Assistant Professor of Statistical Science
Duke University
Email: jason.q.xu at duke dot edu
© 2021 Jason Xu · All rights reserved ·
Most of these are available on arXiv. Corresponding software is available at the bottom of this page.
For a broad description of current interests, see my research page.
Bu, F., Aiello, A., Volfovsky, A.* and Xu,J.*. Likelihood-based Inference for partially observed stochastic epidemics with individual heterogeneity.
Agarwal, M. and Xu, J. Quasi-Newton Acceleration of EM and MM Algorithms via Broyden’s Method with Extrapolation.
Won, J., Lange, K. and Xu, J. A Unified Analysis of Convex and Non-convex l_{p}-ball Projection Problems.
Stutz, T., Sehl, M., Sinsheimer, J. and Xu, J. Computational Tools for Assessing Gene Therapy under Branching Process Models of Mutation.
Chakraborty, S. and Xu, J. Biconvex Clustering.
He, M., Lu, D., Xu, J.* and Xavier, R*. Community Detection in Weighted Multilayer Networks with Ambient Noise.
Xu, J. and Lange, K. A Proximal Distance Algorithm for Likelihood-based Sparse Covariance Estimation.
Paul, D.*, Chakraborty, S.*, Das, S. and Xu, J. Implicit Annealing in Kernel and Multi-kernel Spaces: A Strongly Consistent Clustering Approach.
Gustafson, A., Hirn, M., Mohammed, K., Narayanan, H. and Xu, J. Structural Risk Minimization for C^{1,1}(R^{d}) Regression.
(* denotes equal contribution)
Paul, D.*, Chakraborty, S.*, Das, S. and Xu, J (2021). Uniform Concentration Bounds toward a Unified Framework for Robust Clustering. To appear, Neural Information Processing Systems (NeurIPS). (Spotlight Paper)
Landeros, A., Ji, X., Lange, K. L., Stutz, T., Xu, J., Sehl, M. E.*, and Sinsheimer, J.S* (2021). An Examination of School Reopening Strategies during the SARS-CoV-2 Pandemic. PLOS One.
Ciccone, E.J., et al. (2021). SARS-CoV-2 Infection in Healthcare Personnel and Their Household Contacts at a Tertiary Academic Medical Center: Protocol for a Longitudinal Cohort Study. JMIR Research Protocols.
Zhang, Z., Lange, K. and Xu, J. (2020). Simple and Scalable Sparse k-Means Clustering via Feature Ranking. Neural Information Processing Systems (NeurIPS). (Spotlight Paper)
Stutz, T., Landeros, A., Xu, J., Sinsheimer, J. S., Lange, K. and Sehl, M. E (2021). Stochastic Simulation Algorithms for Interacting Particle Systems. PLOS One.
Bu, F., Aiello, A., Xu, J.* and Volfovsky, A* (2020). Likelihood-based Inference for Partially Observed Epidemics on Dynamic Networks. Journal of the American Statistical Association. (SBSS Student Paper Award)
Chakraborty, S.*, Paul, D.*, Das, S. and Xu, J. (2020). Entropy Weighted Power k-Means Clustering. Artificial Intelligence and Statistics (AISTATS).
Xu, J., Koelle, S., Wu, C., Guttorp, P., Dunbar, C. E., Abkowitz, J. L. and Minin, V. N. (2019). Statistical Inference for Partially Observed Branching Processes, with Application to Cell Lineage Tracking of in vivo Hematopoiesis. The Annals of Applied Statistics.
Xu, J. and Lange, K (2019). Power k-Means Clustering. International Conference on Machine Learning (ICML). (Long Oral)
Won, J., Xu, J and Lange, K. (2019). Projection onto Minkowski Sums with Application to Constrained Learning. International Conference on Machine Learning (ICML). (Long Oral)
Xu, J., Wang, Y., Guttorp, P. and Abkowitz, J. L. (2018). Visualizing Hematopoiesis as a Stochastic Process. Blood.
Ho, L., Xu, J., Crawford, F. W., Minin, V. N. and Suchard, M. A. (2018). Birth/birth-death Processes and their Computable Transition Probabilities with Biological Applications. Journal of Mathematical Biology.
Xu, J., Chi, E., Yang, M. and Lange, K. (2018). A Majorization-minimization Algorithm for Split Feasibility Problems. Computational Optimization and Applications.
Xu, J., Chi, E. and Lange, K. (2018). Generalized Linear Model Regression under Distance-to-set Penalties. Neural Information Processing Systems (NeurIPS). (Spotlight Paper)
Letcher, A.*, Trišović, J.*, Cademartori, C., Chen, X. and Xu, J. (2018). Automatic Conflict Detection in Police Body-Worn Audio. Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (MAA Outstanding Student Research Award)
Hardin, W., Li, R., Xu, J., Shelton, A., Alas G., Minin, V. N. and Paredez, A. R. (2017). Myosin-independent cytokinesis in Giardia Utilizes Flagella to Coordinate Force Generation and Direct Membrane Trafficking. Proceedings of the National Academy of Sciences.
Koelle, S., Wu, C., Xu, J., Lu, R., Li, B., Donahue, R. E. and Dunbar, C. E. (2017). Quantitative Stability of Hematopoietic Stem and Progenitor Cell Clonal Output in Rhesus Macaques Receiving Transplants. Blood.
Xu, J., Koelle, S., Wu, C., Guttorp, P., Dunbar, C. E., Abkowitz, J. L. and Minin, V. N. (2016). Stochastic Modeling of Hematopoietic Stem and Progenitor Cell Barcoding Data from Rhesus Macaques Challenges the Classic Model of Hematopoiesis. Blood.
Xu, J., Guttorp, P., Kato-Maeda, M. and Minin, V. N. (2015). Likelihood-based Inference for Discretely Observed Birth-death-shift Processes, with Applications to Evolution of Mobile Genetic Elements. Biometrics. (David P. Byar Paper Competition Award)
Xu, J. and Minin, V. N. (2015). Efficient Transition Probability Computation for Continuous-time Branching Processes via Compressed Sensing. Uncertainty in Artificial Intelligence (UAI).
Foti, N.*, Xu, J.*, Laird, D. and Fox, E. B. (2015). Stochastic Variational Inference for Hidden Markov Models. Neural Information Processing Systems (NeurIPS).
Huber, M. L., McCall, E., Rozenfeld, D. and Xu, J. (2012). Bounds on the Artificial Phase Transition for Perfect Simulation of Repulsive Point Processes. Involve.
QNMM: R package for quasi-Newton acceleration of MM and EM algorithms
SKFR: R and Julia code for sparse k-means with feature ranking
CoEpiNet: R code for inference for partially observed epidemics over dynamic contact networks
EWP: R code for power k-means clustering with entropy-based feature weighing
MinkowskiProjection: Matlab code for projection onto Minkowski sums of sets
splitFeas: R package, MM algorithms for multi-set split feasibility problems
branchCorr: R package, M-estimation for partially observed stochastic compartmental models
bdsem: R package, MLE and EM inference for discretely observed multi-type branching processes
pysvihmm: Python implementation of stochastic variational inference for hidden Markov models
multiBD: R package, likelihood inference for partially observed multivariate birth-death processes
hematopoiesisSimulator: Java GUI for simulating and visualizing stochastic models of hematopoiesis
Please email me for any code not yet released in a completed package.