Giosuè Migliorini

I am a Ph.D. Student in Statistics at UC Irvine, advised by Prof. Padhraic Smyth.

I had the privilege to spend the summer of 2023 working at Los Alamos National Laboratory, under the mentorship of Dr. Natalie E. Klein, where I applied Bayesian deep learning to scientific problems.

Before moving to the U.S., I lived and studied in Italy, where I completed my Master's in Data Science at Bocconi University. You can check out my Master's thesis on computational methods for Bayesian variable selection at this link, completed under the supervision of Prof. Giacomo Zanella at Bocconi's Bayes Lab.

Email  /  GitHub  /  Google Scholar  /  LinkedIn  /  CV

Research

I'm interested in deep generative models, optimal transport, and Bayesian deep learning.

Latent Trajectory Learning from the Interaction of Discrete Particles

Consider time series whose dynamics can be described by discrete latent states. Our approach models the latent trajectory as the solution to a multi-marginal Schrödinger bridge problem in a discrete state space. The proposed methodology enables trajectory reconstruction and extrapolation for irregularly-observed time series data in high-dimensional state spaces.

A Generative Diffusion Model for Probabilistic Ensembles of Precipitation Maps Conditioned on Multisensor Satellite Observations

Residual diffusion model for probabilistic predictions of precipitation maps from infrared and microwave radiometric measurements.

Dynamic Conditional Optimal Transport through Simulation-Free Flows

We introduce a dynamic formulation of conditional optimal transport, and incorporate it into the flow matching framework. We showcase the method on challenging Bayesian inverse problems.

Functional Flow Matching

Theory and methodology for flow matching in infinite-dimensional spaces, for training and sampling at arbitrary resolutions.

Teaching

• Statistical Methods II (STATS 211P - Teaching Assistant): weekly discussion sessions, office hours, and homework grading for students of the Masters of Data Science at UCI. Topics include generalized linear models, PCA, regularization, model selection, splines, mixed-effects models.
• Introduction to Probability and Statistics (STATS 120B - Reader): undergraduate-level class focusing on maximum likelihood and hypothesis testing.
• Introduction to Probability and Statistics for Computer Science (STATS 67 - Reader): undergraduate-level class on basic statistics and probability theory.

Education

• Ph.D. in Statistics - University of California, Irvine (2022 - present)
• M.Sc. in Data Science (summa cum laude) - Bocconi University (2020 - 2022)
• B.Sc. in Economics (summa cum laude) - University of Padova (2017 - 2020)

Design and source code from Jon Barron's website