Peer-reviewed Publications / Preprints

Clinical Trial Design and Implementation

• Pradilla, G., Ratcliff, J. J., Hall, A. J., Saville, B. R., Allen, J. W., Paulon, G., and others (2024). Early minimally invasive removal of intracerebral hemorrhage trial. New England Journal of Medicine 390, 1277-1289

Drift-Diffusion Models for Decision Making with Applications in Neuroscience

• Roark, C. L., Paulon, G., Rebaudo, G., McHaney, J. R., Sarkar, A., Chandrasekaran, B. (2024). Individual differences in working memory impact the trajectory of non-native speech category learning. PloS one
• Roark, C. L., Paulon, G., Sarkar, A., Chandrasekaran, B. (2021). Comparing perceptual category learning across modalities in the same individuals. Psychonomic Bulletin & Review 28, 898-909
• Paulon, G., Llanos, F., Chandrasekaran, B., Sarkar, A. (2021). Bayesian semiparametric longitudinal drift-diffusion mixed models for tone learning in adults. Journal of the American Statistical Association 116, 1114-1127 [R Package]
• Paulon, G., Reetzke, R., Chandrasekaran, B., Sarkar, A. (2019). Functional logistic mixed effects models for learning curves from longitudinal binary data. Journal of Speech, Language, and Hearing Research 62, 543-553 [R Package]

Other Miscellaneous Interests

• Paulon, G., Müller, P., Sarkar, A. (2024). Bayesian semiparametric hidden Markov tensor models for time varying random partitions with local variable selection. Bayesian Analysis
• Paulon, G., Müller, P., Sal y Rosas, V. G. (2024). Bayesian nonparametric bivariate survival regression for current status data. Bayesian Analysis 19, 49-75
• Paulon, G., De Iorio, M., Guglielmi, A., Ieva, F. (2020). Joint modeling of recurrent events and survival: a Bayesian non-parametric approach. Biostatistics 21, 1-14
• Paulon, G., Trippa, L., Müller, P. (2018). Invited comment on “Bayesian cluster analysis: point estimation and credible balls”. Bayesian Analysis 13, 590-593 [Markdown]

Teaching

During my time as a PhD student at the University of Texas at Austin, I designed and taught the course “Statistical Learning and Inference” to a class of about 50 upper-level undergraduate students twice (Fall 2019 and Fall 2020). The course introduced students to ideas of statistical learning and predictive modeling from a statistical, theoretical and computational perspective. All course materials are publicly available here.

Talks & Presentations

• Invited talk: Forecasting with Confidence: Harnessing Predictive Probabilities in Practice. Society for Clinical Trials (SCT), Boston, MA; May, 2024
• Invited talk: ENRICH: A Novel Approach to Clinical Trials in ICH. Midwest Biopharmaceutical Statistics Workshop (MBSW), Indianapolis, IN; June, 2024
• Session chair: Real World Experience in Conducting Adaptive Platform Trials. Speakers: Ed Mills (Cytel), Michelle Detry (Berry Consultants, LLC), Anna McGlothlin (Berry Consultants, LLC), Hong Yu (Massachusetts General Hospital). Society for Clinical Trials (SCT), San Diego, CA; May, 2022
• Dissertation Defense: Bayesian partition models for local inference in longitudinal and survival data. The University of Texas at Austin; August, 2021
• Invited talk: Bayesian semiparametric longitudinal drift-diffusion mixed models for tone learning. Joint Statistical Meetings (JSM), Seattle, WA; August, 2021
• Invited talk: Bayesian semiparametric longitudinal drift-diffusion mixed models for tone learning. International Society for Bayesian Analysis (ISBA), Kunming (China); July, 2021
• Invited talk: Dependent mixtures: Modeling cell lineage. International Chinese Statistical Association (ICSA), Houston, TX; December, 2020
• SDS Seminar Series: Bayesian semiparametric longitudinal drift-diffusion mixed models for tone learning. The University of Texas at Austin; February, 2020
• Poster: Bayesian autoregressive models for waiting times of recurrent events. Bayesian Young Statisticians Meeting (BAYSM), Florence (Italy); June, 2016