Research Interests

  • Computational Statistics
  • Latent Variable Estimation
  • Cognitive Diagnostic Methods
  • Restricted Latent Class Models
  • Deep Learning

Journal Articles


  • Jimenez, A., Balamuta, J.J., & Culpepper, S.A., “A sequential exploratory diagnostic model using a Pólya-gamma data augmentation strategy”, British Journal of Mathematical and Statistical Psychology (2023). doi: 10.1111/bmsp.12307
  • Balamuta, J.J. & Culpepper, S.A., “Exploratory Restricted Latent Class Models with Monotonicity Requirements under Pòlya–gamma Data Augmentation”, Psychometrika, 2022. doi: 10.1007/s11336-021-09815-9
  • Culpepper, S.A. & Balamuta, J.J., “Inferring latent structure in polytomous data with a higher-order diagnostic model”, Multivariate Behavioral Research, 2021. doi: 10.1080/00273171.2021.1985949
  • Culpepper, S.A. & Balamuta, J.J., “A Hierarchical Model for Accuracy and Choice on Standardized Tests”, Psychometrika, vol. 82, no. 3, pp. 820-845, 2017. doi: 10.1007/s11336-015-9484-7

Data Science and High Performance Computing (HPC)

  • Curtin, R., Edel, M., Shrit, O., Agrawal, S., Basak, S., Balamuta, J.J., Birmingham, R., Dutt, K., Eddelbuettel, D., Garg, R., Jaiswal, S., Kaushik, A., Kim, S., Mukherjee, A., Sai, N.G., Sharma, N., Parihar, Y.S., Swain, R., & Sanderson, C., “mlpack 4: a fast, header-only C++ machine learning library”, Journal of Open Source Software, 2023. doi: 10.21105/joss.05026
  • Eddelbuettel, D. & Balamuta, J.J., “Extending R with C++: A Brief Introduction to Rcpp”, The American Statistician, vol. 72, no. 1 Special Issue on Data Science, pp. 28-36, 2018. doi: 0.1080/00031305.2017.1375990

Time Series

  • Balamuta, J.J., Molinari, R. , Guerrier, S., & Yang W., “A Computationally Efficient Framework for Automatic Inertial Sensor Calibration”, IEEE Sensors Journal, vol. 18, no. 4, pp. 1636-1646, 2017. doi: 10.1109/JSEN.2017.2773663
  • Guerrier, S., Molinari, R., & Balamuta, J.J., “Discussion on Maximum Likelihood-Based Methods for Inertial Sensor Calibration”, IEEE Sensors Journal, vol. 16, no. 14, pp. 5522-5523, 2016. doi: 10.1109/JSEN.2016.2565389

Conference Proceedings

Time Series

  • Claussen, P., Skaloud, J., Molinari, R., Balamuta, J. J., & Guerrier, S., “An Overview of a New Sensor Calibration Platform”, in Proceeding of the 4th IEEE International Workshop on Metrology for Aerospace, Padova, Italy, 2017. doi: 10.1109/MetroAeroSpace.2017.7999598
    • Best Demonstration Award
  • Guerrier, S., Skaloud, J., Molinari, R., & Balamuta, J.J., “An Inertial Sensor Calibration Platform to Estimate and Select Error Models” in Proceedings of IAIN, Prague, Czech Republic, 2015. doi: 10.1109/IAIN.2015.7352255
  • Molinari, R., Balamuta, J.J., Guerrier, S., & Skaloud, J., “Automatic and Computationally Efficient Method For Model Selection In Inertial Sensor Calibration” in Proceedings of IEEE GNSS+, Tampa, FL, USA, 2015.
  • Balamuta, J.J., Molinari, R., Guerrier, S., & Skaloud, J., “A Computationally Efficient Platform for Inertial Sensor Calibration” in Proceedings of IEEE GNSS+, Tampa, FL, USA, 2015.