I’m a Ram and Vijay Shriram Data Science Postdoctoral Fellow at Stanford University, where I’m hosted by Emmanuel Candès in Statistics.

I work on developing theory and methods for reliable and responsible machine learning and data science. My work has had particular focus on modern contexts involving large-scale machine learning, societal feedback, and economic incentives.

I obtained my PhD in Electrical Engineering and Computer Sciences at UC Berkeley in 2023, where I was advised by Moritz Hardt and Michael Jordan. I spent the summer of 2020 interning at Apple AI Research, hosted by Vitaly Feldman. My PhD research was generously supported by an Apple PhD Fellowship in AI/ML. Before starting my PhD, I completed my BEng in Electrical and Computer Engineering at the University of Novi Sad in Serbia, where I was advised by Dragana Bajovic. During undergrad I spent a summer at Caltech, working with Babak Hassibi.

email: “firstname”.”lastname”@stanford.edu

office: 116 Sequoia Hall

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

*A Note on the Prediction-Powered Bootstrap*

T. Zrnic

*Note* arxiv package

*Active Statistical Inference*

T. Zrnic, E. J. Candès

*International Conference on Machine Learning (ICML) 2024* arxiv code

*Plug-in Performative Optimization*

L. Lin, T. Zrnic

*International Conference on Machine Learning (ICML) 2024* arxiv

*Locally Simultaneous Inference*

T. Zrnic, W. Fithian

*Annals of Statistics (AoS) 2024+* 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

*Preprint* 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

*Prediction and Statistical Inference in Feedback Loops*

T. Zrnic

UC Berkeley EECS