new paper

_data/cv.yml

@@ -42,20 +42,20 @@
       institution: "Netflix"
       year: "2024 - 2025"
       description:
-        - "Awarded to support advanced research in machine learning and causal inference."
+        - "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."
     - title: "Machine Learning Research Intern"
       institution: "Netflix"
       year: "Summer 2024"
       description:
-        - "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."
+        - "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."
     - title: "Visiting Student Researcher"
-      institution: "University of California, Berkeley"
+      institution: "University of California, Berkeley, Computational Precision Health"
       year: "2023 - Current"
       description:
         - "Advisor: Ahmed Alaa, PhD."
-        - "Focused on distribution-free methods for predictive inference using ML tools."
+        - "Research on distribution-free machine learning methods for model calibration, uncertainty quantification, predictive inference, and conformal prediction."
     - title: "Research Assistant"
       institution: "University of Washington, Statistics"
       year: "2021 - Current"

_pages/about.md

@@ -22,11 +22,11 @@ social: true  # includes social icons at the bottom of the page
 
 ## About me
 
-I am a fourth-year Ph.D. student in Statistics at the University of Washington, specializing in causal inference and debiased machine learning. I am advised by [Marco Carone](http://faculty.washington.edu/mcarone/about.html), PhD and [Alex Luedtke](http://www.alexluedtke.com), PhD. I also collaborate with [Ahmed Alaa](https://vcresearch.berkeley.edu/faculty/ahmed-alaa) on model calibration and conformal prediction. My research is supported by a Netflix Graduate Research Fellowship, through which I work with Nathan Kallus and Aurelien Bibaut.
+I am a fourth-year Ph.D. student in Statistics at the University of Washington, specializing in causal inference and debiased machine learning. I am advised by [Marco Carone](http://faculty.washington.edu/mcarone/about.html), PhD and [Alex Luedtke](http://www.alexluedtke.com), PhD. I also collaborate with [Ahmed Alaa](https://vcresearch.berkeley.edu/faculty/ahmed-alaa) at UC Berkeley Computational Precision Health on model calibration and conformal prediction. My research is currently supported by a Netflix Graduate Research Fellowship, through which I work with [Nathan Kallus](https://nathankallus.com/) and [Aurelien Bibaut](https://scholar.google.com/citations?user=N_8WC5oAAAAJ&hl=en).
 For more detail on my background, please check out my [CV](https://larsvanderlaan.github.io/cv/).
 
 
-My research interests encompass a wide range of areas, including semiparametric statistics, statistical learning and calibration theory, debiased machine learning, and causal inference. I am enthusiastic about applying these methodologies to various domains, such as survival and longitudinal data analysis, inference on heterogeneous treatment effects, and personalized decision-making.
+My research interests encompass a wide range of areas, including semiparametric statistics, statistical learning and calibration, reinforcement learning, debiased machine learning, and causal inference. I am enthusiastic about applying these methodologies to various domains, such as survival and longitudinal data analysis, inference on heterogeneous treatment effects, and personalized decision-making.
 
 For the latest updates on my research, you can follow me on Twitter at [@Larsvanderlaan3](https://twitter.com/LarsvanderLaan3) and connect with me on [LinkedIn](https://www.linkedin.com/in/lars-van-der-laan-32367615b/). Additionally, you can access all my research publications on my [Google Scholar](https://scholar.google.com/citations?user=0bwP0i4AAAAJ&hl=en) profile. To explore a curated selection of my works, please visit the [publications](https://larsvanderlaan.github.io/publications/) tab.