Michael Wieck-Sosa

I am a fourth-year PhD Student in Statistics at Carnegie Mellon University.

I am very lucky to be advised by Aaditya Ramdas and Cosma Shalizi.

Here is my resume. You can reach me at mwiecksosa AT cmu DOT edu.

My research interests are at the intersection of:

• Dependence (high-dimensional time series, nonlinear dynamics, time-varying networks)
• Causality (causal discovery, invariant causal prediction, conditional independence testing)
• Likelihood-free methods (parameter estimation, uncertainty quantification, goodness-of-fit)
• Machine learning (neural networks, transformers, large language models)

Research

Papers

• Estimating dynamic models by matching random features (with Cosma Shalizi). Submitted to the Journal of the American Statistical Association. Preprint. Code.
• Dynamic models with p parameters are identified by 2p+1 random features (with Cosma Shalizi). Submitted to Physical Review E. Preprint.
• The dynamic generalized covariance measure for conditional independence testing with nonstationary time series (with Michel F. C. Haddad and Aaditya Ramdas). Minor revision at the Journal of Business & Economic Statistics. Preprint. Slides. Code.

Current projects

• Learning generative sequence models: fast rates via low-dimensional predictive states (with Cosma Shalizi).
• Invariant causal prediction for nonlinear time series with the higher-order generalized covariance measure (with Aaditya Ramdas).
• Learning the signature of a large language model (with Cosma Shalizi).
• Simulation-based inference through random features (with Cosma Shalizi).
• Viral load kinetics reveal population infectivity as a driver of disease transmission (with James Hay, Michel F. C. Haddad, Cosma Shalizi, and Aaditya Ramdas).
• How foundation models learn temporal dependencies in practice (with Tom Zhang* and Chad Schafer).

* Denotes students advised.

News

• December 2026: I’ll be giving an invited talk at CFE-CMStatistics 2026.
• June 2026: I’ll be giving an invited talk at IWSM 2026.
• April 2026: I was awarded the DeGroot-Goel Fellowship for 2026 by the CMU Statistics & Data Science Department faculty.
• April 2026: I successfully proposed my thesis. Topics: generative models and time series theory/methods.