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<h2>Publications</h2>
<h4><strong><span style="text-decoration: underline;">Preprints</span></strong></h4>
<ul>
<li><span dir="ltr" role="presentation">Z. Chen, A. Andre, W. Ma, I. Knight, S. Shuvaev, E.L. Dyer. <a href="https://arxiv.org/pdf/2512.24898"><strong>PRISM: A Hierarchical Multiscale Approach for Time Series Forecasting</strong></a></span><span dir="ltr" role="presentation">, arXiv, 2026</span></li>
</ul>
<h4><strong><span style="text-decoration: underline;">2026</span></strong></h4>
<div id="gsc_oci_title_wrapper">
<ul>
<li>S. Daste, T.H. Pham, M. Seppo, A. André, S. Srinivasan, J. Xiao, A. Sattin, C. Nardin, T. Fellin, K.M. Franks, E.L. Dyer, A. Fleischmann. <a href="https://www.biorxiv.org/content/10.64898/2025.12.12.694045v1"><strong>Projection-specific Routing of Odor Information in the Olfactory Cortex</strong></a>, Current Biology, May 2026</li>
<li>W. Ma+, R. Liu+, E. Zippi, H. Pouransari, J. Xiao, C. Sandino, B. Mahasseni, J. Minxha, E. Azemi, A. Moin, E.L. Dyer. <a class="gsc_oci_title_link" href="https://arxiv.org/abs/2410.08421"><strong>Narrative of time across scales (NOTS)</strong></a>, ICLR TSALM Workshop, April 2026 (+ co-first authors)</li>
</ul>
</div>
<h4><strong><span style="text-decoration: underline;">2025</span></strong></h4><div id="gsc_oci_title_wrapper">
<ul>
<li>D. Lachi, M. Azabou, V. Arora, E.L. Dyer. <a href="https://arxiv.org/abs/2407.11907"><strong>GraphFM: A generalist graph transformer that learns transferable representations across diverse domains</strong></a></span>Transactions on Machine Learning (TMLR), December 2025 <span style="color: #008b8b;"><strong>[TMLR]</strong></span></li>
<li>V. Arora, D. Lachi, I.J. Knight, M. Azabou, B. Richards, C. Hurwitz, J. Siegle, E.L. Dyer. <strong><a href="https://neurips.cc/virtual/2025/poster/115008">Know Thyself by Knowing Others: Learning Neuron Identity from Population Context</a></strong>, The Thirty-Ninth Annual Conference on Neural Information Processing Systems, 2025. <span style="color: #008b8b;"><strong>[NeurIPS 2025]</strong></span></li>
<li>D. Lachi, M. Mohammadi, J. Meyer, V. Arora, T. Palczewski, E.L. Dyer. <strong><a href="https://openreview.net/forum?id=fcVIJ2WSIX">RGP: A Cross-Attention based Graph Transformer for Relational Deep Learning</a></strong>, The Fourth Learning on Graphs Conference, 2025. <span style="color: #008b8b;"><strong>[LOG 2025]</strong></span></li>
<li>V. Arora*, D. Lachi*, S.P. Mahato, M. Azabou, Z. Chen, E.L. Dyer. <strong><a href="https://neurips.cc/virtual/2025/127666">Exploiting All Laplacian Eigenvectors for Node Classification with Graph Transformers</a></strong>, New Perspectives in Graph Machine Learning (NeurIPS workshop), 2025. <span style="color: #008b8b;"><strong>[NPGML 2025]</strong></span></li>
<li>J. Meyer*, D. Lachi*, M. Mohammadi, R.R. Upendra, E.L. Dyer, M. Li, T. Palczewski. <strong><a href="https://neurips.cc/virtual/2025/127657">RELATE: A Schema-Agnostic Cross-Attention Encoder for Multimodal Relational Graphs</a></strong>, New Perspectives in Graph Machine Learning (NeurIPS workshop), 2025. <span style="color: #008b8b;"><strong>[NPGML 2025]</strong></span></li>
<li>S.P. Mahato*, J. Xiao*, A. Andre, G. Chau, W. Ma, I.J. Knight, D. Nguyen, J. Hu, B.W. Brunton, M.S. Beauchamp, B. Pesaran, S.A. Shuvaev, E.L. Dyer. <strong><a href="https://neurips.cc/virtual/2025/122131">A scalable self-supervised method for modeling human intracranial recordings during natural behavior</a></strong>, Foundation Models for the Brain and Body (NeurIPS workshop), 2025. <span style="color: #008b8b;"><strong>[BrainBodyFM 2025]</strong></span></li>
