• K. Shadi, E.L. Dyer, C. Dovrolis, Multi-sensory integration in the mouse cortical connectome using a network diffusion model, biorXiv, p.832485, 2019 (Preprint)
  • E.C. Johnson, M. Wilt, L.M. Rodriguez, R.Tenazas, C. Rivera, N. Drenkow, D. Kleissas, T.J. LaGrow, H. Cowley, J. Downs, J. Matelsky, M. Hughes, E. Reilly, B. Wester, E.L. Dyer, K.P. Kording, W. Gray Roncal, Toward a Reproducible, Scalable, Framework for Processing Large Neuroimaging Datasets, biorXiv, p.615161, 2019 (Preprint)
  • T.J. LaGrow, M. Moore, J.A. Prasad, A. Webber , M.A. Davenport, E.L. Dyer, Sparse Recovery Methods for Estimating Cytoarchitectonic Divisions, biorXiv, p.445742, 2018 (Preprint)


* denotes equal contribution


  • A. Balwani, E.L. Dyer, Modeling variability in brain architecture with deep feature learning, to appear in IEEE Asilomar Conference on Signals, Systems, and Computers, December 2019.
  • K. Milligan, A. Balwani, E.L. Dyer, Brain Mapping at High Resolutions: Challenges and Opportunities, in press, Current Opinion in Biomedical Engineering. (Paper)
  • M. Dabagia, E.L. Dyer, Barycenters in the Brain: An optimal transport approach to modeling connectivity, Optimal Transport for Machine Learning (OTML) Workshop at Neural Information Processing Systems (NeurIPS), Dec 2019.
  • J. Lee, M. Dabagia, E.L. Dyer*, C. Rozell*, Hierarchical Optimal Transport for Multimodal Distribution Alignment, to appear at Neural Information Processing Systems (NeurIPS), Dec 2019.
    (Paper, Python Code, Matlab Code)
  • D. Rolnick, E.L. Dyer, Generative models and abstractions for large-scale neuroanatomy datasets, Current Opinion in Neurobiology, February 2019. (Paper)
  • T. J. Lee, A. Kumar, A. Balwani, D. Brittain, S. Kinn, C. A. Tovey, E. L. Dyer, N.M. da Costa, R.C. Reid, C.R. Forest, D.J. Bumbarger, Large-scale neuroanatomy using LASSO: Loop-based Automated Serial Sectioning Operation, Plos One, October 2018. (Paper)
  • C. Pandarinath, K.C. Ames, A.A. Russo, A. Farshchian, L.E. Miller, E.L. Dyer, J.C. Kao, Latent factors and dynamics in motor cortex and their application to brain-machine interfaces, . (Paper)
  • T.J. LaGrow, M. Moore, J.A. Prasad, M.A. Davenport, E.L. Dyer, Approximating Cellular Densities from High-Resolution Neuroanatomical Imaging Data, IEEE Engineering in Medicine and Biology Society Annual Conference (EMBC), July 2018. (Paper)
  • X. Yang, V. De Andrade, W. Scullin, E. L. Dyer, F. De Carlo, N. Kasthuri, Doga Gürsoy, Enhancing low-dose X-ray tomography through a deep convolutional neural network, Nature Scientific Reports 8, 2018. (Paper)
  • E.L. Dyer, M. Azar, H.L. Fernandes, M. Perich, L.E. Miller, and K.P. Körding, A cryptography-based approach to movement decoding, Nature Biomedical Engineering, 1(12), 967. 2017. (Paper, Code)
  • E.L. Dyer, W.G. Roncal, J.A. Prasad, H.L. Fernandes, D. Gürsoy, V. De Andrade, K. Fezzaa, X. Xiao, J.T. Vogelstein, C. Jacobsen, K.P. Körding & N. Kasthuri, Quantifying mesoscale neuroanatomy using X-ray microtomography, . (Paper, Code, Data)
  • A. Mirhoseini, E.L. Dyer, E. Songhori, R.G. Baraniuk, and F. Koushanfar, RankMap: A platform-aware framework for distributed learning from dense datasets, IEEE Transactions on Neural Networks and Learning Systems, 2017. (Paper, Code)

