Research

My research interests center around empirical Bayes, causal inference, and experimentation.

Research

2026

Regression Adjustments for Experimental Designs in Two-Sided Marketplaces.
T. Sudijono, L. Lei, L. Masoero, S. Vijaykumar, G. Imbens, J. McQueen.
To be Submitted (2026).
[ ArXiv | Software ]

2025

Compound Selection Decisions: An Almost SURE Approach.
Jiafeng Chen*, Lihua Lei*, Timothy Sudijono*, Liyang Sun*, Tian Xie*.
Submitted (2026).
[ ArXiv | Software ]

Non-Identifiability distinguishes Neural Networks among Parametric Models.
S. Chatterjee*, T. Sudijono*.
Submitted (2025).
[ ArXiv ]

2024

Optimizing the Returns to Experimentation Programs.
T. Sudijono, S. Ejdemyr, A. Lal, M. Tingley.
Submitted (2024). Extended Abstract at ACM EC 2025.
[ ArXiv | Software ]

Neural Networks Generalize on Low Complexity Data.
S. Chatterjee*, T. Sudijono*.
Annals of Statistics (2026).
[ Journal | ArXiv ]

Synthetic Control Inference via Refined Placebo Tests.
L. Lei.*, T. Sudijono*,
Submitted (2024).
[ ArXiv | Software ]

2023

Fluctuation Bounds in the Restricted Solid-on-Solid Model of Surface Growth.
T. Sudijono.
Random Structures & Algorithms (2025).
[ Journal | ArXiv ]

Undergraduate Works

A Topological Data Analytic Approach for Discovering Biophysical Signatures in Protein Dynamics.
W. S. Tang*, G. M. da Silva*, H. Kirveslahti, E. Skeens, B. Feng, T. Sudijono, K. Yang, S. Mukherjee, B. Rubinstein, L. Crawford.
PLOS Computational Biology (2022).
[ Journal | BioArXiv ]

A statistical pipeline for identifying physical features that differentiate classes of 3D shapes.
B. Wang*, T. Sudijono*, H. Kirveslahti*, T. Gao, D. M. Boyer, S. Mukherjee, and L. Crawford.
Annals of Applied Statistics (2021).
[ Journal | BioArXiv | Software ]

* denotes equal contribution or alphabetical order.

Theses

Stationarity and Ergodicity of Local Dynamics of Interacting Markov Chains on Large Sparse Graphs.
T. Sudijono, (Advised by K. Ramanan, A. Ganguly).
Sc. B. Thesis - May 2019.
[ Repository ]