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Correlations in Monte Carlo eigenvalue simulations : uncertainty quantification, prediction and reduction
(Massachusetts Institute of Technology, 2018)
Monte Carlo methods have mostly been used as a benchmark tool for other transport and diffusion methods in nuclear reactor analysis. One important feature of Monte Carlo calculations is the report of the variance of the ...
Development of an isotope-sensitive warhead verification technique using nuclear resonance fluorescence
(Massachusetts Institute of Technology, 2019)
Nearly three decades after the end of the Cold War, nuclear arms control remains a pressing global issue. Despite obligations under the Treaty on the Non-Proliferation of Nuclear Weapons (NPT) to make 'good-faith efforts' ...
Electronic structure of perovskite oxide surfaces at elevated temperatures and its correlation with oxygen reduction reactivity
(Massachusetts Institute of Technology, 2014)
The objective is to understand the origin of the local oxygen reduction reaction (ORR) activity on the basis of the local electronic structure at the surface of transition metal oxides at elevated temperatures and in oxygen ...
Modeling radiation-induced mixing at interfaces between low solubility metals
(Massachusetts Institute of Technology, 2014)
This thesis studies radiation-induced mixing at interfaces between low solubility metals using molecular dynamics (MD) computer simulations. It provides original contributions on the fundamental mechanisms of radiation-induced ...
Sparsity and robustness in modern statistical estimation
(Massachusetts Institute of Technology, 2018)
Two principles at the forefront of modern machine learning and statistics are sparse modeling and robustness. Sparse modeling enables the construction of simpler statistical models, with examples including the Lasso and ...
Optimal trees for prediction and prescription
(Massachusetts Institute of Technology, 2018)
For the past 30 years, decision tree methods have been one of the most widely-used approaches in machine learning across industry and academia, due in large part to their interpretability. However, this interpretability ...
Machine learning approaches to challenging problems : interpretable imbalanced classification, interpretable density estimation, and causal inference
(Massachusetts Institute of Technology, 2018)
In this thesis, I address three challenging machine-learning problems. The first problem that we address is the imbalanced data problem. We propose two algorithms to handle highly imbalanced classification problems. The ...
Algorithms for large-scale personalization
(Massachusetts Institute of Technology, 2018)
The term personalization typically refers to the activity of online recommender systems, and while product and content personalization is now ubiquitous in e-commerce, systems today remain relatively primitive: they are ...
Operational decisions and learning for multiproduct retail
(Massachusetts Institute of Technology, 2018)
We study multi-product revenue management problems, focusing on the role of uncertainty in both the seller and the customer decision processes. We begin by considering a logit model framework for personalized revenue ...
Demand uncensored : car-sharing mobility services using data-driven and simulation-based techniques
(Massachusetts Institute of Technology, 2019)
In the design and operation of urban mobility systems, it is often desirable to understand patterns in traveler demand. However, demand is typically unobserved and must be estimated from available data. To address this ...