cv

Table of contents

General Information

Full Name Lars van der Laan
Date of Birth July 28, 1998
Languages English, Dutch, Spanish

Education

  • 2021 - Current
    PhD in Statistics
    University of Washington, Seattle
    • Advised by Marco Carone, PhD and Alex Luedtke, PhD.
    • Conducting research in semi/nonparametric statistics and debiased machine learning with a focus on applications to causal inference. Additionally, researching supervised statistical learning and calibration methods for heterogeneous treatment effects and investigating causal inference techniques after data-driven model selection.
    • Actively collaborating with researchers at the Fred Hutchinson Research Center on projects related to causal inference and debiased machine learning with a focus on randomized and observational COVID-19 vaccine trials.
  • 2023 - Current
    Visting Student Researcher, Computational Precision Health
    University of Berkeley, California
    • Advised by Ahmed Alaa, PhD.
    • Developing distribution-free methods for predictive inference using machine learning tools.
    • Research Focuses include conformal prediction/inference, distribution-free prediction intervals with conditional validity, and Venn-Abers and isotonic calibration of point-predictions.
  • 2019 - 2020
    MA in Statistics
    University of California, Berkeley
    • Completed coursework on theoretical and applied statistics at the PhD level.
    • Successfully completed coursework on object-oriented programming and software development in Python and Java.
    • Successfully completed coursework on data analysis and statistical computing in Python using NumPy, Pandas, and Dask.
    • Completed an industry application-oriented capstone project.
  • 2016 - 2019
    Double BSc in Mathematics and Physics
    University of Groningen, Netherlands
    • Graduated cum laude.

Experience

  • 2020 - 2021
    Research Assistant and Statistical Consultant
    School of Public Health, UC Berkeley
    • Led several statistical analyses as part of an environmental epigenetics research group under the guidance of Professor Andres Cardenas.
    • Collaborated on impactful research projects and made significant contributions to numerous published papers.
  • 2020
    Summer Internship (causal Inference and survival analysis)
    Genentech
    • Advised by Dr. Jonathan Levy.
    • Developed statistical analysis software in R for nonparametric causal inference in survival analysis using machine learning tools.
    • Extracted, cleaned, and analyzed observational cancer survival data from the Flatiron database using SQL, R, and Python.
    • Leveraged debiased machine learning and causal inference techniques to drive data-driven decision-making.
  • 2020 - 2022
    Intern and Research Assistant (causal inference for covid-19 vaccines)
    Fred Hutchinson Research Center
    • Collaborated with Professor Peter B. Gilbert on research projects related to causal inference and debiased/targeted machine learning with applications to the US-sponsored COVID-19 vaccine trials.
    • Developed code pipelines for a unified statistical analysis of several US Government-sponsored COVID-19 vaccine trials, including those from Moderna and Johnson & Johnson.
    • Co-authored several theoretical and applied publications appearing in journals including Biometrics, Science, and Nature.

Skills

  • Programming Languages
    • Proficient in Batchscript, Python, R, SQL, Java, and C++ for software development, data manipulation, and data analysis.
    • Proficient with object-oriented and functional programming paradigms and committed to adhering to quality coding practices.
    • Proficient in parallel computing techniques and cluster management, enabling efficient and scalable data processing.
    • Experienced in developing statistical software and data tools to support research and analytics.
  • Data Analysis and Statistical Computing
    • Proficient in data analysis and statistical computing in SQL, R, and Python.
    • Experienced in parallel computing in R and Python using Future and Dask.
    • Skilled in data cleaning and manipulation in SQL, R (using dplyr and data.table), and Python (using NumPy and Pandas).
    • Experienced in high-performance computing in C++ with integration into R through Rcpp.
  • Software Ecosystems for Ensemble Learning and Causal Inference
    Proficient in the causal machine learning ecosystems for R and Python (tlverse and pyWhy), including
    • Ensemble Superlearning with [sl3](http://tlverse.org/sl3/)
    • Dependent Task Parallelization with [delayed](https://tlverse.org/delayed/)
    • Highly Adaptive Lasso Spline Estimation with [hal9001](https://tlverse.org/hal9001/)
    • Generalized Targeted/Debiased Machine Learning with [tmle3](https://tlverse.org/tmle3/)
    • Causal Machine Learning with [EconML](https://github.com/py-why/EconML)
    • Causal Inference with [doWhy](https://github.com/py-why/doWhy).
  • Communication
    • Demonstrated ability in effectively conveying complex findings to diverse audiences through excellent written and verbal communication skills.
    • Capable of presenting technical information in a clear and understandable manner.