Research

Currently, I work on problems in neural network theory, causal inference, and empirical Bayes.

Research

Regression Adjustments for Experimental Designs in Two-Sided Marketplaces.
T. Sudijono, L. Lei, L. Masoero, S. Vijaykumar, G. Imbens, J. McQueen.
Work in progress (2025+).

Compound Selection Decisions: An Almost SURE Approach.
Jiafeng Chen*, Lihua Lei*, Timothy Sudijono*, Liyang Sun*, Tian Xie*.
To be Submitted (2025).
[ ArXiv | Software ]

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

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*.
Accepted, Annals of Statistics (2025).
[ ArXiv ]

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

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

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 ]