cv
Table of contents
General Information
| Full Name | Lars van der Laan |
| Address | Seattle, Washington, USA |
| Phone | (925) 257-3339 |
| lvdlaan@uw.edu | |
| Homepage | https://larsvanderlaan.github.io |
Education
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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.
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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.
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2016 - 2019 Double BSc in Mathematics and Physics
University of Groningen, Netherlands - Coursework in theoretical and applied physics and mathematics.
- Graduated cum laude.
Experience
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2024 - 2025 Netflix PhD Research Fellowship
Netflix - Awarded to support advanced research in machine learning and causal inference.
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Summer 2024 Machine Learning Research Intern
Netflix - Developed debiased ML methods for estimating long-term causal effects in experiments using RL and MDPs.
- Implemented gradient-boosted fitted value iteration software for large-scale data.
- Built calibration methods for ML models for classification, regression, and policy learning.
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2023 - Current Visiting Student Researcher
University of California, Berkeley - Advisor: Ahmed Alaa, PhD.
- Focused on distribution-free methods for predictive inference using ML tools.
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2021 - Current Research Assistant
University of Washington, Statistics - Conducted research on causal inference and debiased machine learning.
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2021 - 2024 Research Assistant
Fred Hutchinson Research Center - Collaborated with Dr. Peter B. Gilbert on causal inference for COVID-19 vaccine trials.
Skills
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Programming Languages
- Batchscript, Python, R, SQL, Java, C++. Proficient in object-oriented and functional programming paradigms.
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Data Analysis
- Proficient in SQL, R, Python, data cleaning/manipulation, and high-performance computing in C++ with Rcpp.
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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
- Self-Calibrating Conformal Prediction - The Thirty-Eighth Annual Conference on Neural Information Processing Systems (Neurips), 2024
- Long-term causal inference from short-term experiments in semiparametric Markov decision processes - Conference on Digital Experimentation (CODE), 2024
- Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts - American Causal Inference Conference (ACIC), 2024
- 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
- Self-Consistent Conformal Prediction - International Seminar on Selective Inference, 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
- Causal Isotonic Calibration for Heterogeneous Treatment Effects - International Conference of Machine Learning (ICML), 2023