James E. Johndrow

Assistant Professor of Statistics

Contact Information

Primary Email:
johndrow@wharton.upenn.edu

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

Research

James Johndrow, Aaron Smith, Natesh Pillai, David B. Dunson (2019), MCMC for Imbalanced Categorical Data, Journal of the American Statistical Association, (to appear).
James Johndrow, Kristian Lum, Daniel Manrique-Vallier (2019), Low-risk population size estimates in the presence of capture heterogeneity, Biometrika, 106 (1), pp. 197-210.
James Johndrow and Kristian Lum (2019), An algorithm for removing sensitive information: Application to race-independent recidivism prediction, Annals of Applied Statistics, 13 (1), pp. 189-220.
James Johndrow and Anirban Bhattacharya (2019), Optimal Gaussian Approximations to the Posterior for Log-Linear Models with Diaconis–Ylvisaker Priors, Bayesian Analysis, 13 (1), pp. 201-223.
James Johndrow, Anirban Bhattacharya, David B. Dunson (2019), Tensor decompositions and sparse log-linear models, The Annals of Statistics, 45 (1), pp. 1-38.
James Johndrow and Julia A. Palacios (2019), Exact limits of inference in coalescent models, Theoretical Population Biology, 125, pp. 75-93.
Emanuele Aliverti, Kristian Lum, James Johndrow, David B. Dunson (Working), Removing the influence of a group variable in high-dimensional predictive modelling.
James Johndrow and Aaron Smith (2018), Fast mixing of metropolis-hastings with unimodal targets, Electronic Communications in Probability, 23 (71), pp. 1-9.
James Johndrow, Paulo Orenstein, Anirban Bhattacharya (Working), Bayes Shrinkage at GWAS scale: Convergence and Approximation Theory of a Scalable MCMC Algorithm for the Horseshoe Prior.
Leo L. Duan, James Johndrow, David B. Dunson (2018), Scaling up Data Augmentation MCMC via Calibration, Journal of Machine Learning Research, 19, pp. 1-34.

Teaching

Current Courses

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.
STAT991303

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.
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.

Activity

Latest Research

James Johndrow, Aaron Smith, Natesh Pillai, David B. Dunson (2019), MCMC for Imbalanced Categorical Data, Journal of the American Statistical Association, (to appear).

All Research

James E. Johndrow

Contact Information

Overview

Education

Academic Positions Held

Research

Teaching

Current Courses

STAT991 - Sem In Adv Appl Of Stat

Past Courses

STAT405 - STAT COMPUTING WITH R

STAT705 - STAT COMPUTING WITH R

STAT991 - SEM IN ADV APPL OF STAT

Activity

Latest Research