Michael Wieck-Sosa

I am a fourth-year PhD Student in the Department of Statistics & Data Science 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:

• Dependent data (high-dimensional time series, spatiotemporal data, time-varying networks, nonstationary, nonlinear)
• Causality (causal discovery, invariant causal prediction, causal representation learning, conditional independence testing)
• Machine learning (neural networks, transformers, foundation models, large language models)
• Likelihood-free methods (estimation, confidence sets, goodness-of-fit testing, random feature methods, change-point detection)

Research

Papers

• Estimating dynamic models by matching random features (with Cosma Shalizi). Manuscript to be posted on arXiv upon publication, in accordance with the journal’s policies. Preprint. Slides. Code.
• Dynamic models with p parameters are identified by 2p+1 random features (with Cosma Shalizi). Manuscript to be posted on arXiv upon publication, in accordance with the journal’s policies. Preprint.
• Conditional independence testing with a single realization of a multivariate nonstationary nonlinear time series (with Michel F. C. Haddad and Aaditya Ramdas). Preprint. Slides. Code.

Works in progress

• Deep conditional independence testing for multimodal nonstationary time series with transformers: Applications to text sequences and LLMs (with Aaditya Ramdas).
• Learning predictive states from large-scale time series data (with Cosma Shalizi).
• Simulation-based inference through random features (with Cosma Shalizi).

News

• May 2026: I’ll be giving a 20-minute talk at IWSM 2026.
• April 2026: I was awarded the DeGroot-Goel Fellowship for 2026 by the CMU Statistics faculty.
• April 2026: I successfully proposed my thesis.