statistics, Stanford University, 2016-2019

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Research

Teaching

Past Courses

  • STAT405 - STAT COMPUTING WITH R

    The goal of this course is to introduce students to the R programming language and related eco-system. This course will provide a skill-set that is in demand in both the research and business environments. In addition, R is a platform that is used and required in other advanced classes taught at Wharton, so that this class will prepare students for these higher level classes and electives.

  • STAT470 - DATA ANALY & STAT COMP

    This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Using R, we will study and practice the following methodologies: data cleaning, feature extraction; web scrubbing, text analysis; data visualization; fitting statistical models; simulation of probability distributions and statistical models; statistical inference methods that use simulations (bootstrap, permutation tests). Prerequisite: Waiving the Statistics Core completely if prerequisites are not met.

  • STAT705 - STAT COMPUTING WITH R

    The goal of this course is to introduce students to the R programming language and related eco-system. This course will provide a skill-set that is in demand in both the research and business environments. In addition, R is a platform that is used and required in other advanced classes taught at Wharton, so that this class will prepare students for these higher level classes and electives.

  • STAT991 - SEM IN ADV APPL OF STAT

    This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

  • STAT999 - INDEPENDENT STUDY

Activity

Latest Research

James Johndrow, Paulo Orenstein, Anirban Bhattacharya (2020), Bayes Shrinkage at GWAS scale: Convergence and Approximation Theory of a Scalable MCMC Algorithm for the Horseshoe Prior, Journal of Machine Learning Research, (to appear).
All Research