Portfolio item number 1
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Posts by Collection
portfolio
Portfolio item number 2
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publications
Approximate filtering via discrete dual processes
Published in Stochastic Processes and their Applications, 2024
This paper gives an extensive analysis of approximate (and exact) filtering strategies, when the dual process is a Markov chain on a discrete state space.
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Conjugate gradient methods for high-dimensional GLMMs
Published in Journal of the American Statistical Association, 2025
This paper studies the spectrum of the complex sparse high-dimensional matrices arising from mixed models, and relates it to the convergence rate of conjugate gradient.
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Linear-cost unbiased posterior estimates for crossed effects and matrix factorization models via couplings
Published in ArXiv, 2026
In this paper, we show that two-step coupling strategies achieve coalescence times that match the relaxation times of blocked Gibbs-Sampler schemes up to logarithmic factors.
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talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Statistics (Fall 2022, Fall 2023, Fall 2024)
Undergraduate course, Bocconi University, 2024
Introduction to descriptive statistics and inference.
Quantitative methods for finance (Spring 2024, Spring 2025)
Undergraduate course, Bocconi University, 2025
Quantitative modeling in finance, with a focus on portfolio theory.
Mathematics (Fall 2023, Fall 2024, Fall 2025)
Undergraduate course, Bocconi University, 2025
Introduction to mathematical tools for economic and finance models.
Monte Carlo Methods (Spring 2026)
Undergraduate course, ENSAE, Paris, 2026
Foundations of Monte Carlo and quasi-Monte Carlo methods.
Theoretical foundations of Machine Learning (Spring 2026)
Undergraduate course, ENSAE, Paris, 2026
Introduction to machine learning methods.