Publications

Preprints

D. Lachi, M. Azabou, V. Arora, E.L. Dyer: GraphFM: A Scalable Framework for Multi-Graph Pretraining, arXiv, July 2024
R. Liu⁺, Wenrui Ma⁺, E. Zippi, H. Pouransari, J. Xiao, C. Sandino, B. Mahasseni, J. Minxha, E. Azemi, E.L. Dyer, A. Moin: Generalizable autoregressive modeling of time series through functional narratives, arXiv, Oct 2024 (+ co-first authors)

2025

Zhang Y, Wang Y, Azabou M, Andre A, Wang Z, Lyu H, Laboratory TI, Dyer E, Paninski L, Hurwitz C. Neural Encoding and Decoding at Scale. [ICML25]
N. Ahad, M.A. Davenport, E.L. Dyer: Time Series Domain Adaptation via Channel-Selective Representation Alignment, Transactions on Machine Learning Research, Jan 2025 [TMLR]
M. Azabou⁺, K. Xuejing Pan⁺, V. Arora, I.J. Knight, E.L. Dyer⁺, B.A. Richards⁺ Multi-session, multi-task neural decoding from distinct cell-types and brain regions (+ co-first, co-last authors) [ICLR25]
H. Yu, H. Lyu, E. YiXun Xu, C. Windolf, E. Kenji Lee, F. Yang, A.M. Shelton, S. Olsen, S. Minavi, O. Winter, International Brain Laboratory, E.L. Dyer, C. Chandrasekaran, N.A. Steinmetz, L. Paninski, C. Hurwitz: In vivo cell-type and brain region classification via multimodal contrastive learning [ICLR25]

2024

Z. Chen, C-H Lin, R. Liu, J. Xiao, E.L. Dyer: Your contrastive learning problem is secretly a distribution alignment problem, Advances in Neural Information Processing Systems, Dec 2024 [NeurIPS24]
Y. Zhang, Y. Wang, D. Jimenez-Beneto, Z. Wang, M. Azabou, B. Richards, O. Winter, The International Brain Laboratory, E.L. Dyer, L. Paninski, C. Hurwitz: Towards a “universal translator” for neural dynamics at single-cell, single-spike resolution, Advances in Neural Information Processing Systems, Dec 2024 [NeurIPS24]
G. Haspel, B. Baker, I. Beets, E.S. Boyden, J. Brown, G. Church, N. Cohen, D. Colon-Ramos, E.L. Dyer, C. Fang-Yen, S. Flavell, M.B. Goodman, A.C. Hart, E.J. Izquierdo, K. Kagias, S. Lockery, Y. Lu, A. Marblestone, J. Matelsky, B. Mensh, T.D. Pereira, H. Pfister, K. Rajan, H.G. Rotstein, M. Scholz, J.W. Shaevitz, E. Shlizerman, Q. Simeon, M.A. Skuhersky, V. Tiruvadi, V. Venkatachalam, D. Wei, B. Wester, G. Robert Yang, E. Yemini, M. Zimmer, K.P. Kording: The time is ripe to reverse engineer an entire nervous system: simulating behavior from neural interactions, arXiv, Sept 2024
G. Tolossa, A. Schneider, E.L. Dyer, K.B. Hengen: A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains, eLife, Nov 2024
C. Kaushik⁺, R. Liu⁺, C-H Lin, A. Khera, M.Y. Jin, W. Ma, V. Muthukumar, E.L. Dyer: Balanced data, imbalanced spectra: Unveiling class disparities with spectral imbalance, International Conference on Machine Learning, 2024 (⁺co-first authors) [ICML24]
J.N. McGregor, C.A. Farris, S. Ensley, A. Schneider, C. Wang, Y. Liu, J. Tu, H. Elmore, K.D. Ronayne, R. Wessel, E.L. Dyer, K. Bhaskaran-Nair, D.M. Holtzman, K.B. Hengen: Tauopathy severely disrupts homeostatic set-points in emergent neural dynamics but not the activity of individual neurons, Neuron, Sept 2023 [Neuron]
D.F. Parks⁺, A.M. Schneider⁺, Y. Xu, S.J. Brunwasser, S. Funderburk, D. Thurber, T. Blanche, E.L. Dyer, D. Haussler, K.B. Hengen: A non-oscillatory, millisecond-scale embedding of brain state provides insight into behavior, Nature Neuroscience, July 2024 [Nature Neuroscience]
Jingyun Xiao, Ran Liu, Eva L. Dyer: GAFormer: Enhancing time-series transformers through group-aware embeddings, International Conference on Learning Representations (ICLR), 2024 [ICLR24]
C-H Lin, C. Kaushik, E.L. Dyer⁺ & V. Muthukumar⁺: The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective, Journal of Machine Learning Research (JMLR), (+ co-last authors) [JMLR]
R. Liu, S. Khose, J. Xiao, L. Sathidevi, K. Ramnath, Z. Kira, E.L. Dyer: LatentDR: Improving model generalization through sample-aware latent degradation and restoration, IEEE Winter Conference on Applications of Computer Vision, 2024 [WACV24]