<li>A. Andre*, G. Roy*, E.L. Dyer, K. Wang. <strong><a href="https://arxiv.org/abs/2508.20401">Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting</a></strong>, Evaluating and Applying Recommender Systems (RecSys workshop), 2025. <span style="color: #008b8b;"><strong>[EARL 2025]</strong></span></li>
<li>A.H Ryoo*, N.H. Krishna*, X. Mao*, M. Azabou, E.L. Dyer, M.G. Perich, G. Lajoie. <a href="https://arxiv.org/abs/2506.05320"><strong>Generalizable, real-time neural decoding with hybrid state-space models.</strong></a>, arXiv preprint arXiv:2506.05320, to appear at NeurIPS 2025. <span style="color: #008b8b;"><strong>[NeurIPS 2025]</strong></span></li>
<li>G. Mentzelopoulos, I. Asmanis, K. Kording, E.L. Dyer, K. Daniilidis, F. Vitale: A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks, to appear at NeurIPS 2025. <span style="color: #008b8b;"><strong>[NeurIPS 2025]</strong></span></li>
<li>N. Ahad, E.L. Dyer, K.B. Hengen, Y. Xie, M.A. Davenport. <strong><a href="https://arxiv.org/abs/2202.04000">Learning Sinkhorn divergences for supervised change point detection</a></strong>, IEEE Transactions on Signal Processing, August 2025. <span style="color: #008b8b;"><strong>[IEEE-TSP]</strong></span></li>
<li>Y. Zhang, Y. Wang, M. Azabou, A. Andre, Z. Wang, H. Lyu, T.I. Laboratory, E.L. Dyer, L. Paninski, C. Hurwitz. <strong><a href="https://arxiv.org/abs/2504.08201">Neural Encoding and Decoding at Scale</a></strong>. <strong><span style="color: #008b8b;">[ICML25]</span></strong></li>
<li><span dir="ltr" role="presentation">N. Ahad, M.A. Davenport, E.L. Dyer: </span><a href="https://openreview.net/pdf?id=8C8LJIqF4y"><strong>Time Series Domain Adaptation via Channel-Selective Representation Alignment</strong></a>, <span dir="ltr" role="presentation">Transactions on Machine Learning Research, Jan 2025 <strong><span style="color: #008b8b;">[TMLR]</span></strong></span></li>
<li>M. Azabou<span dir="ltr" role="presentation"><sup>+</sup></span>, K. Xuejing Pan<span dir="ltr" role="presentation"><sup>+</sup></span>, V. Arora, I.J. Knight, E.L. Dyer<span dir="ltr" role="presentation"><sup>+</sup></span>, B.A. Richards<span dir="ltr" role="presentation"><sup>+</sup></span> <strong><a href="https://openreview.net/pdf?id=IuU0wcO0mo" target="_blank" rel="noopener noreferrer">Multi-session, multi-task neural decoding from distinct cell-types and brain regions</a> </strong> (+ co-first, co-last authors) <strong><span style="color: #008b8b;">[ICLR25]</span></strong></li>
<li id="gsc_oci_title">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: <strong><a class="gsc_oci_title_link" href="https://www.biorxiv.org/content/10.1101/2024.11.05.622159.abstract" data-clk="hl=en&sa=T&ei=uNZ1Z6TPC8aWy9YP44PQwQc">In vivo cell-type and brain region classification via multimodal contrastive learning</a> </strong><strong><span style="color: #008b8b;">[ICLR25]</strong></span></li>
</ul>
</div>
<h4><span style="text-decoration: underline;"><strong>2024</strong></span></h4>
<ul>
<li>Z. Chen, C-H Lin, R. Liu, J. Xiao, E.L. Dyer: <a href="https://arxiv.org/pdf/2502.20141"><strong>Your contrastive learning problem is secretly a distribution alignment problem</strong></a>, Advances in Neural Information Processing Systems, Dec 2024 <strong><span style="color: #008b8b;">[NeurIPS24]</span></strong></li>
