My research focuses on rough analysis, deep learning and kernel methods. I am particularly interested in the interplay between neural networks and differential equations, as well as in developing algorithms to extract actionable information from high-dimensional, irregularly-sampled time series.
Prior to joining Imperial, I obtained my PhD from the University of Oxford under the supervision of Prof. Terry Lyons.
et al., 2021, The signature kernel is the solution of a Goursat PDE, Siam Journal on Mathematics of Data Science, Vol:3, ISSN:2577-0187, Pages:873-899
Salvi C, Lemercier M, Gerasimovics A, Neural stochastic PDEs: resolution-invariant learning of continuous spatiotemporal dynamics, Thirty-sixth Conference on Neural Information Processing Systems, NeurIPS
et al., Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes, Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021)
et al., 2021, SigGPDE: scaling sparse Gaussian processes on sequential data, Thirty-eighth International Conference on Machine Learning (ICML-2021), PMLR, Pages:6233-6242, ISSN:2640-3498
et al., Neural Rough Differential Equations for Long Time Series, Thirty-eighth International Conference on Machine Learning (ICML 2021)
et al., SK-Tree: a systematic malware detection algorithm on streaming trees via the signature kernel, 2021 IEEE International Conference on Cybersecurity and Resilience
et al., Distribution Regression for Sequential Data, The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021)
Arribas Perez I, Salvi C, Szpruch L, Sig-SDEs model for quantitative finance, 1st ACM International Conference on AI in Finance (ICAIF 2020)
et al., Deep Signature Transforms, Advances in Neural Information Processing Systems 32 (NeurIPS 2019)