Marshall Faculty Publications, Awards, and Honors: March 2024
We are proud to highlight the amazing Marshall faculty who have received awards, recognitions, and publications for their groundbreaking work.
Matteo Sesia is an assistant professor in the department of Data Sciences and Operation, at the USC Marshall School of Business. His research is focused on developing data science methods combining the power of machine learning algorithms with the reliability of rigorous statistical guarantees. While pursuing this goal, he enjoys dividing his time between theoretical, methodological, computational, and applied work. His doctoral research earned the Jerome H. Friedman Applied Statistics Dissertation Award from the Stanford Statistics Department in 2020.
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NEWS + EVENTS
Marshall Faculty Publications, Awards, and Honors: March 2024
We are proud to highlight the amazing Marshall faculty who have received awards, recognitions, and publications for their groundbreaking work.
Marshall Faculty Publications, Awards, and Honors: December 2023/January 2024
We are thrilled to congratulate our faculty on new promotions and recently accepted and published research.
Marshall Faculty Publications, Awards, and Honors: November 2023
We congratulate our distinguished faculty for their recently accepted and published research and awards.
Marshall Faculty Publications, Awards, and Honors: October 2023
We are proud to highlight the amazing Marshall faculty who have been recognized this month for their leading-edge work and expertise.
Marshall Faculty Publications, Awards, and Honors: May 2023 and Year-End Roundup
We are thrilled to congratulate our faculty on recently accepted and published research, 2022-2023 teaching and research awards, and new chair appointments.
RESEARCH + PUBLICATIONS
This paper develops a conformal method to compute prediction intervals for non-parametric regression that can automatically adapt to skewed data. Leveraging black-box machine learning algorithms to estimate the conditional distribution of the outcome using histograms, it translates their output into the shortest prediction intervals with approximate conditional coverage. The resulting prediction intervals provably have marginal coverage in finite samples, while asymptotically achieving conditional coverage and optimal length if the black-box model is consistent. Numerical experiments with simulated and real data demonstrate improved performance compared to state-of-the-art alternatives, including conformalized quantile regression and other distributional conformal prediction approaches.
Supratherapeutic oxygen levels consistently cause oxygen toxicity in the lungs and other organs. The prevalence and severity of hyperoxemia among pediatric intensive care unit (PICU) patients remain unknown. This was the first study to examine the prevalence and duration of hyperoxemia in PICU patients receiving oxygen therapy. This is a retrospective chart review. This was performed in a setting of 36-bed PICU in a quaternary-care children's hospital. All the patients were children aged <18 years, admitted to the PICU for ≥24 hours, receiving oxygen therapy for ≥12 hours who had at least one arterial blood gas during this time.
There was no intervention. Of 5,251 patients admitted to the PICU, 614 were included in the study. On average, these patients received oxygen therapy for 91% of their time in the PICU and remained hyperoxemic, as measured by pulse oximetry, for 65% of their time on oxygen therapy. Patients on oxygen therapy remained hyperoxemic for a median of 38 hours per patient and only 1.1% of patients did not experience any hyperoxemia. Most of the time (87.5%) patients received oxygen therapy through a fraction of inspired oxygen (FiO2)-adjustable device. Mean FiO2 on noninvasive support was 0.56 and on invasive support was 0.37. Mean partial pressure of oxygen (PaO2) on oxygen therapy was 108.7 torr and 3,037 (42.1%) of PaO2 measurements were >100 torr. Despite relatively low FiO2, PICU patients receiving oxygen therapy are commonly exposed to prolonged hyperoxemia, which may contribute to ongoing organ injury.
Robotic-assisted esophagectomy results in similar pain with significantly less analgesic
consumption compared to open and hybrid surgery. Despite longer operation time, RAMIE
patients require shorter post-operative ICU stays compared to open and hybrid surgery
patients, and experience lower blood loss compared to open surgery patients.
In this article we develop a method based on model-X knockoffs to find conditional associations that are consistent across environments, while controlling the false discovery rate. The motivation for this problem is that large datasets may contain numerous associations that are statistically significant and yet misleading, as they are induced by confounders or sampling imperfections. However, associations replicated under different conditions may be more interesting. In fact, sometimes consistency provably leads to valid causal inferences even if conditional associations do not. Although the proposed method is widely applicable, in this paper we highlight its relevance to genome-wide association studies, in which robustness across populations with diverse ancestries mitigates confounding due to unmeasured variants. The effectiveness of this approach is demonstrated by simulations and applications to UK Biobank data.
Genome-wide association studies compare a phenotype to thousands of genetic variants, searching for associations of potential biological interest. Standard analyses rely on linear models of the phenotype given one variable at a time. However, their assumptions are difficult to verify and their univariate approaches make it hard to recognize interesting associations from spurious ones. Our work takes a different path: We analyze all variants simultaneously, modelling the randomness in the genotypes, which is better understood, instead of the phenotype. Our solution accounts for linkage disequilibrium and population structure, controls the false discovery rate, and leverages powerful machine-learning tools. Applications to the UK Biobank data indicate increased power compared to state-of-the-art alternatives and high replicability.
AWARDS
USC Marshall School of Business
05.05.2023
Stanford University
06.15.2020
COURSES