2023

E.L. Dyer, K.P. Kording: Why the simplest explanation isn't always the best, Proceedings of the National Academy of Sciences (PNAS), Dec 2023 [PNAS]
M. Azabou, V. Arora, V. Ganesh, X. Mao, S. Nachimuthu, M. Mendelson, B. Richards, M. Perich, G. Lajoie, E.L. Dyer: A unified, scalable framework for neural population decoding, NeurIPS 2023 [NeurIPS23] (Page)
M. Azabou, M. Mendelson, N. Ahad, M. Sorokin, S. Thakoor, C. Urzay, E.L. Dyer: Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis, Advances in Neural Information Processing Systsems 2023 (Spotlight, 3% acceptance) (Web) [NeurIPS23]
A. Ten Eyck, Y-C Chen, L. Gifford, D. Torres-Rivera, E.L. Dyer, G.B. Melikyan: Label-free imaging of nuclear membrane for analysis of nuclear import of viral complexes, Journal of Virological Methods, Volume 322, December 2023
Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer: Half-Hop: A graph upsampling approach for slowing down message passing, ICML 2023 [ICML23]
A. Schneider⁺, M. Azabou⁺, L. McDougall-Vigier, D. Parks, S. Ensley, K. Bhaskaran-Nair, T. Nowakowski, E.L. Dyer⁺ & K.B. Hengen⁺: Transcriptomic cell type structures in vivo neuronal activity across multiple timescales, Cell Reports, 2023 (Code) [Cell Reports]
G.O. Bozdag, S.A. Zamani-Dahaj, P.C. Kahn, T.C. Day, K. Tong, A.H. Balwani, E.L. Dyer, P.J. Yunker⁺& W.C. Ratcliff⁺: De novo evolution of macroscopic multicellularity, Nature, 2023 [Nature]
M. Mendelson, M. Azabou, S. Jacob, N. Grissom, D.P. Darrow, B. Ebitz, A. Herman, E.L. Dyer: Learning signatures of decision making from many individuals playing the same game, 11th IEEE EMBS Conference on Neural Engineering (NER'23), April 2023 [NER23]
C. Urzay⁺, N. Ahad⁺, Mehdi Azabou, Aidan Schneider, Geethika Atmakuri, K.B. Hengen, E.L. Dyer: Detecting change points in neural population activity with contrastive metric learning, 11th IEEE EMBS Conference on Neural Engineering (NER'23), April 2023 [NER23]

2022

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, Advances in Neural Information Processing Systems (NeurIPS), Dec 2022 [NeurIPS22]
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, Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, Dec 2022 (Page) [NeurIPS22]
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 [CVPR22]
J.M. Jackson⁺, R. Liu⁺, E.L. Dyer: Building representations of different brain areas through hierarchical point cloud networks, Conference on Medical Imaging with Deep Learning (MIDL), 2022 [MIDL22]
S. Thakoor, C. Tallec, M. Gheshlaghi Azar, M. Azabou, E.L. Dyer, R. Munos, P. Velickovic, M. Valko: Large-Scale Representation Learning on Graphs via Bootstrapping, International Conference on Learning Representations (ICLR), 2022 (Code) [ICLR22]
M. Dabagia, K. Kording, E.L. Dyer: Aligning latent representations of neural activity, Nature Biomedical Engineering, 2022 [NatureBME]
N. Ahad, E.L. Dyer, K.B. Hengen, Y. Xie, M.A. Davenport: Learning Sinkhorn divergences for supervised change point detection, arXiv, Feb 2022
S.J. Brunwasser, C. Farris, H. Elmore, E.L. Dyer, K. Bhaskaran Nair, J.D. Whitesell, J.A. Harris, K.B. Hengen: Circuit-specific selective vulnerability in the DMN persists in the face of widespread amyloid burden, bioRxiv, Nov 2022

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, Neural Information Processing Systems (NeurIPS), Oral (1% submissions), 2021 [NeurIPS21] (Code)
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, Dec 2021 [NeurIPS21] (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, Neural Information Processing Systems (NeurIPS), Workshop on Self-supervised Learning: Theory and Practice, Dec 2021 [NeurIPS21]
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 [NeurIPS21] (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, 2021 [ICIP21]
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, 2021 (Poster, Code) [ICML21]
C-H. Lin, J.D. Miano, E.L. Dyer: Bayesian optimization for modular black-box systems with switching costs, Conference on Uncertainty in Artificial Intelligence (UAI), July 2021 [UAI21]

2020

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
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
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 (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
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 (Interactive Atlas)
[Nature Scientific Data]A. Balwani, E.L. Dyer: A deep feature learning approach for mapping the brain’s microarchitecture and organization, International Conference on Machine Learning (ICML), Workshop on ML Interpretability for Scientific Discovery, July 2020 (Extended version)

2019

A. Balwani, E.L. Dyer, Modeling variability in brain architecture with deep feature learning, IEEE Asilomar Conference on Signals, Systems, and Computers, Dec 2019
K. Milligan, A. Balwani, E.L. Dyer, Brain Mapping at High Resolutions: Challenges and Opportunities, Current Opinion in Biomedical Engineering, 2019
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) (Code)
D. Rolnick, E.L. Dyer, Generative models and abstractions for large-scale neuroanatomy datasets, Current Opinion in Neurobiology, Feb 2019

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, Journal of Neuroscience, 31 October 2018, 38 (44) 9390-9401.
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
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 Annual Conference (EMBC), July 2018
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

2017

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. (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, eNeuro, 25 September 2017, 4 (5) ENEURO.0195-17. 2017. (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. (Code)

Before 2017

M. Azar, E.L. Dyer, and K.P. Körding, Convex relaxation regression: Black-Box optimization of smooth functions by learning their convex envelopes, Conference on Uncertainty in Artificial Intelligence (UAI), June 2016. (Poster, Slides)
E.L. Dyer, A.C. Sankaranarayanan, and R.G. Baraniuk, Greedy feature selection for subspace clustering, The Journal of Machine Learning Research (JMLR) 14 (1), 2487-2517, September 2013
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. (Code)