Luca Masserano

Hello! I am Luca, a PhD student in the Joint PhD Program in Statistics and Machine Learning at Carnegie Mellon University, where I am fortunate to be advised by Ann B. Lee and Barnabás Póczos.

I am broadly interested in statistics and machine learning, with a current focus on robust and reliable simulation-based inference. I am also curious (and keen to learn more) about uncertainty quantification, forecasting and optimization. At CMU, I am part of the Statistical Methods for the Physical Sciences (STAMPS) group. My research has also been supported by the National Science Foundation (grant #2020295).

I spent this summer as a Machine Learning Scientist Intern at AWS AI Labs in Berlin (Germany), working with Syama Sundar Rangapuram and Lorenzo Stella on integrating mixed-integer programs as differentiable blocks of end-to-end deep learning pipelines.

Before joining CMU, I obtained an M.Sc. in Data Science at Bocconi University in Milan (Italy), where I was advised by Igor Pruenster and Antonio Lijoi.

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We developed theory and methodology to embed arbitrary mixed-integer programs as differentiable blocks of deep learning pipelines via stochastic perturbations of the optimization inputs. We also proposed to exploit inluence functions to do sensitivity analysis on the combinatorial solvers and drive perturbations in the optimal direction.

WALDO allows to exploit arbitrary prediction algorithms and posterior estimators to construct reliable confidence sets for parameters of interest in simulation-based inference, i.e. when the likelihood is intractable but we can sample from it. Confidence sets from WALDO are guaranteed to be valid at the correct coverage level without being overly conservative. In addition, one can still exploit prior knowledge to achieve tighter constraints.

We present an adaptive sampling strategy that selects the part of the time series history that is relevant for forecasting. We achieve this by learning a discrete distribution over relevant time steps by Bayesian optimization. We instantiate this idea with a two-step method that is pre-trained with uniform sampling and then training a lightweight adaptive architecture with adaptive sampling.

In this work, we propose a unified and modular inference framework that bridges classical statistics and modern machine learning in SBI/LFI providing (i) a practical approach to the Neyman construction of confidence sets with frequentist finite-sample coverage for any value of the unknown parameters; and (ii) interpretable diagnostics that estimate the empirical coverage across the entire parameter space.

Developed theory and methodology to embed arbitrary mixed-integer programs as differentiable blocks of deep learning pipelines via stochastic perturbations of the optimization inputs. Proposed to exploit inluence functions to do sensitivity analysis on the combinatorial solvers and drive perturbations in the optimal direction.

Built a time series forecasting method that is robust under distribution shifts. I proposed a novel adaptive sampling approach and delivered an implementation that (i) avoids noisy data regions, (ii) focuses on relevant shifted region in the past, and (iii) has also promising first results with real-world datasets with known distribution shifts.

Designed and developed a new research platform that allows to inspect the downstream effect of any modification in a suite of equity risk models. This platform streamlined the research process by reducing time between idea generation and implementation. I also worked with software engineers to refine compliance of production code with quantitative models' logic.

Exploited various statistical models to improve real-time detection of damaged integrated circuits produced in a semiconductor plant in southern Italy.

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