<li>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: <a href="https://arxiv.org/abs/2407.14668"><strong>Towards a “universal translator” for neural dynamics at single-cell, single-spike resolution</strong>,</a> Advances in Neural Information Processing Systems, Dec 2024 <strong><span style="color: #008b8b;">[NeurIPS24]</span></strong></li>
<li>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: <a href="https://arxiv.org/abs/2308.06578"><strong>The time is ripe to reverse engineer an entire nervous system: simulating behavior from neural interactions</strong></a>, arXiv, Sept 2024</li>
<li class="gsc_oci_value">G. Tolossa, A. Schneider, E.L. Dyer, K.B. Hengen: <strong><a href="https://elifesciences.org/reviewed-preprints/101506">A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains</a></strong>, eLife, Nov 2024</li>
<li>C. Kaushik<span dir="ltr" role="presentation"><sup>+</sup></span>, R. Liu<span dir="ltr" role="presentation"><sup>+</sup></span>, C-H Lin, A. Khera, M.Y. Jin, W. Ma, V. Muthukumar, E.L. Dyer: <a href="https://arxiv.org/abs/2402.11742"><strong>Balanced data, imbalanced spectra: Unveiling class disparities with spectral imbalance</strong></a>, International Conference on Machine Learning, 2024 (<span dir="ltr" role="presentation"><sup>+</sup></span>co-first authors) <strong><span style="color: #008b8b;">[ICML24]</span></strong></li>
<li>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: <a href="https://www.biorxiv.org/content/10.1101/2023.09.01.555947v2"><strong>Tauopathy severely disrupts homeostatic set-points in emergent neural dynamics but not the activity of individual neurons</strong></a>, <em>Neuron</em>, Sept 2023 <strong><span style="color: #008b8b;">[Neuron]</span></strong></li>
<li>D.F. Parks<span dir="ltr" role="presentation"><sup>+</sup></span>, A.M. Schneider<span dir="ltr" role="presentation"><sup>+</sup></span>, Y. Xu, S.J. Brunwasser, S. Funderburk, D. Thurber, T. Blanche, E.L. Dyer, D. Haussler, K.B. Hengen: <a href="https://www.nature.com/articles/s41593-024-01715-2"><strong>A non-oscillatory, millisecond-scale embedding of brain state provides insight into behavior</strong></a>, <em>Nature Neuroscience</em>, July 2024 <strong><span style="color: #008b8b;">[Nature Neuroscience]</span></strong></li>
<li>Jingyun Xiao, Ran Liu, Eva L. Dyer: <a href="https://openreview.net/forum?id=c56TWtYp0W"><strong>GAFormer: Enhancing time-series transformers through group-aware embeddings</strong></a>, <em>International Conference on Learning Representations (ICLR)</em>, 2024 <strong><span style="color: #008b8b;">[ICLR24]</span></strong></li>
<li>C-H Lin, C. Kaushik, E.L. Dyer<sup>+</sup> & V. Muthukumar<sup>+</sup>: <a href="https://arxiv.org/pdf/2210.05021.pdf"><strong>The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective</strong></a>, <em>Journal of Machine Learning Research (JMLR)</em>, (+ co-last authors) <strong><span style="color: #008b8b;">[JMLR]</span></strong></li>
<li>R. Liu, S. Khose, J. Xiao, L. Sathidevi, K. Ramnath, Z. Kira, E.L. Dyer: <a href="https://arxiv.org/abs/2308.14596"><strong>LatentDR: Improving model generalization through sample-aware latent degradation and restoration</strong></a>, <em>IEEE Winter Conference on Applications of Computer Vision</em>, 2024 <strong><span style="color: #008b8b;">[WACV24]</span></strong></li>
</ul>
<h4><span style="text-decoration: underline;">2023</span></h4>
<ul>
<li>E.L. Dyer, K.P. Kording: <a href="https://www.pnas.org/doi/10.1073/pnas.2319169120"><strong>Why the simplest explanation isn't always the best</strong></a>, Proceedings of the National Academy of Sciences (PNAS), Dec 2023 <strong><span style="color: #008b8b;">[PNAS]</span></strong></li>
