NBA coaches and general managers are tasked with building lineups and rosters that maximize their chances of winning. Further, basketball is a team sport where interactions between the players in a lineup can be integral ...

The de-identification of publicly released datasets that contain personal information is necessary to preserve personal privacy. One such de-identification algorithm, k-anonymization, reduces the risk of the re-identification ...

Differential privacy is one recent framework for analyzing and quantifying the amount of privacy lost when data is released. Meanwhile, multiple imputation is an existing Bayesian-inference based technique from statistics ...

This thesis presents five contributions to machine learning, with themes of differentiability and Bayesian inference. We present Firefly Monte Carlo, an auxiliary variable Markov chain Monte Carlo algorithm that only ...

Neuroscience is entering an exciting new age. Modern recording technologies enable simultaneous measurements of thousands of neurons in organisms performing complex behaviors. Such recordings offer an unprecedented opportunity ...

Undiagnosed mental illness poses a significant health risk. In-person screenings to identify individuals at-risk of mental illness are expensive, time-consuming, and often inaccurate. This report presents an array of ...

Galvanized by the accelerated pace and ease of data collection, researchers in more and more disciplines are turning to large, heterogeneous datasets to answer scientific questions. Divining insight from massive and complex ...

Musical influence is a topic of interest and debate among critics, historians, and general listeners alike, yet to date there has been limited work done to tackle the subject in a quantitative way. In this thesis, we address ...

Many problems in data science and machine learning require identifying latent features that occur in a set of observations. For example, given a set of images, we may want to determine what objects are contained in each ...

The theory of causal inference, as formalized by the potential outcomes framework, relies on an assumption that the experimental units are independent. When independence is not tenable, we say there is interference, and ...