I am an Assistant Professor at Stanford University, jointly appointed between Statistics, Management Science & Engineering, and, by courtesy, Computer Science.

I work on foundational questions in machine learning, statistics, and data-driven decision-making. Example topics of interest include AI-assisted statistical inference and data collection, performative prediction, and studying selection bias.

Until recently I took an academic hiatus to work on AI evaluation as a Member of Technical Staff at LMArena. Before that, I was a Ram and Vijay Shriram Postdoctoral Fellow at Stanford University, affiliated with Stanford Data Science. I obtained my PhD in Electrical Engineering and Computer Sciences at UC Berkeley in 2023.

Contact

email: “firstname”.”lastname”@stanford.edu
office: E248 CoDa

Publications and preprints

(* denotes equal contribution, α-β denotes alphabetical ordering)

Probably Approximately Correct Labels
(α-β) E. J. Candès, A. Ilyas, T. Zrnic
Preprint arxiv code talk

Robust Sampling for Active Statistical Inference
P. Li, T. Zrnic, E. J. Candès
Conference on Neural Information Processing Systems (NeurIPS) 2025 arxiv

Look-Ahead Reasoning on Learning Platforms
H. Zhu, T. Zrnic, C. Mendler-Dünner
Conference on Neural Information Processing Systems (NeurIPS) 2025 arxiv

Can Unconfident LLM Annotations Be Used for Confident Conclusions?
K. Gligorić*, T. Zrnic*, C. Lee*, E. J. Candès, D. Jurafsky
Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL) 2025 NAACL arxiv tutorial

Prediction-Powered Inference with Imputed Covariates and Nonuniform Sampling
D. M. Kluger, K. Lu, T. Zrnic, S. Wang, S. Bates
Preprint arxiv

Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI
(α-β) W. Ji, L. Lei, T. Zrnic
Preprint arxiv

A Flexible Defense Against the Winner’s Curse
T. Zrnic, W. Fithian
Annals of Statistics (AoS) 2025 AoS arxiv code

A Note on the Prediction-Powered Bootstrap
T. Zrnic
Note 2024 arxiv package

Active Statistical Inference
T. Zrnic, E. J. Candès
International Conference on Machine Learning (ICML) 2024 (oral) ICML arxiv code

Plug-in Performative Optimization
L. Lin, T. Zrnic
International Conference on Machine Learning (ICML) 2024 ICML arxiv

Locally Simultaneous Inference
T. Zrnic, W. Fithian
Annals of Statistics (AoS) 2024 AoS arxiv code talk

Cross-Prediction-Powered Inference
T. Zrnic, E. J. Candès
Proceedings of the National Academy of Sciences (PNAS) 2024 PNAS arxiv code package

PPI++: Efficient Prediction-Powered Inference
(α-β) A. N. Angelopoulos, J. C. Duchi, T. Zrnic
Preprint arxiv code package

Prediction-Powered Inference
(α-β) A. N. Angelopoulos, S. Bates, C. Fannjiang, M. I. Jordan, T. Zrnic
Science 2023 Science arxiv package

Post-Selection Inference via Algorithmic Stability
T. Zrnic, M. I. Jordan
Annals of Statistics (AoS) 2023 AoS arxiv talk

Algorithmic Collective Action in Machine Learning
(α-β) M. Hardt, E. Mazumdar, C. Mendler-Dünner, T. Zrnic
International Conference on Machine Learning (ICML) 2023 ICML arxiv talk

Valid Inference After Causal Discovery
P. Gradu*, T. Zrnic*, Y. Wang, M. I. Jordan
Journal of the American Statistical Association (JASA) 2024 JASA arxiv

A Note on Zeroth-Order Optimization on the Simplex
T. Zrnic, E. Mazumdar
Note arxiv

Regret Minimization with Performative Feedback
M. Jagadeesan, T. Zrnic, C. Mendler-Dünner
International Conference on Machine Learning (ICML) 2022 ICML arxiv
Symposium on Foundations of Responsible Computing (FORC) 2022 (non-archival)

Private Prediction Sets
A. N. Angelopoulos*, S. Bates*, T. Zrnic*, M. I. Jordan
Harvard Data Science Review (HDSR) 2022 HDSR arxiv code

Who Leads and Who Follows in Strategic Classification?
T. Zrnic*, E. Mazumdar*, S. S. Sastry, M. I. Jordan
Conference on Neural Information Processing Systems (NeurIPS) 2021 NeurIPS arxiv

Individual Privacy Accounting via a Rényi Filter
(α-β) V. Feldman, T. Zrnic
Conference on Neural Information Processing Systems (NeurIPS) 2021 NeurIPS arxiv short talk long talk
Symposium on Foundations of Responsible Computing (FORC) 2021 (non-archival)

Outside the Echo Chamber: Optimizing the Performative Risk
J. Miller*, J. C. Perdomo*, T. Zrnic*
International Conference on Machine Learning (ICML) 2021 ICML arxiv blog post
Symposium on Foundations of Responsible Computing (FORC) 2021 (non-archival)

Asynchronous Online Testing of Multiple Hypotheses
T. Zrnic, A. Ramdas, M. I. Jordan
Journal of Machine Learning Research (JMLR) 2021 JMLR arxiv blog post code online FDR package

Stochastic Optimization for Performative Prediction
C. Mendler-Dünner*, J. C. Perdomo*, T. Zrnic*, M. Hardt
Conference on Neural Information Processing Systems (NeurIPS) 2020 NeurIPS arxiv blog post code

Performative Prediction
J. C. Perdomo*, T. Zrnic*, C. Mendler-Dünner, M. Hardt
International Conference on Machine Learning (ICML) 2020 ICML arxiv blog post talk code

The Power of Batching in Multiple Hypothesis Testing
T. Zrnic, D. L. Jiang, A. Ramdas, M. I. Jordan
International Conference on Artificial Intelligence and Statistics (AISTATS) 2020 AISTATS arxiv talk code

Natural Analysts in Adaptive Data Analysis
T. Zrnic, M. Hardt
International Conference on Machine Learning (ICML) 2019 ICML arxiv talk

SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate
A. Ramdas, T. Zrnic, M. J. Wainwright, M. I. Jordan
International Conference on Machine Learning (ICML) 2018 ICML arxiv code

Tensor-Based Crowdsourced Clustering via Triangle Queries
R. K. Vinayak, T. Zrnic, B. Hassibi
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2017 IEEE

Improving Location of Recording Classification Using Electric Network Frequency (ENF) Analysis
Z. Saric, A. Zunic, T. Zrnic, M. Knezevic, D. Despotovic, T. Delic
IEEE International Symposium on Intelligent Systems and Informatics (SISY) 2016 IEEE

PhD thesis

Prediction and Statistical Inference in Feedback Loops
T. Zrnic
UC Berkeley EECS