<li>M. Azabou, V. Arora, V. Ganesh, X. Mao, S. Nachimuthu, M. Mendelson, B. Richards, M. Perich, G. Lajoie, E.L. Dyer: <a href="https://arxiv.org/abs/2310.16046"><strong>A unified, scalable framework for neural population decoding</strong></a>, NeurIPS 2023 <strong><span style="color: #008b8b;">[NeurIPS23]</span></strong> (<a href="https://poyo-brain.github.io/"><strong>Page</strong></a>)</li>
<li>M. Azabou, M. Mendelson, N. Ahad, M. Sorokin, S. Thakoor, C. Urzay, E.L. Dyer: <a href="https://arxiv.org/abs/2303.08811"><strong>Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis</strong></a>, Advances in Neural Information Processing Systsems 2023 (Spotlight, 3% acceptance) (<a href="https://multiscale-behavior.github.io/"><strong>Web</strong></a>)<strong><span style="color: #008b8b;"> [NeurIPS23]</span></strong></li>
<li>A. Ten Eyck, Y-C Chen, L. Gifford, D. Torres-Rivera, E.L. Dyer, G.B. Melikyan: <a href="https://www.sciencedirect.com/science/article/pii/S0166093423001593"><strong>Label-free imaging of nuclear membrane for analysis of nuclear import of viral complexes</strong></a>, Journal of Virological Methods, Volume 322, December 2023</li>
<li>Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer: <a href="http://proceedings.mlr.press/v202/azabou23a/azabou23a.pdf"><strong>Half-Hop: A graph upsampling approach for slowing down message passing</strong></a>, ICML 2023 <strong><span style="color: #008b8b;">[ICML23]</span></strong></li>
<li>A. Schneider<sup>+</sup>, M. Azabou<sup>+</sup>, L. McDougall-Vigier, D. Parks, S. Ensley, K. Bhaskaran-Nair, T. Nowakowski, E.L. Dyer<sup>+</sup> & K.B. Hengen<sup>+</sup>: <a href="https://www.cell.com/cell-reports/pdfExtended/S2211-1247(23)00329-7"><strong>Transcriptomic cell type structures in vivo neuronal activity across multiple timescales</strong></a>, Cell Reports, 2023 (<a href="https://github.com/nerdslab/lolcat"><strong>Code</strong></a>) <strong><span style="color: #008b8b;">[Cell Reports]</span></strong></li>
<li>G.O. Bozdag, S.A. Zamani-Dahaj, P.C. Kahn, T.C. Day, K. Tong, A.H. Balwani, E.L. Dyer, P.J. Yunker<sup>+ </sup>& W.C. Ratcliff<sup>+</sup>: <a href="https://www.nature.com/articles/s41586-023-06052-1"><strong>De novo evolution of macroscopic multicellularity</strong></a>, Nature, 2023 <strong><span style="color: #008b8b;">[Nature]</span></strong></li>
<li>M. Mendelson, M. Azabou, S. Jacob, N. Grissom, D.P. Darrow, B. Ebitz, A. Herman, E.L. Dyer: <a href="https://ieeexplore.ieee.org/document/10123846"><strong>Learning signatures of decision making from many individuals playing the same game</strong></a>, 11th IEEE EMBS Conference on Neural Engineering (NER'23), April 2023 <strong><span style="color: #008b8b;">[NER23]</span></strong></li>
<li>C. Urzay<sup>+</sup>, N. Ahad<sup>+</sup>, Mehdi Azabou, Aidan Schneider, Geethika Atmakuri, K.B. Hengen, E.L. Dyer: <a href="https://sites.gatech.edu/dyerlab/files/2023/05/NER_20230_Change_detection_metric-2.pdf"><strong>Detecting change points in neural population activity with contrastive metric learning</strong></a>, 11th IEEE EMBS Conference on Neural Engineering (NER'23), April 2023 <strong><span style="color: #008b8b;">[NER23]</span></strong></li>
</ul>
<h4><span style="text-decoration: underline;">2022</span></h4>
<ul>
<li>R. Liu, M. Azabou, M. Dabagia, J. Xiao, E.L. Dyer: <a href="https://arxiv.org/abs/2206.06131"><strong>Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers</strong></a>, Advances in Neural Information Processing Systems (NeurIPS), Dec 2022 <strong><span style="color: #008b8b;">[NeurIPS22]</span></strong></li>
