In the Pipeline…

  • J. Lee, M. Dabagia, E.L. Dyer, C. Rozell, Hierarchical Optimal Transport for Multimodal Distribution Alignment, arXiv:1906.11768, 2019. (Preprint)
  • E.C. JohnsonM. WiltL.M. RodriguezR.TenazasC. RiveraN. DrenkowD. Kleissas,T.J. LaGrowH. CowleyJ. DownsJ. MatelskyM. Hughes, E. ReillyB. WesterE.L. DyerK.P. KordingW. Gray Roncal, Toward a Reproducible, Scalable, Framework for Processing Large Neuroimaging Datasets, bioarXiv, 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, bioarXiv, p.445742, 2018 (Preprint)

Journal Papers

  • D. Rolnick, E.L. Dyer, Generative models and abstractions for large-scale neuroanatomy datasets, Current Opinion in Neurobiology, February 2019. (Paper, Current Opinion Article)
  • T. J. Lee, A. Kumar, A.H. 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)
  • 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 brain 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)
  • 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)

Conference Papers

  • J. Lee, E.L. Dyer, C. Rozell, Cluster-based Optimal Transport Alignment, Signal Processing with Adaptive Sparse Structured Representations (SPARS), Toulouse, France, July 1-4, 2019.
  • 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)
  • 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, 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)

Abstracts

  • 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 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
  • J. Krzyston, S.Y. Lee, E.M. Buckley & E.L. Dyer, Learning Biomarkers of Disease from Non-Invasive Measurements of Cerebral Blood Flow, Optical Society of America’s Optics and the Brain Meeting, April 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
  • A. Bleckert, A. Bodor, J. Borseth, D. Brittain, D. Bumbarger, D. Castelli, E.L. Dyer, T. Keenan, Y. Li, F. Long, J. Perkins, D. Reid, D. Sullivan, M. Takeno, R. Torres, D. Williams, C. Reid, N. da Costa, Linking functional and anatomical circuit connectivity using fast parallelized TEM imaging,
    Society for Neuroscience Annual Meeting (SFN), November 2016
  • R. Vescovi, E. Miqueles, D. Gursoy, V. De Andrade, E.L. Dyer, K. Körding, M. Cardoso, F. De Carlo, C. Jacobsen, N. Kasthuri. TOMOSAIC: Towards Terabyte Tomography, X-ray microscopy (XRM), August 2016
  • E.L. Dyer, H.L. Fernandes, X. Xiao, W. Gray Roncal, J.T. Vogelstein, C. Jacobsen, K.P. Körding and N. Kasthuri, Quantifying mesoscale neuroanatomy using X-ray microtomography, presented at the Society for Neuroscience (SFN) Annual Meeting (Oct ’15) and the Annual Statistical Analysis of Neural Data (SAND) Meeting (May ’15) (Abstract)
  • E.L. Dyer, T.A. Goldstein, R. Patel, and R.G. Baraniuk, Sparse Self-Expressive Decompositions for Dimensionality Reduction and Clustering, Signal Processing with Adaptive Sparse Structured Representations (SPARS), July 2015 (Abstract)
  • D.B. Murphy, J. Dapello, E.L. Dyer, R.G. Baraniuk, and J.T Robinson, Compressive neural circuit reconstruction using patterned optical stimulation, Society for Neuroscience (SFN) Annual Meeting, 2013
  • E.L. Dyer, C. Studer, and R.G Baraniuk, Subspace clustering with dense representations, Signal Processing with Adaptive Sparse Structured Representations (SPARS) 2013 Proceedings, Lausanne, Switzerland, 2013
  • E.L. Dyer, U. Rutishauser, and R.G Baraniuk, Group sparse coding with collections of winner-take-all (WTA) circuits, Organization for Computational Neurosciences (OCNS), BMC Neuroscience, 2012
  • E.L. Dyer, A.C. Sankaranarayanan, and R.G. Baraniuk, Learning hybrid linear models via sparse recovery, Signal Processing with Adaptive Sparse Structured Representations (SPARS) 2011 Proceedings
  • E.L. Dyer, D.H. Johnson, and R.G. Baraniuk, Learning modular representations from global sparse coding networks, Organization for Computational Neurosciences (OCNS), BMC Neuroscience 2010, 11(1): P131
  • E.L. Dyer, D.H. Johnson, and R.G. Baraniuk, Sparse coding in modular networks, Computational and systems neuroscience (COSYNE), 2010
  • E.L. Dyer, D.H. Johnson, and R.G Baraniuk, Sparse coding with population sketches, Organization for Computational Neurosciences (OCNS), BMC Neuroscience 2009, 10(1):P132

Other

  • 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)

Theses

  • 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.