News & Events

Employment

 
 
 
 
 
June 2020 – August 2020
Remote

Machine Learn and Instrument Autonomy (MLIA) Summer Intern

Jet Propulsion Laboratory

Researched gradient-free optimizations and developed a codebase for a decision theoretic uncertainty quantification method.
 
 
 
 
 
May 2016 – June 2019
New York, NY

Senior Data Scientist

tellic

Worked with pharmaceutical text data to assist in standing up tellic’s NLP technology.

Selected Publications

(2023). Optimization-based frequentist confidence intervals for functionals in constrained inverse problems: Resolving the Burrus conjecture.

Preprint

(2023). Estimating Posterior Uncertainty via a Data Assimilation Specialized Monte Carlo Procedure.

Preprint

(2022). Uncertainty quantification for wide-bin unfolding: one-at-a-time strict bounds and prior-optimized confidence intervals. JINST.

Preprint Code DOI

(2022). Uncertainty quantification of the 4th kind; optimal posterior accuracy-uncertainty tradeoff with the minimum enclosing ball. JCP.

Preprint Code DOI

Skills

Statistics

Machine Learning

Inverse Modeling

Python

2.7 - 3.6

SQL

Teaching and
Mentoring

Jun 2022 – Jul 2022

Lead Instructor @ Data Science Summer Camp

Department of Statistics & Data Science, Carnegie Mellon University

Developed and led a week-long data science summer camp to expose local high school students to a career in data science, including lectures and interactive coding activities.
Aug 2019

Teaching Assistant

Department of Statistics & Data Science, Carnegie Mellon University

Leading recitations, grading homework, organizing TA schedules, hosting office hours, and developing custom software to streamline grading:

  • 36-218 Probability Theory for Computer Scientists (Head TA)
  • 36-219 Probability Theory and Random Processes (Head TA)
  • 36-226 Introduction to Statistical Inference (Head TA)
  • 36-401 Modern Regression
  • 36-402 Advanced Methods for Data Analysis
  • 46-924 Natural Language Processing (for the M.S. in Computational Finance program)
Aug 2019 – Dec 2019

Advisor for Data Science Initiative

Department of Statistics & Data Science, Carnegie Mellon University

Advised a group of four students for an undergraduate research project with Principal Financial Group to forecast fixed-income market conditions.

Grants

  • JPL Strategic University Research Partnership Awarding yearly funding to facilitate the development and implementation of decision theoretic and optimization-based UQ for JPL applications, including remote sensing, carbon flux inversion, and glacier modeling.

Recent Publications

We present an optimization-based framework to construct confidence intervals for functionals in constrained inverse problems, ensuring …
Preprint

Through the Bayesian lens of data assimilation, uncertainty on model parameters is traditionally quantified through the posterior …
Preprint

Unfolding is an ill-posed inverse problem in particle physics aiming to infer a true particle-level spectrum from smeared …
Preprint Code DOI

Uncertainty quantification (UQ) is, broadly, the task of determining appropriate uncertainties to model predictions. There are …
Preprint Code DOI

Contact Me

  • mcstanle@andrew.cmu.edu
  • Pittsburgh, PA