Kaitlyn Lee

Bio

Kaitlyn Lee is a fourth-year PhD student in Biostatistics at the UC Berkeley mentored by Professor Alejandro Schuler. Her research interests span causal inference, machine learning, and scalable statistical methods, with a focus on developing practical approaches to answer real-world questions in health, social policy, and clinical studies. Her current work includes designing efficient machine learning algorithms for causal effect estimation, bridging rigorous theory with applied data analysis.

Education

UC Berkeley | Berkeley, CA

PhD in Biostatistics | August 2022 - May 2027 (expected)

MA in Biostatistics | August 2022 - August 2024

Harvard College | Cambridge, MA

AB in Physics with Statistics Secondary | August 2016 - May 2020

Fellowships

NSF Graduate Research Fellowship | 2024 - 2027

Stern Health Fellow | 2022 - 2027

UC Berkeley Chancellor’s Fellowship | 2022 – 2024

Biostatistics DEIB Fellow | 2024 - 2025

Awards

Tom Ten Have Poster Award Honorable Mention, American Causal Inference Conference | 2025

Certificate of Distinction in Teaching, Harvard University | Fall 2020

Experience

Genentech | Product Data Science Intern | Summer 2025 - Present

Center for Targeted Machine Learning, UC Berkeley | Graduate Student Researcher | Fall 2023 - Present

Cornerstone Research | Analyst | January 2017 - June 2022

Huybers Lab, Harvard University | Research Assistant | May 2020 - December 2020

Publications

  • Lee, K. J., Hubbard, A., Schuler, A. (2025). Bridging Binarization: Causal Inference with Dichotomized Continuous Exposures. arXiv:2405.07109. Journal of Causal Inference (accepted, Oct 2025).
  • Lee, K. J., Schuler, A. (2025). RieszBoost: Gradient Boosting for Riesz Regression. arXiv:2501.04871.
  • Gordon, E. R., Trager, M. H., Kwinta, B. D., Stonesifer, C. J., Lee, K. J., … Geskin, L. J. (2024). Maintenance therapy for CTCL: importance for prevention of disease progression. Leukemia & Lymphoma, 1–8. https://doi.org/10.1080/10428194.2024.2376164.

Conference Presentations

  • Lee, K. J., Schuler, A. (2024, May). RieszBoost: Gradient Boosting for Riesz Regression. Poster presentation. American Causal Inference Conference, Detroit, MI. Tom Ten Poster Award Honorable Mention.
  • Lee, K. J., Hubbard, A., Schuler, A. (2024, April). Bridging Binarization: Causal Inference with Dichotomized Continuous Treatments. Oral presentation. European Causal Inference Meeting, Ghent, Belgium.
  • Lee, K. J., Schuler, A. (2024, February). RieszBoost: Gradient Boosting for Riesz Regression. Oral presentation. Center for the Application of Mathematics and Statistics to Economics and Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley Conference, Berkeley, CA.
  • Lee, K. J., Hubbard, A., Schuler, A. (2024, May). Bridging Binarization: Causal Inference with Dichotomized Continuous Treatments Poster presentation. American Causal Inference Conference, Seattle, WA.
  • Lee, K. J., Hubbard, A., Schuler, A. (2024, June). Bridging Binarization: Causal Inference with Dichotomized Continuous Treatments Poster presentation. Society of Epidemiological Research Conference, Austin, TX.

Kaitlyn Lee

Bio

Kaitlyn Lee is a fourth-year PhD student in Biostatistics at the UC Berkeley mentored by Professor Alejandro Schuler. Her research interests span causal inference, machine learning, and scalable statistical methods, with a focus on developing practical approaches to answer real-world questions in health, social policy, and clinical studies. Her current work includes designing efficient machine learning algorithms for causal effect estimation, bridging rigorous theory with applied data analysis.

Education

UC Berkeley | Berkeley, CA

PhD in Biostatistics | August 2022 - May 2027 (expected)

MA in Biostatistics | August 2022 - August 2024

Harvard College | Cambridge, MA

AB in Physics with Statistics Secondary | August 2016 - May 2020

Fellowships

NSF Graduate Research Fellowship | 2024 - 2027

Stern Health Fellow | 2022 - 2027

UC Berkeley Chancellor’s Fellowship | 2022 – 2024

Biostatistics DEIB Fellow | 2024 - 2025

Awards

Tom Ten Have Poster Award Honorable Mention, American Causal Inference Conference | 2025

Certificate of Distinction in Teaching, Harvard University | Fall 2020

Experience

Genentech | Product Data Science Intern | Summer 2025 - Present

Center for Targeted Machine Learning, UC Berkeley | Graduate Student Researcher | Fall 2023 - Present

Cornerstone Research | Analyst | January 2017 - June 2022

Huybers Lab, Harvard University | Research Assistant | May 2020 - December 2020

Publications

  • Lee, K. J., Hubbard, A., Schuler, A. (2025). Bridging Binarization: Causal Inference with Dichotomized Continuous Exposures. arXiv:2405.07109. Journal of Causal Inference (accepted, Oct 2025).
  • Lee, K. J., Schuler, A. (2025). RieszBoost: Gradient Boosting for Riesz Regression. arXiv:2501.04871.
  • Gordon, E. R., Trager, M. H., Kwinta, B. D., Stonesifer, C. J., Lee, K. J., … Geskin, L. J. (2024). Maintenance therapy for CTCL: importance for prevention of disease progression. Leukemia & Lymphoma, 1–8. https://doi.org/10.1080/10428194.2024.2376164.

Conference Presentations

  • Lee, K. J., Schuler, A. (2024, May). RieszBoost: Gradient Boosting for Riesz Regression. Poster presentation. American Causal Inference Conference, Detroit, MI. Tom Ten Poster Award Honorable Mention.
  • Lee, K. J., Hubbard, A., Schuler, A. (2024, April). Bridging Binarization: Causal Inference with Dichotomized Continuous Treatments. Oral presentation. European Causal Inference Meeting, Ghent, Belgium.
  • Lee, K. J., Schuler, A. (2024, February). RieszBoost: Gradient Boosting for Riesz Regression. Oral presentation. Center for the Application of Mathematics and Statistics to Economics and Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley Conference, Berkeley, CA.
  • Lee, K. J., Hubbard, A., Schuler, A. (2024, May). Bridging Binarization: Causal Inference with Dichotomized Continuous Treatments Poster presentation. American Causal Inference Conference, Seattle, WA.
  • Lee, K. J., Hubbard, A., Schuler, A. (2024, June). Bridging Binarization: Causal Inference with Dichotomized Continuous Treatments Poster presentation. Society of Epidemiological Research Conference, Austin, TX.