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

  • G. Ozan BozdagSeyed Alireza Zamani-DahajPenelope C. KahnThomas C. DayKai TongAishwarya H. BalwaniEva L. Dyer, Peter J. YunkerWilliam C. Ratcliff, De novo evolution of macroscopic multicellularity, Sept 2021 (Preprint)

  • S. Thakoor, C. Tallec, M. Gheshlaghi Azar, M. Azabou, E.L. Dyer, R. Munos, P. Velickovic, M. Valko, Bootstrapped Representation Learning on Graphs, May 2021 (Preprint, Code)


  • M. Dabagia, K. Kording, E.L. Dyer, Comparing high-dimensional neural recordings by aligning their low-dimensional latent representations, to appear in Nature Biomedical Engineering, 2022 (Preprint)


  • 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, presented at NeurIPS Workshop on Self-supervised Learning: Theory and Practice, Feb 2021 (Preprint, Code)
  • M. Azabou+, M. Dabagia+, R. Liu+, C-H. Lin, K.B. Hengen, E.L. Dyer, Using self-supervision and augmentations to build insights into neural coding, NeurIPS Workshop on Self-supervised Learning: Theory and Practice, Dec 2021
  • R. Liu, M. Azabou, M. Dabagia, C-H. Lin, M. Gheshlaghi Azar, K.B. Hengen, M. Valko, E.L. Dyer, Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity, NeurIPS, Oral Presentation, 2021  (Paper, Code)
  • Felix Pei+, Joel Ye+, David Zoltowski, Anqi Wu, Raeed H. Chowdhury, Hansem Sohn, Joseph E. O’Doherty, Krishna V. Shenoy, Matthew T. Kaufman, Mark Churchland, Mehrdad Jazayeri, Lee E. Miller, Jonathan Pillow, Il Memming Park, Eva L. Dyer, Chethan Pandarinath, Neural Latents Benchmark ’21: Evaluating latent variable models of neural population activity, NeurIPS Benchmark and Datasets Track, 2021 (Paper)
  • A. Balwani+J. Miano+, R. Liu, L. Kitchell, J.A. Prasad, E.C. Johnson, W. Gray-Roncal, E.L. Dyer, Multi-scale modeling of neural structure in X-ray imagery, IEEE International Conf. on Image Processing (ICIP), 2021 (Paper)
  • C-H. Lin, M. Azabou, E.L. Dyer, Making transport more robust and interpretable by moving data through a small number of anchor points,  International Conference on Machine Learning (ICML), 2021 (Paper, ICML Poster, Code)
  • C-H. Lin, JD Miano, EL Dyer, Bayesian optimization for modular black-box systems with switching costs, Conference on Uncertainty in Artificial Intelligence (UAI), July 2021 (Paper)


  • 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)
  • M. Jas, T. Achakulvisut, A. Idrizović, D. Acuna, M. Antalek, V. Marques, T. Odland, R.P. Garg, M. Agrawal, Y. Umegaki, P. Foley, H. Fernandes, D. Harris, B. Li, O. Pieters, S. Otterson, G. De Toni, C. Rodgers, E.L. Dyer, M Hamalainen, K.P. Kording, P. Ramkumar. Pyglmnet: Python implementation of elastic-net regularized generalized linear models. J Open Source Software 5:47, 2020 (Paper, Code)
  • 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, under review at Plos Computational Biology, 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)


  • 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)
  • 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
[ LAB github – NERDSLAB
Swap-VAE: A self-supervised approach for disentangling neural activity 
Mine Your Own vieW (MYOW): Self-supervised learning through across-sample prediction
Bootstrapping Graph Representation Learning (BGRL)
Latent OT (LOT)
X-ray MicroCT Dataset