In the pipeline…

  • X. Yang, V. De Andrade, F. De Carlo, E. L. Dyer, N. Kasthuri, Doga Gürsoy, Enhancing low-dose X-ray tomography through a deep convolutional neural network, in review, 2017.

Papers

  • E.L. Dyer, M. Azar, H.L. Fernandes, M. Perich, L.E. Miller, and K.P. Körding: A cryptography-based approach to brain decoding, to appear in Nature Biomedical Engineering, December 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 and N. Kasthuri, Quantifying mesoscale neuroanatomy using X-ray microtomography, to appear in eNeuro, 2017. (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)
  • 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)

Peer-reviewed Abstracts

  • 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.