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Improvements in data collection and storage capabilities have led to a growing interest in the study of temporal relationships and dynamics for large, complex data.  Scott Bruce is involved in the development of novel statistical methods for the analysis of time series and longitudinal data generated from modern experimental and observational studies in areas such as sleep research, neuroscience, and psychiatry. Many applications in these fields are not served holistically by existing theory and methods; Bruce is passionate about developing practical, computationally efficient analytical tools that address and utilize the full nature of the complexity underlying the data generating process. 

In one of his recent works, he is exploring frequency-domain approaches for assessing associations between temporally-evolving data and outcomes of interest, which can be applied to analyze the relationship between sleep quality and heart rate variability during sleep. Bruce’s methodological research with strong practical applications has led to a number of successful collaborations and publications in high-impact statistics and scientific journals, such as Biometrics and IEEE Transactions on Big Data. In the classroom, Bruce challenges students to think critically about real-world analytical problems, then design and apply statistical data modeling and computational solutions to address them.