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 reframes the well-known Wald test and uses Neyman inversion to convert point predictions and posteriors from any prediction algorithm or posterior estimator to confidence sets with correct conditional coverage, even for finite observed sample sizes.

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.

This paper presents (i) a practical procedure for the Neyman construction of confidence sets, and (ii) diagnostics that estimate conditional coverage over the entire parameter space. Any method that defines a test statistic can leverage this machinery to create valid confidence sets and diagnostics without costly Monte Carlo samples.

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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