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Table of contents

General Information

Full Name Lars van der Laan
Address Seattle, Washington, USA
Phone (925) 257-3339
Email lvdlaan@uw.edu
Homepage https://larsvanderlaan.github.io

Education

  • 2021 - Present
    PhD in Statistics
    University of Washington, Seattle
    • Advised by Marco Carone, PhD and Alex Luedtke, PhD.
    • Research on semiparametric statistics, causal inference, and debiased/targeted machine learning.
    • Collaboration with Fred Hutchinson Research Center on COVID-19 vaccine trials.
  • 2019 - 2020
    MA in Statistics
    University of California, Berkeley
    • Coursework in theoretical and applied statistics, programming, and data analysis.
    • Completed an industry-oriented capstone project.
  • 2016 - 2019
    Double BSc in Mathematics and Physics
    University of Groningen, Netherlands
    • Coursework in theoretical and applied physics and mathematics.
    • Graduated cum laude.

Experience

  • 2024 - 2025
    Netflix PhD Research Fellowship
    Netflix
    • Awarded to support advanced research in machine learning and causal inference. Current projects include double reinforcement learning and policy evaluation, long-term causal inference from short-term experiments, and the automation of debiased machine learning techniques.
  • Summer 2024
    Machine Learning Research Intern
    Netflix
    • Developed debiased ML methods for estimating long-term causal effects in short-term experiments using double reinforcement learning in time-invariant Markov decision processes.
    • Implemented gradient-boosted fitted value- and Q-iteration software for large-scale data using lightgbm.
    • Built calibration methods for ML models for classification, regression, and policy learning.
  • 2023 - Current
    Visiting Student Researcher
    University of California, Berkeley, Computational Precision Health
    • Advisor: Ahmed Alaa, PhD.
    • Research on distribution-free machine learning methods for model calibration, uncertainty quantification, predictive inference, and conformal prediction.
  • 2021 - Current
    Research Assistant
    University of Washington, Statistics
    • Conducted research on causal inference and debiased machine learning.
  • 2022 – 2023
    Teaching Assistant – PhD Theoretical Statistics Sequence
    University of Washington, Department of Statistics
    • Served as TA for STAT 581, 582, and 583: PhD-level sequence in statistical theory.
    • Led problem sessions, office hours, wrote exams, and provided feedback on theoretical coursework.
  • 2021 - 2024
    Research Assistant
    Fred Hutchinson Research Center
    • Collaborated with Dr. Peter B. Gilbert on causal inference for COVID-19 vaccine trials.

Awards

  • Netflix Graduate Research Fellowship – awarded to support advanced research in machine learning and causal inference. Mentors: Aurelien Bibaut and Nathan Kallus.
  • Best Paper Award, Conference on Causal Learning and Reasoning (CLeaR), 2025 – for 'Stabilized Inverse Probability Weighting via Isotonic Calibration,' Lausanne, Switzerland.

Skills

  • Programming Languages
    • Batchscript, Python, R, SQL, Java, C++. Proficient in object-oriented and functional programming paradigms.
  • Data Analysis
    • Proficient in SQL, R, Python, data cleaning/manipulation, and high-performance computing in C++ with Rcpp.
  • Ensemble Learning & Causal Inference
    • Experienced with causal ML ecosystems like tlverse and pyWhy.

Journals Reviewed/Refereed For

  • Journal of the Royal Statistical Society (Series B)
  • Journal of the American Statistical Association
  • Biometrika
  • Electronic Journal of Statistics
  • Journal of Machine Learning Research
  • Journal of Causal Inference

Invited Talks – Oral Presentations

  • Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference – American Causal Inference Conference (ACIC), Detroit, Michigan, 2025
  • Stabilized Inverse Probability Weighting via Isotonic Calibration – Best Paper Award, Conference on Causal Learning and Reasoning (CLeaR), Lausanne, Switzerland, 2025
  • Long-term causal inference from short-term experiments in semiparametric Markov decision processes – Conference on Digital Experimentation (CODE), 2024
  • Super-Efficient Estimation of Average Treatment Effect based on Randomized Controlled Trial Augmented with External Controls or Observational Study – Interactive Causal Learning, 2024
  • Adaptive debiased machine learning using data-driven model selection techniques – CMStatistics, 2023
  • Adaptive debiased machine learning using data-driven model selection techniques – Forum on the Integration of Observational and Randomized Data (FIORD), Center for Targeted Machine Learning and Causal Inference (CTML), UC Berkeley, 2023
  • Nonparametric inference on the causal effect of a stochastic threshold-based intervention – Western North American Region of the International Biometric Society, 2023

Invited Talks – Poster Presentations

  • Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction – International Conference on Machine Learning (ICML), 2025
  • Doubly robust inference via calibration – American Causal Inference Conference (ACIC), Detroit, Michigan, 2025
  • Self-Calibrating Conformal Prediction – The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024
  • Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts – American Causal Inference Conference (ACIC), 2024
  • Causal Isotonic Calibration for Heterogeneous Treatment Effects – International Conference on Machine Learning (ICML), 2023

Invited Talks – Seminar Presentations

  • Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts – Missing Data & Causal Inference Seminar Series, Julie Josse Group, University of Montpellier, 2025
  • Self-Consistent Conformal Prediction – International Seminar on Selective Inference, 2024
  • Adaptive debiased machine learning using data-driven model selection techniques – Causal Inference Working Group, Stanford University (invited by Stefan Wager), 2023
  • Causal Isotonic Calibration for Heterogeneous Treatment Effects – Center for Causal Inference Seminar Series, University of Pennsylvania, 2023

Invited Talks – Workshops

  • Highly Adaptive Lasso and Adaptive TMLE in Causal Inference – American Causal Inference Conference (ACIC), 2024
  • Recent advances in highly adaptive lasso (HAL) and its crucial role in causal inference – International Symposium on Biopharmaceutical Statistics (ISBS), 2024