In the Pipeline…

  • T.J. LaGrow, M. Moore, J.A. Prasad, A. Webber , M.A. Davenport, E.L. Dyer, Cytoarchitecture and Layer Estimation in High-Resolution Neuroanatomical Images, in review, July 2018.
  • D. Rolnick, E.L. Dyer, Generative models and abstractions for large-scale neuroanatomy datasets, in review, July 2018.

Journal Papers

  • Timothy J. Lee, Aditi Kumar, Aishwarya H. Balwani, Derrick Brittain, Sam Kinn, Craig A. Tovey, Eva L. Dyer, Nuno M. da Costa, R. Clay Reid, Craig R. Forest, Daniel J. Bumbarger, Large-scale neuroanatomy using LASSO: Loop-based Automated Serial Sectioning Operation, Plos One (in press), October 2018.
  • 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, to appear in the Journal of Neuroscience, October 2018. (Preprint)

  • 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

  • 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)

Peer-reviewed Abstracts

  • 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. (Paper)
  • 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. (Paper, Code)

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.