Publications

Compactness criterion for semimartingale laws and semimartingale optimal transport, with Ariel Neufeld, Trans. Amer. Math. Soc. Vol. 372, No. 1, 187-- 231.

 Supermartingale deflators in the absence of a numéraire, with Philipp Harms and Ariel Neufeld, Math. Finan. Econ., No. 15, 885--915.

Examples of Itô càdlàg rough paths, with David J. Proemel, Proc. Amer. Math. Soc. Vol. 146, No. 11, p. 4937-- 4950. 

Characterization of non-linear Besov spaces, with David J. Proemel and Josef Teichmann, Trans. Amer. Math. Soc. Vol. 373, No. 1, 529--550.

Stochastic analysis with modelled distributions, with David J. Proemel and Josef Teichmann, Stoch. PDE. Anal. Comp., Vol. 9, No. 2, 343--379.

On the Sobolev rough paths, with David J. Proemel and Josef Teichmann, J. Math. Anal. Appl., Vol. 497, No. 1.

Optimal extension to Sobolev rough paths, with David J. Proemel and Josef Teichmann, Potential Analysis. Vol. 59, 1399--1424.

A Sobolev rough path extension theorem via regularity structures, with David J. Proemel and Josef Teichmann, ESAIM: Probab. Stat., Vol. 27, 136--155.

Adapted topologies and higher rank signatures, with Patric Bonnier and Harald Oberhauser, Ann. Appl. Probab., Vol. 33, No. 3, 2136--2175.

Càdlàg rough differential equations with reflecting barriers, with Andrew. L. Allan and David J. Proemel, Stoch. Process. Appl., Vol. 142, 79--104.

Pathwise convergence of the Euler scheme for rough and stochastic differential equations, with Andrew. L. Allan, Anna P. Kwossek and David J. Proemel, Preprint, arXiv: 2309.16489.

Model-free Portfolio Theory: A Rough Path Approach, with Andrew. L. Allan, Christa Cuchiero and David J. Proemel, Mathematical Finance,  Vol. 33, No. 3, 709--765.

A Càdlàg Rough Path Foundation for Robust Finance, with Andrew. L. Allan and David J. Proemel, Finance and Stochastics, Published Online, DOI: 10.1007/s00780-023-00522-0.

Optimal Stopping via Distribution Regression: a Higher Rank Signature Approach, with C. Salvi, M. Lemercier, B. Horvath, and T. Lyons,  Preprint, arXiv: 2304.01479.

 Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes, with C. Salvi, M. Lemercier, B. Horvath, T. Damoulas and T. Lyons, Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021) .

An Approximation Theory for Metric Space-Valued Functions: With a View Towards Deep Learning, with A. Kratsios, M. Lassas, M. V. de Hoop and I. Dokmanic, Preprint, arXiv: 2304.12231.

High Rank Path Development: an Approach of Learning the Filtration of Stochastic Processes, with Jiajie Tao and Hao Ni, Preprint, arXiv: 2405.14913.