publications and talks

2017

Pourzanjani, A., Jiang, R.M., Atzberger, P.J. and Petzold, L.R., 2017. General Bayesian Inference over the Stiefel Manifold via the Givens TransformarXiv preprint arXiv:1710.09443.

Pourzanjani A., Bales B.B., Harrington M. Petzold, L. R. (2018, January). Alzheimer’s Disease Progression. Presentation at Stancon 2018.

Pourzanjani, A., Jiang, R. M., Petzold, L. R. (2017, December). Improving the Identifiability of Neural Networks for Bayesian Inference. Poster session presented at NIPS Workshop on Bayesian Deep Learning.

Pourzanjani A., Bales B.B., Harrington M. Petzold, L. R. (2017, December). A Statistical Model for Automatic Staging and Prediction of Alzheimers Disease. Poster session presented at the Annual Finch Alzheimer’s Disease Symposium.

Pourzanjani, A., Wu T., Jiang, R. M., Cohen, M., Petzold, L. R. (2017). Understanding Coagulopathy using Multi-view Data in the Presence of Sub-Cohorts: A Hierarchical Subspace Approach. To Appear: Proceedings of Machine Learning for Healthcare 2017, JMLR W&C Track Volume 68.

2016

Pourzanjani, A., Quisel, T., Foschini, L. (2016). Adherent use of digital health trackers is associated with weight loss. PloS one, 11(4), e0152504.

2015

Pourzanjani, A., Stuck, D., Sontag, D., Foschini, L. (2015, December). Fully Bayesian Unsupervised Disease Progression Modeling. Poster session presented at NIPS Workshop on Machine Learning for Healthcare.

Pourzanjani, A., Herzog, E. D., Petzold, L. R. (2015). On the inference of functional circadian networks using Granger Causality. PloS one, 10(9), e0137540.