PUBLICATIONS:

  • Chazal, F., Fasy, B.T., Lecci, F., Michel, B., Rinaldo, A., and Wasserman, L., (2015), Subsampling Methods for Persistent Homology, the Proceedings of the 32nd International Conference on Machine Learning (ICML), 214–2151. (arXiv:1406.1901)
  • Becker, J.T., Junker, B., Kuller, L., Lecci, F. and Lopez, O., (2015), “Empirically Derived Trajectories to Dementia Over 15 Years of Follow-Up Identified Using Mixed Membership Models”, American Journal of Epidemiology: kwv051.
  • Molsberry, S., Lecci, F., Kingsley, L., Junker, B., Reynolds, S., Goodkin, K., Levine, A., Martin, E., Miller E., Munro, C., Ragin A., Sacktor N., Becker, J., (2015), “Mixed Membership Models Identify Risk Factors to Trajectories for Cognitive Impairment in the Multicenter AIDS Cohort Study”, Journal of the International AIDS Society, 29.6: 713-721.
  • Fasy, B.T., Lecci, F., Rinaldo, A., Wasserman, L., Balakrishnan, S. and Singh. A., (2014), “Confidence Sets for Persistence Diagrams”, The Annals of Statistics, 42(6), 2301–2339. (arXiv:1303.7117)
  • Lecci, F., Rinaldo, A. and Wasserman, L., (2014), “Statistical Analysis of Metric Graph Reconstruction”, Journal of Machine Learning Research, 15, 3425–3446. (arXiv:1305.1212)
  • Lecci, F., (2014) “An Analysis of Development of Dementia through the Extended Trajectory Grade of Membership Model”, in Airoldi, E., Blei, D., Erosheva, E., and Fienberg, S.E., (Eds.), Handbook of Mixed Membership Models and Their Applications, Chapman & Hall.
  • Chazal, F., Fasy, B.T., Lecci, F., Rinaldo, A., and Wasserman, L., (2014), “Stochastic Convergence of Persistence Landscapes and Silhouettes”, in The Proceedings of the 30th Symposium of Computational Geometry (SoCG), pp. 474 – 483. (arXiv:1312.0308)
  • Chazal, F., Fasy, B.T., Lecci, F., Rinaldo, A., Singh, A. and Wasserman, L., (2013), “On the Bootstrap for Persistent Diagrams and Landscapes”, Modeling and Analysis of Information Systems, 20:6, 96–105. (arXiv:1311.0376)


PAPERS UNDER REVIEW
:
  • Adhikari, S., Lecci, F., Junker, B., and Tibshirani, R.J., (2015), “High-dimensional Longitudinal Classification with the Multinomial Fused Lasso”, submitted to The Annals of Applied Statistics (arXiv:1501.07518)
  • Chazal, F., Fasy, B.T., Lecci, F., Michel, B., Rinaldo, A., Wasserman, L., (2016), “Robust Topological Inference: Distance-to-a-Measure and Kernel Distance”, submitted to Journal of Machine Learning Research. (arXiv:1412.7197)
  • Fasy, B.T., Kim, J., Lecci, F., and Maria, C. (2015), “Introduction to the R Package TDA”. (arXiv:1411.1830)


SELECTED TALKS:
  • (slides) Statistical Inference for Persistent Homology, INRIA Saclay, Paris, March 2014.
  • (slides) Statistical Inference for Persistence Diagrams, Topological Data Analysis Workshop, SAMSI, Research Triangle Park (NC), Feb 2014.
  • (slides) Statistical Inference for Persistent Homology, Department of Mathematics, University of Torino, Italy, Dec 2013.
  • (slides) Trajectories to Dementia and Mixed Membership Models, National Council on Measurement in Education (NCME), San Francisco, April 2013.

POSTERS:
  • (pdf) Statistical Inference For Persistent Homology, IMA Workshop “Topological Data Analysis”, Minneapolis, 2013
  • (pdf) Trajectories to Dementia Identified with Mixed Membership Models, Joint Statistical Meetings, Montreal, 2013
  • (pdf) Fused Lasso to Determine the Risk Factors for Dementia, Joint Statistical Meetings, Montreal, 2013