Biography
Francisco completed his PhD in the Computer Laboratory in 2025. He was interested in the duality between optimisation and sampling with a focus on applications. In particular, exploring stochastic control based methodologies (e.g. Schrödinger Bridges) in practical contexts such as Bayesian machine learning as well as generative modelling, for example developing better samplers for Bayesian Deep Learning. Overall, he aimed to focus on dynamical formulations of different learning tasks to explore physically motivated efficient algorithms, always keeping the practical/application component as the main focus.
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Recent Publications
Dimensionality Reduction as Probabilistic Inference
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Bayesian learning via neural Schrödinger–Föllmer flows
Statistics and Computing, 33(3):
Adversarial Concept Erasure in Kernel Space
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Efficient Representations for Privacy-Preserving Inference
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Solving Schrödinger Bridges via Maximum Likelihood
Entropy, 23(9):1134