CV

Education

Hansen, B., Avalos-Pacheco A., Russo M., De Vito R. Fast Variational Inference for Bayesian Factor Analysis in Single and Multi-Study Settings, arXiv:2305.13188 [submitted]
Hansen, B., Avalos-Pacheco, A., Russo, M., & De Vito, R. A Variational Bayes Approach to Factor Analysis. Bayesian Statistics, New Generations New Approaches (pp. 1521). Springer International Publishing. 2023. https://doi.org/10.1007/978-3-031-42413-7
Liu, T.,^*, Chambers, L.C.^*, Hansen, B,.,^*, Bazerman L.B., Cachay, E.R., Christopoulos, K., Drainoni, M., Gillani, F.S., Mayer, K.H., Moore, R.D., Rana, A., Beckwith, C.G., Risk of HIV Viral Rebound in the Era of Universal Treatment in a Multicenter Sample of Persons With HIV in Primary Care, Open Forum Infectious Diseases, Volume 10, Issue 6, June 2023, ofad257, https://doi.org/10.1093/ofid/ofad257

Non-negative Matrix Factorization: Understanding the Genres of Mutations in Cancer, Take-Out Thursdays Lightning Talks, Brown University Department of Biostatistics, November 2023.
Variational Inference for Nonnegative Matrix Factorization Models, Annual PhD Research Presentation, Brown University Department of Biostatistics, April 2023.
Fast Variational Inference for Bayesian Multi-Study Factor Analysis, Annual PhD Research Presentation, Brown University Department of Biostatistics, May 2022
Fast Variational Inference for Bayesian Multi-study Factor Analysis, BaYSM (computation session), Montreal, Canada, June 2021.

January 2019 - June 2020: Data Science Co-op
- Healthcare Technology Center, Johnson & Johnson
- Deliver high-quality Data Science solution and produce business value across many groups within Technology Services in collaboration with small teams of Data Scientists
- Follow best Data Science practices including programming practices, version control, AGILE
- Programming Languages: R, Python, Git