I am a postdoc researcher at KTH Royal Institute of Technology in Stockholm, Sweden, working with Liam Solus in the Mathematics of Data and AI group. My research interests include causality, machine learning, and the methodology and philosophy of science, especially when applied to the cognitive and brain sciences. In particular, I develop causal discovery methods, using tools from a variety of mathematical fields, including combinatorics and algebraic statistics. I did my PhD with Moritz Grosse-Wentrup in the Research Group Neuroinformatics at the University of Vienna, where we studied how neural activity gives rise to cognition and behavior, with a special focus on causal discovery methods, neuroimaging data, and brain-computer interfaces.


  • Causality and Machine Learning
  • Graph Theory and Algebraic Statistics
  • Methodology and Philosophy of Science


  • PhD in Computer Science, 2022

    University of Vienna, Austria

  • MS in Logic, Computation, and Methodology, 2017

    Carnegie Mellon University, Pittsburgh, PA

  • BA in Philosophy, BA in Mathematics, 2015

    St. Mary's University, San Antonio, TX


(2023). Neuro-Causal Factor Analysis. arXiv:2305.19802 [stat.ML].

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(2022). Combinatorial and algebraic perspectives on the marginal independence structure of Bayesian networks. arXiv:2210.00822 [stat.ME].

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(2022). A Transformational Characterization of Unconditionally Equivalent Bayesian Networks. 11th International Conference on Probabilistic Graphical Models (PGM).

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(2022). A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations. Conference on Causal Learning and Reasoning (CLeaR).

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(2021). Distance Covariance: A Nonlinear Extension of Riemannian Geometry for EEG-based Brain-Computer Interfacing. 2021 IEEE International Conference on Systems, Man, And Cybernetics (SMC).


Talks and Posters