Fraud detection is a critical area where deep learning has been effectively applied to identify and prevent unauthorized transactions, money laundering, and other financial crimes. Traditional rule-based systems and statistical models often struggle to detect sophisticated fraud patterns, particularly when dealing with large volumes of data and rapidly evolving fraud techniques. In contrast, deep learning models, such as CNNs, RNNs, and autoencoders, have proven highly effective in analyzing complex, high-dimensional transaction data and detecting subtle, non-linear patterns indicative of fraudulent activity.
In this project, we build a User ID-based fraud detection model using autoencoders, trained on unlabelled real-world credit card transaction data, capable of detecting fraud with a precision of up to 35% and a recall of up to 72%, performing significantly better than traditional ML/statistical baseline models..

Known as the most dangerous highway in Tennessee, Music Highway, the stretch of Interstate 40 between Memphis and Nashville, could use a serious tuning up. This project investigates the effectiveness and cost-efficiency of potential physical safety interventions along its Madison and Henderson County segments, with the goal of reducing crash severity. We used a data-driven geospatial modeling approach to assess whether adding specific safety features to targeted segments predicts statistically significant changes in crash injury outcomes.

In this project, trained a cycleGAN on speech data and singing data to create a voice synthesizer that takes speech and outputs a synthesized voice to play over a given song.

With recent major cuts to governmental regulation agencies in the US, we investigate whether those cuts are justified. In particular, we analyze the efficacy of RGGI, a state-level cap-and-trade program designed to regulate CO2 emissions in power plants. By using synthetic controls, we answer the counterfactual question: "how would CO2 emissions look in a world where RGGI was never enacted?".

Internet use has been identified by researchers as having the potential to rise to the level of addiction, with associated increased rates of anxiety and depression. Identifying cases of problematic internet usage currently requires evaluation by an expert, however, which is a significant impediment to screening children and adolescents across society. One potential solution is to rely on data that is more easily and uniformly collected: the kind collected by a family physician, a simple survey, or by a smartwatch. The research question this project sets out to answer is: “Can we predict the level of problematic internet usage exhibited by children and adolescents, based on their physical activity and survey responses?”