Lars van der Laan

Curriculum Vitae

General Information

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

Education

PhD in Statistics

  • Institution: University of Washington, Seattle
  • Year: 2021 - Current
  • Advisors: Marco Carone, PhD; Alex Luedtke, PhD
  • Research Focus: Semi/nonparametric statistics, debiased machine learning, shape-constrained inference, statistical learning and calibration theory for heterogeneous treatment effects.
  • Collaborations: Actively collaborating with researchers at the Fred Hutchinson Research Center on projects related to causal inference and debiased machine learning.

MA in Statistics

  • Institution: University of California, Berkeley
  • Year: 2019 - 2020
  • Coursework: Theoretical and applied statistics, object-oriented programming and software development in Python and Java, data analysis, and statistical computing in Python.
  • Capstone: Industry application-oriented capstone project.

Double BSc in Mathematics and Physics

  • Institution: University of Groningen, Netherlands
  • Year: 2016 - 2019
  • Honors: Graduated cum laude

Professional Experience

Research Assistant and Statistical Consultant

  • Institution: School of Public Health, UC Berkeley
  • Year: 2020 - 2021
  • Responsibilities: Led several statistical analyses in environmental epigenetics research, collaborated on impactful projects and contributed to published papers.

Summer Internship in Causal Inference and Survival Analysis

  • Institution: Genentech
  • Year: 2020
  • Advisor: Dr. Jonathan Levy
  • Project: Developed statistical software in R for causal inference in survival analysis using machine learning tools.

Intern and Research Assistant in Causal Inference for COVID-19 Vaccines

  • Institution: Fred Hutchinson Research Center
  • Year: 2020 - 2022
  • Responsibilities: Collaborated on research projects related to causal inference in COVID-19 vaccine trials, developed code pipelines, and co-authored publications in Biometrics, Science, and Nature.

Journals Reviewed/Refereed For

  • Electronic Journal of Statistics (EJS)
  • Journal of Machine Learning Research (JMLR)
  • Journal of Causal Inference (JCI)

Invited Talks

  1. “Nonparametric inference on the causal effect of a stochastic threshold-based intervention”
    • Event: Invited speaker for session on surrogate outcomes
    • Institution: Western North American Region of The International Biometric Society
    • Date: 2023
  2. “Causal Isotonic Calibration for Heterogeneous Treatment Effects”
    • Event: Center for Causal Inference Seminar Series
    • Institution: University of Pennsylvania, Berkeley
    • Date: 2023
  3. “Causal Isotonic Calibration for Heterogeneous Treatment Effects”
    • Event: Conference poster session
    • Conference: International Conference of Machine Learning (ICML)
    • Date: 2023

Skills

Programming Languages

  • Proficient in Batchscript, Python, R, SQL, Java, and C++
  • Object-oriented and functional programming paradigms
  • Parallel computing techniques and cluster management

Data Analysis and Statistical Computing

  • Data analysis and statistical computing in SQL, R, and Python
  • Parallel computing in R and Python using Future and Dask
  • Data cleaning in SQL, R, and Python
  • High-performance computing in C++ with R integration

Software Ecosystems for Ensemble Learning and Causal Inference

  • Proficient in the causal machine learning ecosystems for R and Python (tlverse and pyWhy)
  • Ensemble Superlearning with sl3
  • Dependent Task Parallelization with delayed
  • Generalized Targeted/Debiased Machine Learning with tmle3
  • Causal Machine Learning with EconML
  • Causal Inference with doWhy

Communication Skills

  • Excellent written and verbal communication skills
  • Ability to present technical information clearly to diverse audiences

Contact Information

  • B313
  • Padelford Hall, Northeast Stevens Way
  • Seattle, WA 98103
  • 925-257-3339

Last Updated: September 7, 2023