Brief Abstract: We propose a semiparametric sieve likelihood approach for fitting linear regression models to data with both left-truncation and right-censoring, common in cohort studies of disease onset. Our method yields consistent, asymptotically normal, and semiparametrically efficient estimators, as demonstrated through simulations across various error distributions. We illustrate its effectiveness with an application to The 90+ Study on aging and dementia.

Notes and Links:

• Paper (coming soon) (Submitted to Statistics in Medicine)
• Paper code and ltrc R Package

Brief Abstract: Building on results from the project above, we aim to establish a general theorem for the joint normality of spline parameter estimates in semiparametric models. Key challenges include handling bundled parameters, controlling spline approximation error, and addressing high dimensionality as the number of knots increases with sample size. Leveraging insights from random matrix theory, we will apply our results to semiparametric linear models and the Cox model to demonstrate their practical utility.

Notes and Links:

• Work in Progress with planned completion in the Spring of 2026

Brief Abstract: We introduce a novel framework for analyzing age-related disease data, presenting three key metrics that capture disease dynamics in a population. Using data from the National Alzheimer’s Disease Coordinating Center, we illustrate these metrics and demonstrate differences in the Golden Health Index based on APOE E4 allele count.

Notes and Links:

• Paper (coming soon)
• Code (coming soon)
• Slides (coming soon)

Brief Abstract: We address left-truncation and the regression case for the previously introduced metric: The Golden Health Index. We present theoretical results, simulations, and an application to the NACC dataset, demonstrating its utility in analyzing population health trajectories and identifying risk factors for accelerated decline.

Notes and Links:

• Paper (coming soon)
• Code (coming soon)
• Slides (coming soon) (to be presented at WNAR 2025)

Brief Abstract: Synthetic financial time series generation has primarily focused on high-frequency trading, leaving longer-term portfolio management underexplored. We propose a framework with a novel evaluation metric to improve the fidelity of synthetic data for long-term investors. This approach enables more realistic stochastic backtesting, helping investors refine portfolio strategies over extended time horizons.

Notes and Links:

• Extended Abstract (accepted for refereed presentation at SDSS 2025)
• Talk Slides (Coming Soon)
• Talk Transcript (Coming Soon)

Brief Abstract: We examined whether COVID-19 was associated with cognitive decline in older adults using data from the UC Irvine Alzheimer’s Disease Research Center. No increased risk of cognitive diagnosis changes or significant declines in neuropsychological tests were observed. These findings suggest that older adults do not experience long-term cognitive decline after COVID-19 based on a comprehensive evaluation of functional and cognitive abilities.

Notes and Links:

• Joint work with Arunima Kapoor, David L. Sultzer, Michelle McDonnell, and Bin Nan
• Paper (coming soon)

Brief Abstract: Inclusion body myositis (IBM) is a progressive muscle disease affecting individuals over 40, with an annual strength decline of 3.5–5.4%. Anti-NT5c1A antibodies serve as a potential biomarker, but their relationship to disease severity remains unclear, with conflicting studies suggesting either a more severe phenotype in seropositive patients or no significant differences. These findings highlight the need for further research into IBM’s clinical features while accounting for serological status and disease duration.

Notes and Links:

• Joint work with Mercedes Herrera, Marie Wencel, and the INSPIRE-IBM Team
• Poster (presented at MDA 2024)

Brief Abstract: We analyze data from men's professional beach volleyball leagues to classify players into four groups using k-means clustering and assess which pairings lead to the most success. While two player groups perform best with partners from the same group, the other two thrive with different-group partners. We examine key metrics in the most successful pairings and discuss future applications of this approach.

Notes and Links:

• Poster (presented at UCONN Sports Analytics Symposium 2021)

Brief Abstract: This paper examines the tuning and performance of two-part models on insurance datasets from the Casualty Actuarial Society (CAS), covering bodily injury, property damage, and collision claims. After exploring and summarizing the data, we tune and train models, analyzing select predictions. Our approach provides a foundation for future ratemaking models to be developed and tested more efficiently.

Notes and Links:

• Published Paper PDF
• Github Repository
• Permanent DOI Link

Brief Abstract: We propose mSHAP, a fast method for computing SHAP values in two-part insurance models, improving interpretability and reducing model risk. Our approach outperforms kernelSHAP and is applied to auto insurance pricing, with an R package (mshap) available for broader use.

Notes and Links:

• Published Paper PDF
• Github Repository
• R package mshap on CRAN (> 10k downloads)
• Presentation Slides
• Permanent DOI Link