Robin Dunn

Robin Dunn

Principal Statistical Consultant

Novartis Pharmaceuticals

Biography

I am a Principal Statistical Consultant at Novartis Pharmaceuticals. I work in the Advanced Exploratory Analytics (AEA) group of the Advanced Methodology & Data Science (AMDS) team.

My research focuses on developing methods with finite sample validity in flexible settings and applying modern statistical methods to biomedical problems.

I completed my PhD in Statistics at Carnegie Mellon University in July 2021. I was fortunate to work with Professors Larry Wasserman (co-advisor), Aaditya Ramdas (co-advisor), and Sivaraman Balakrishnan on universal likelihood ratio testing. The universal LRT provides tests with valid type I error control in any setting where we can write a likelihood ratio (or upper bound the null maximum likelihood). My thesis is available here.

Education

  • PhD in Statistics, 2021

    Carnegie Mellon University

  • MS in Statistics, 2017

    Carnegie Mellon University

  • BA in Mathematics, 2016

    Kenyon College

Publications

Universal Inference Meets Random Projections: A Scalable Test for Log-concavity

Shape-constrained density estimation poses a middle ground between fully nonparametric and fully parametric density estimation. Log-concavity is a common choice of shape constraint. Using universal inference, we develop the first test for log-concavity that is provably valid. Validity holds in finite samples.

Gaussian Universal Likelihood Ratio Testing

We present the first in-depth exploration of the size, power, and relationships between several universal LRT variants. We illustrate the benefits of the universal LRT in a test of a non-convex doughnut-shaped null hypothesis.

Distribution-Free Prediction Sets with Random Effects

We extend conformal prediction methods to a random effects setting. Conformal prediction constructs valid prediction sets in finite samples, even if the predictive model is incorrect.

Risk Scoring for Time to End-stage Knee Osteoarthritis: Data from the Osteoarthritis Initiative

We construct the first risk score for end-stage knee OA, using an end-stage definition that depends on symptomatic and radiographic criteria. We note particular implications for clinical trial patient selection.

A Flexible Pipeline for Prediction of Tropical Cyclone Paths

We construct prediction bands for tropical cyclone paths. We also develop a publicly available computational pipeline for this task.

Teaching

Instructor

Summer 2019: 36-315: Statistical Graphics and Visualization. Syllabus.

Developed course materials, presented lectures, led labs, and held office hours.
Topics: choosing and interpreting graphics, mastering ggplot, interactive graphics with Shiny.

Advising / Mentoring

Summer 2018: Summer Undergraduate Research Experience graduate advisor.

Mentored three undergraduate students to identify research directions on data science for justice. Provided guidance on R tools (ggplot, data.table, shapefiles) and statistical models (generalized linear models, random forests, spatiotemporal ETAS models). Students presented their final work at a departmental seminar and at the poster session of the American Statistical Association’s 2018 StatFest.

Teaching Assistant

Carnegie Mellon University

  • Fall 2019: 36-462/662: Data Mining.
  • Spring 2019: 36-402/608: Advanced Methods for Data Analysis.
  • Fall 2018: 36-315: Statistical Graphics and Visualization.

Kenyon College

  • Spring 2014 – Spring 2016 (4 semesters): STAT 206: Data Analysis.
  • Fall 2014: ECON 101: Principles of Microeconomics.
  • Fall 2013: ECON 102: Principles of Macroeconomics.

Honors and Awards

  • 2019 PhD Teaching Assistant of the Year, Carnegie Mellon University Department of Statistics & Data Science.
  • 2016 Kenyon College valedictorian. Highest Honors on Mathematics Honors curriculum. Distinction on Senior Exercise.
  • 2016 Gertrude M. Cox Scholarship, ASA Committee on Women in Statistics and Caucus for Women in Statistics.
  • 2016 Reginald B. Allen Prize, Kenyon College Department of Mathematics & Statistics academic honor.
  • 2016 NSF Graduate Research Fellowship, National Science Foundation.
  • 2015 Goldwater Scholar, Barry Goldwater Scholarship and Excellence in Education Foundation.
  • 2013 CAUSE Undergraduate Statistics Class Project Competition (third place), Consortium for the Advancement of Undergraduate Statistics Education.