Selective Randomization Inference for Adaptive Experiments
Bernoulli-IMS 11th World Congress in Probability and Statistics - Bochum, Germany
About Me
I am a postdoctoral researcher at École polytechnique fédérale de Lausanne at the Chair of Biostatistics held by Mats Stensrud.
I am broadly interested in developing statistical methods to advance data analysis under realistic assumptions, particularly in causal inference.
Before joining EPFL, I completed my PhD degree at the Statistical Laboratory of the University of Cambridge under the supervision of Qingyuan Zhao and obtained a Master in Mathematics at TU Munich advised by Mathias Drton.
Beyond this, I also undertook industry projects in the implementation of RNNs with Tensorflow, Bayesian optimal experimental design and detection of synthetically lethal gene pairs based on CRISPR knock-out experiments.
Interests
- Causality
- Sensitivity Analysis
- Randomization Inference
- Stochastic Optimization
- Selective Inference
- Applied Statistics
Education
PhD Mathematics of Information, 2024
University of Cambridge
M.Sc. Mathematics, 2020
Technical University of Munich
Exchange Research Student, 2020
Kyoto University
Visiting Student, 2018
University of Oxford
B.Sc. Mathematics, 2017
Ludwig Maximilian University, Munich
Publications and Pre-Prints
(2024).
Optimization-based Sensitivity Analysis for Unmeasured Confounding using Partial Correlations.
(2021).
Post-selection Inference with HSIC-Lasso.
ICML 2021.
(2021).
Confidence in Causal Discovery with Linear Causal Models.
UAI 2021.
(2020).
Model Uncertainty in Statistical Inference.
Master Thesis.
Featured Talks
Selective Randomization Inference for Adaptive Studies
American Causal Inference Conference - Seattle, WA, USA
Sensitivity Analysis with the R2-Calculus
European Causal Inference Meeting - Oslo, Norway
Sensitivity Analysis with the R2-Calculus
Online Causal Inference Seminar - online
Post-selection inference with HSIC-Lasso
International Conference on Machine Learning - online
Teaching at the University of Cambridge
- Teaching Assistant
- Causal Inference (Part III): 2021, 2022
- Supervisor
- Statistical Modelling (Part II): 2023
- Statistics (Part IB): 2023
- Mathematics of Machine Learning (Part II): 2022