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


  • R. Liu, M. Azabou, M. Dabagia, J. Xiao, E.L. Dyer: Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers, arXiv:2206.06131 (Preprint)
  • J. Quesada, L. Sathidevi, R. Liu, N. Ahad, J.M. Jackson, M. Azabou, J. Xiao, C. Liding, C. Urzay,  W. Gray-Roncal, E.C. Johnson+, E.L. Dyer+: MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction, 2022 (Page)
  • D. Parks+, A. Schneider+, Y. Xu, S. Funderburk, D. Thurber, T. Blanche, E.L. Dyer, D. Haussler, K.B. Hengen: Circuit-specific flickering of sleep and wake predicts natural behaviors, 2022
  • N. Ahad, E.L. Dyer, K.B. Hengen, Y. Xie, M.A. Davenport: Learning Sinkhorn divergences for supervised change point detection, arXiv:2202.04000, 2022 (Preprint)
  • G.O. BozdagS.A. Zamani-DahajP.C. KahnT.C. DayK. TongA.H. BalwaniE.L. Dyer, P.J. Yunker+, W.C. Ratcliff+: De novo evolution of macroscopic multicellularity, 2021 (Preprint)


  • M. Azabou, M. Mendelson, M. Sorokin, S. Thakoor, N. Ahad, C. Urzay, E.L. Dyer, Learning Behavior Representations Through Multi-Timescale Bootstrapping, Conference on Vision and Pattern Recognition (CVPR) Workshop on Multi-Agent Behavior, 2022 (Paper, Poster) [Oral] 
  • J.M. Jackson+, R. Liu+, E.L. Dyer: Building representations of different brain areas through hierarchical point cloud networks, Medical Imaging with Deep Learning (MIDL) (Paper)
  • S. Thakoor, C. Tallec, M. Gheshlaghi Azar, M. Azabou, E.L. Dyer, R. Munos, P. Velickovic, M. Valko, Bootstrapped Representation Learning on Graphs, International Conference Learning Representations (ICLR), 2022 (Paper, 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, Neural Information Processing Systems (NeurIPS) Workshop on Self-supervised Learning: Theory and Practice (Oral), Feb 2021 (Preprint, Code) [Oral]
  • 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, Neural Information Processing Systems (NeurIPS) Workshop on Self-supervised Learning: Theory and Practice, Dec 2021 (Paper)
  • 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, Neural Information Processing Systems (NeurIPS), accepted for Oral (1% submissions), 2021  (Paper, Code) [Oral]
  • F. Pei+, J. Ye+, D. Zoltowski, A. Wu, R.H. Chowdhury, H. Sohn, J.E. O’Doherty, K.V. Shenoy, M.T. Kaufman, M. Churchland, M. Jazayeri, L.E. Miller, J.Pillow, I.M.Park, E.L. Dyer, C. Pandarinath, Neural Latents Benchmark ’21: Evaluating latent variable models of neural population activity, Neural Information Processing Systems (NeurIPS), Benchmark and Datasets Track, 2021 (Paper, Page)
  • 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 Conference 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, bioRxiv)
  • 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)
  • 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, 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, Code)
  • D. Rolnick, E.L. Dyer, Generative models and abstractions for large-scale neuroanatomy datasets, Current Opinion in Neurobiology, February 2019. (Paper)
  • 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
[ Github – NerDSlab
MTNeuro Benchmark: Predicting attributes of brain structure across multiple levels of abstraction
Swap-VAE: A self-supervised approach for disentangling neural activity 
MYOW-Neuro: MYOW for Neural Data
Mine Your Own vieW (MYOW): Self-supervised learning through across-sample prediction
Bootstrapped Graph Representation Learning (BGRL)
Latent OT (LOT)
X-ray MicroCT Dataset