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    1. Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model. Meng, R., Bouchard, K.E., PLoS Computational Biology, April, 2024.
    2.  BigNeuron: A resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets. ….; Bouchard, K.E., …; Nature Methods, June, 2023
    3.  DL-TODA: A Deep Learning Tool for Omics Data Analysis, Cecile M. Cres, Andrew Tritt, Kristofer E. Bouchard, Ying Zhang, Biomolecules; March, 2023 
    4. Perspectives for self-driving labs in synthetic biology. Garcia-Martin, H., Radivojevic, T., Zucker, J., Bouchard, K.E., et al., Current Opinion in Biotechnology, Feb., 2023.
    5. The Neurodata Without Borders ecosystem for neurophysiological data science. Rubel, O., ….., Bouchard, K.E. eLife
    6. Ladd, A., Balewski, J., Kim, K.G., Bouchard, K.E., Ben-Shalom, R., Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models. Ladd, A., Balewski, J., Kim, K.G., Bouchard, K.E., Ben-Shalom, R., Frontiers in Neuroinformatics. Jun., 2022.
    7. NeuroGPU: Accelerating multi-compartment, biophysically detailed neuron simulations on GPUs, Ben-Shalom, R., …, Bouchard, K.E., Bender, K., J. Neurosci. Methods, Jan., 2022. 
    8. Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Networks. Meng, R., Lee, H., Bouchard, K.E.; Conference of the International Federation of Classification Societies, 2022.
    9. Numerical Characterization of Support Recovery in Sparse Regression with Correlated Design. Kumar, A., Bouchard, K.E.; Communications in Statistics-Simulation and Computation, 2022.
    10. Hangul Fonts Dataset: a Hierarchical and Compositional Dataset for Investigating Learned Representations. Livezey, J Livezey, J.A., Hwang, A., Yeung, J., Bouchard, K.E.; International Conference on Image Analysis and Processing, 2021
    11. Achieving Sparsity in Bayesian Vector Autoregressions with Three-Parameter-Beta-Normal Prior. Meng, R., Rangarajan, H., Bouchard, K.E., Seminar on Bayesian Inference in Econometrics and Statistics, 2021
    12. Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses. Frye, C.G., Simon, J., Wadia, N.S., Ligeralde, A., DeWeese, M.R., Bouchard, K.E., Neural Computation, 2021
    13. Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models. Ruiz, T., Bhattacharyya, S., Balasubramanian, M., Bouchard, K.E., Learning for Dynamics and Control, 2020.
    14. Scaling of Union of Intersections for Inference of Granger Causal Networks from Observational Data. Balasubramanian, M, Ruiz, T.D., Cook, B., Prabhat, Bhattacharyya, S., Shrivastava, A., Bouchard, K.E.; International Parallel and Distributed Processing Symposium, 2020
    15. HDMF: Hierarchical Data Modeling Framework for Modern Science Data Standards. Tritt, A., Rübel, O., Dichter, B., Ly, R., Chang, E., Kang, D., Frank, L., Bouchard, K.E.; IEEE Big Data, 2019.
    16. PyUoI: The Union of Intersections Framework in Python. Journal of Open Source Software, Sachdeva, P., Livezey, J., Tritt, A.J., Bouchard, K.E. 4(44), 1799, 2019
    17. Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis. Livezey, J, Clark, D.G., Bouchard, K.E.; NeurIPS 2019.
    18. Deep-learning as a data analysis tool for systems neuroscience. Livezey, J.*, Bouchard, K.E.*$, Chang, E.F.$; PLoS Computational Biology, 15(9): e1007091. Sept, 2019. *: co-first authors; $: co-senior authors
    19. Sparse, Predictive, and Interpretable Functional Connectomics with UoI-Lasso. P.S. Sachdeva, S. Bhattacharyya, Bouchard, K.E.; 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2019).
    20. International Neuroscience Initiatives through the Lens of High-Performance Computing. Bouchard, K.E.*, et al., IEEE Computer, 51(4):50-59;
    21. Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction. Bouchard, K.E., Bujan, A.F., Roosta, F., Prabhat, Snijders, A., Mao, J-H., Chang, E.F., Mahoney, M., Bhattacharyya, S.; Advances in Neural Information Processing Systems, 2017. Available on-line.
    22. UoI-NMFcluster: A Robust Non-negative Matrix Factorization Algorithm for Improved Parts-Based Decompositions from Noisy Data. Ubaru, S., Wu, K.J., Saad, Y., Bouchard, K.E.; 16th IEEE International Conference on Machine Learning and Applications. DOI: 1109/ICMLA.2017.0-      152; Best Paper Award. 2017.
    23. Sparse coding of ECoG signals identifies interpretable components for speech control in human sensorimotor cortex. Bouchard, K.E., Bujan, A.F., Chang, E.F., Sommer, F.T.; IEEE, Engineering in Medicine and Biology, 3636-3639; Aug., 2017.
    24. Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions. Murugesan, S., Bouchard, K.E., Chang, E., Dougherty, M., Hamann, B., & Weber, G. H. (2017). BioMedical Central Bioinformatics, 18(6) 1- 45; 2017.
    25. Neuromorphic Kalman filter implementation in IBM’s TrueNorth. Carney, R., Livezey, J., Clark, D., Calafiura, P., Donofrio, D., Bouchard, K.E., & Garcia- Sciveres, M. (2017). In  Phys. Conf. Ser.(Vol. 898, p. 042021).
    26. High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination. Bouchard, K.E., et al., Neuron, 92(3):628-631; 2016.
    27. Usage Pattern-Driven Dynamic Data Layout Reorganization. Tang, H., Byna, S., Harenberg, S., Zou, X., Zhang, W., Wu, K., Dong, B., Rubel, O., Bouchard, K.E., Klasky, S., and Samatova, N.F.; IEEE/ACM Cluster, Cloud, and Grid Computing, DOI:1109/CCGrid.2016.15; 2016.
    28. Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity. Mururgesan, S.,Bouchard, K.E., Brown, J.A., Hamann, B., Seeley, W.W., Trujillo, A., Weber, G. H.; IEEE/ACM Transactions on Computational Biology and Bioinformatics, (99): 805- 818; May, 2016.
    29. Methods for Specifying Scientific Data Standards and Modeling Relationships with Applications to Neuroscience. Rübel, O., Dougherty, M., Prabhat, Denes, P., Conant, D., Chang, E.F., Bouchard, K.E.; Front Neuroinform. 10: 48; , 2016.
    30. Hierarchical spatio-temporal visual analysis of cluster evolution in electrocorticography data. Murugesan, S., Bouchard, K. E., Chang, E. F., Dougherty, M., Hamann, B. and Weber, G. H.; Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 630-639; Oct., 2016.

