I like randomness, and as luck would have it, I research it for a living. I am most interested in stochastic systems, with a focus on causal inference from time series data. I work on theoretical and applied problems, including:

• how to model dynamical systems in biology (e.g. single-cell genomics data) and climate sciences

• proving necessary and sufficient conditions for the identifiability of dynamical systems from observational data

• methods for estimating parametric models from observational data (e.g. SDE parameter estimation)

I am broadly interested in probability theory and information theory, with previous research in data aggregation, synthetic data generation, and feature importance for data explanation. My project repositories are available on my github: https://github.com/guanton

Selected Works

1. Guan, V., Janssen, J., Rahmani, H., Warren, A., Zhang, S., Robeva, E., & Schiebinger, G. (2024). Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots. arXiv preprint arXiv:2410.22729.

2. Guan, V., Guépin, F., Cretu, A.-M., & de Montjoye, Y.-A. (2024). A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data. Proc. Priv. Enhancing Technol., 2024(4)

3. Janssen, J., Guan, V., & Robeva, E. (2023, April). Ultra-marginal feature importance: Learning from data with causal guarantees. In International Conference on Artificial Intelligence and Statistics (pp. 10782-10814). PMLR.