Before 2017

  • M. Azar, E.L. Dyer, and K.P. Körding, Convex relaxation regression: Black-Box optimization of smooth functions by learning their convex envelopes, Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), June 2016. (Paper, Poster, Slides)
  • R.J. Patel, T.A. Goldstein, E.L. Dyer, A. Mirhoseini, and R.G. Baraniuk, Deterministic column sampling for low rank approximation: Nyström vs. Incomplete Cholesky Decomposition, SIAM Data Mining (SDM) Conference, May 2016. (Paper, Code)
  • E.L. Dyer, A.C. Sankaranarayanan, and R.G. Baraniuk, Greedy feature selection for subspace clustering, The Journal of Machine Learning Research 14 (1), 2487-2517, September 2013. (Paper)
  • E.L. Dyer, C. Studer, J.T. Robinson, and R.G Baraniuk, A robust and efficient method to recover neural events from noisy and corrupted data, IEEE EMBS Neural Engineering (NER) Conference, 2013. (Paper, Code)
  • E.L. Dyer, C. Studer, and R.G Baraniuk, Subspace clustering with dense representations, IEEE International Conf. on Signal Processing (ICASSP) 2013 Proceedings, Vancouver, BC, 2013. (Paper)
  • E.L. Dyer, M. Majzoobi, F. Koushanfar, Hybrid modeling of non-stationary process variations, IEEE/ACM Design and Automation Conference (DAC) 2011 Proceedings, San Diego, CA, 2011. (Paper)
  • M. Majzoobi, E.L. Dyer, A. Enably, and F. Koushanfar, Rapid FPGA characterization using clock synthesis and signal sparsity, IEEE International Test Conference (ITC) 2010 Proceedings, Austin, TX, November 2010. (Paper)
  • E.L. Dyer, M.F. Duarte, D.H. Johnson, and R.G. Baraniuk, Recovering spikes from noisy neuronal calcium signals via structured sparse approximation, Lecture Notes in Computer Science, Independent Components Analysis (ICA) 2010, Volume 6365/2010, 604-611. (Paper)
  • G. Fischer, E.L. Dyer, C. Csoma, A. Deguet, and G. Fichtinger, Validation system for MR image overlay and other needle insertion techniques, Medicine Meets Virtual Reality 15- in vivo, in vitro, in silico: Designing the Next in Medicine, IOS Press, 2007. (Paper)



  • A. Balwani, J. Miano, J.A. Prasad, E.L. Dyer: Learning to Segment at Multiple Scales: From Brain Areas to Microstructure, poster at Bioimage Informatics Conference, Seattle, WA, October 2019.
  • K. Milligan, A. Balwani, A. Maguire, S. Margulies, E.L. Dyer: Deep learning for characterization of neuroinflammation in traumatic brain injury, poster at Bioimage Informatics Conference, Seattle, WA October 2019.
  • M. Dabagia, E.L. Dyer: Learning shape primitives and generative models for mesoscale projectomes, poster at Bioimage Informatics Conference, Seattle, WA, October 2019.
  • J. Lee, E.L. Dyer, C. Rozell, Cluster-based Optimal Transport Alignment, oral presentation at Signal Processing with Adaptive Sparse Structured Representations (SPARS), Toulouse, France, July 1-4, 2019
  • Kamal Shadi, EL Dyer, M Mohajerani, C Dovrolis, Multi-sensory integration in the mouse mesoscale connectome using a network diffusion model, oral presentation at Network Neuroscience, 2019
  • Kamal Shadi, EL Dyer, C Dovrolis,  Multi-sensory integration in the mouse mesoscale connectome using a network diffusion model, Cosyne, Lisbon, Portugal, 2019
  • TJ Lee, EL Dyer, and CR Forest, Fully automated serial sectioning using robotic and capillary interaction-based tools, Max Planck/HHMI Connectomics Meeting, Berlin, April 14-17, 2019


  • E. Johnson, …, E.L. Dyer, and W. Gray Roncal, A framework for pipeline optimization and deployment for large neuroscience datasets, Society for Neuroscience (SFN) Annual Meeting, November 2018
  • M.D. Ritch, B.G. Hannon, A.T. Read, E.L. Dyer, J. Reynaud, G.A. Cull, C.F. Burgoyne, and C.R. Ethier, Computer-assisted quantification of glaucoma-induced axonal damage in rat optic nerves, Biomedical Engineering Society (BMES) Annual Meeting, October 2018
  • M. Tondravi, W. Scullin, M. Du, R. Vescovi, V. De Andrade, C. Jacobsen, K.P. Körding, D. Gursoy, E.L. Dyer, A pipeline for distributed segmentation of teravoxel tomography datasets, International X-ray Microscopy (XRM) Conference, 2018
  • M. Du, R. Vescovi, R. Chard, N. Kasthuri, C. Jacobsen, E.L. Dyer, D. Gürsoy, An automated pipeline for the collection, transfer, and processing of large-scale tomography data, Optical Society of America’s Optics and the Brain Meeting, April 2018


  • W.G. Roncal, E.L. Dyer, D. Gürsoy, K.P. Körding, N. Kasthuri: From sample to knowledge: Towards an integrated approach for neuroscience discovery, arXiv:1604.03199 [q-bio.QM], 2016.
  • E.L. Dyer, T.A. Goldstein, R.J. Patel, K.P. Körding, and R.G. Baraniuk: Sparse self-expressive decompositions for matrix approximation and clustering, arXiv:1505.00824 [cs.IT], 2015. (PaperCode)


  • E.L. Dyer, New Theory and Methods for Signals in Unions of Subspaces, Ph.D. Thesis, Dept. of Electrical and Computer Engineering, Rice University, September, 2014.
  • E.L. Dyer, Endogenous Sparse Recovery, M.S. Thesis, Dept. of Electrical and Computer Engineering, Rice University, October, 2011.