     

    ArXiv only

    Provably convergent acceleration in factored gradient descent with applications in matrix sensing. Ajayi, T., Mildebrath, D., Kyrillidis, A., Ubaru, S., Kollias, G., Bouchard, K.E., arXiv:1806.00534

    NWB:N 2.0: An Accessible Data Standard for Neurophysiology. Rübel,O., Tritt., A.J., …., Bouchard, K.E. https://www.biorxiv.org/content/10.1101/523035v1

    Spiking Linear Dynamical Systems on Neuromorphic Hardware for Low-Power Brain-Machine Interfaces. Clark, D. G., Livezey, J. A., Chang, E. F., & Bouchard, K.E. arXiv:1805.08889. 2018

    Modeling neural activity at the ensemble level. Rapela, J., Kostuk, M., Rowat, P., Mullen, T., Chang, E.F., Bouchard, K.E.; arXiv.qbio.NC, May, 2015.

    Bootstrapped Adaptive Threshold Selection for Statistical Model Selection and Estimation. Bouchard, K.E.; arXiv.stat.ML; April, 2015.

    BRAINformat: A Data Standardization Framework for Neuroscience Data. Rübel, O., Prabhat, Denes, P., Conant, D., Chang, E.F., Bouchard, K.E.; biorxiv, Aug., 2015.