[Lab members are in bold, + denotes equal contribution ]


  • M. Azabou, M. Gheshlaghi Azar, R. Liu, C-H. Lin, E.C. Johnson, K. Bhaskharan-Nair, M. Dabagia, K.B. Hengen, W. Gray-Roncal, M. Valko, E.L. Dyer, Mine Your Own vieW: Self-supervised learning through across-sample prediction, Feb 2021 (Preprint, Code)


  • C-H. Lin, M. Azabou, E.L. Dyer, Making transport more robust and interpretable by moving data through a small number of anchor points, Dec 2020 (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, GigaScience, Volume 9, Issue 12, December 2020 (Paper)
  • R. Liu, C. Subakan, A. Balwani, J. Whitesell, J.A. Harris, S. Koyejo, E.L. Dyer, A generative modeling approach for interpreting population-level variability in brain structure, International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI), October 2020 (Paper, Preprint)
  • K. Shadi, E.L. Dyer, C. Dovrolis, Multi-sensory integration in the mouse cortical connectome using a network diffusion model, Network Neuroscience,  October 2020 (Paper, Preprint)
  • J.A. Prasad, A.H. Balwani, E.C. Johnson, J.D. Miano, V. Sampathkumar, V. de Andrade, K. Fezzaa, M. Du, R. Vescovi, C. Jacobsen, K.P. Kording, D. Gursoy, W. Gray Roncal, N. Kasthuri, E.L. Dyer, A three-dimensional thalamocortical dataset for characterizing brain heterogeneity, Nature Scientific Data, October 2020 (Preprint, X-ray Atlas)
  • R. Farhoodi, E.L. Dyer, J. Yarkony, S. Wang, K.P. Kording, Reconstructing the morphology of neurons from sparse noisy observations, submitted, September 2020
  • A. Balwani, E.L. Dyer, A deep feature learning approach for mapping the brain’s microarchitecture and organization, ICML Workshop on ML Interpretability for Scientific Discovery, July 2020 (Preprint, ICML Workshop Paper)
  • C-H Lin, JD Miano, EL Dyer, Bayesian optimization for modular black-box systems with switching costs, submitted, June 2020 (Preprint)
  • M. Dabagia, K. Kording, E.L. Dyer, Comparing high-dimensional neural recordings by aligning their low-dimensional latent representations, Nature Biomedical Engineering (revisions), May 2020 (Preprint)


  • A. Balwani, E.L. Dyer, Modeling variability in brain architecture with deep feature learning, IEEE Asilomar Conference on Signals, Systems, and Computers, Dec 2019 (Paper)
  • K. Milligan, A. Balwani, E.L. Dyer, Brain Mapping at High Resolutions: Challenges and Opportunities, Current Opinion in Biomedical Engineering, 2019 (Paper)
  • J. Lee, M. Dabagia, E.L. Dyer+, C. Rozell+, Hierarchical Optimal Transport for Multimodal Distribution Alignment, Neural Information Processing Systems (NeurIPS), Dec 2019. (+ equal contribution) (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. 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)
  • 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)

Selected Papers < 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, 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, 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)
Project Pages