<li>J. Quesada<sup>+</sup>, L. Sathidevi<sup>+</sup>, R. Liu, N. Ahad, J.M. Jackson, M. Azabou, J. Xiao, C. Liding, C. Urzay, W. Gray-Roncal, E.C. Johnson<sup>+</sup> & E.L. Dyer<sup>+</sup>: <a href="https://arxiv.org/abs/2301.00345"><strong>MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction</strong></a>, Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, Dec 2022 (<a href="https://mtneuro.github.io/"><strong>Page</strong></a>) <strong><span style="color: #008b8b;">[NeurIPS22]</span></strong></li>
<li>M. Azabou, M. Mendelson, M. Sorokin, S. Thakoor, N. Ahad, C. Urzay, E.L. Dyer: <a href="https://arxiv.org/pdf/2206.07041.pdf"><strong>Learning Behavior Representations Through Multi-Timescale Bootstrapping</strong></a>, Conference on Vision and Pattern Recognition (CVPR) Workshop on Multi-Agent Behavior, 2022 <strong><span style="color: #008b8b;">[CVPR22]</span></strong></li>
<li>J.M. Jackson<sup>+</sup>, R. Liu<sup>+</sup>, E.L. Dyer: <a href="https://sites.gatech.edu/dyerlab/files/2023/05/Jackson_MIDL2022.pdf"><strong>Building representations of different brain areas through hierarchical point cloud networks</strong></a>, Conference on Medical Imaging with Deep Learning (MIDL), 2022 <strong><span style="color: #008b8b;">[MIDL22]</span></strong></li>
<li>S. Thakoor, C. Tallec, M. Gheshlaghi Azar, M. Azabou, E.L. Dyer, R. Munos, P. Velickovic, M. Valko: <a href="https://arxiv.org/abs/2102.06514"><strong>Large-Scale Representation Learning on Graphs via Bootstrapping</strong></a>, International Conference on Learning Representations (ICLR), 2022 (<a href="https://github.com/nerdslab/bgrl"><strong>Code</strong></a>) <strong><span style="color: #008b8b;">[ICLR22]</span></strong></li>
<li>M. Dabagia, K. Kording, E.L. Dyer: <a href="https://www.nature.com/articles/s41551-022-00962-7"><strong>Aligning latent representations of neural activity</strong></a>, Nature Biomedical Engineering, 2022 <strong><span style="color: #008b8b;">[NatureBME]</span></strong></li>
<li>N. Ahad, E.L. Dyer, K.B. Hengen, Y. Xie, M.A. Davenport: <a href="https://arxiv.org/pdf/2202.04000.pdf"><strong>Learning Sinkhorn divergences for supervised change point detection</strong></a>, arXiv, Feb 2022</li>
<li><span class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">S.J.</span> <span class="nlm-surname">Brunwasser</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">C.</span> <span class="nlm-surname">Farris</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">H. </span><span class="nlm-surname">Elmore</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">E.L.</span> <span class="nlm-surname">Dyer</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">K. Bhaskaran</span> <span class="nlm-surname">Nair</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">J.D.</span> <span class="nlm-surname">Whitesell</span></span>, J.<span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">A.</span> <span class="nlm-surname">Harris</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">K.B.</span> <span class="nlm-surname">Hengen</span></span></span><span dir="ltr" role="presentation">: </span><strong><a href="https://www.biorxiv.org/content/10.1101/2022.11.14.516510v2.full.pdf">Circuit-specific selective vulnerability in the DMN persists in the face of widespread amyloid burden</a></strong>, bioRxiv, Nov 2022</li>
</ul>
<h4><span style="text-decoration: underline;">2021</span></h4>
<ul>
<li>R. Liu, M. Azabou, M. Dabagia, C-H. Lin, M. Gheshlaghi Azar, K.B. Hengen, M. Valko, E.L. Dyer: <a href="https://arxiv.org/pdf/2111.02338.pdf"><strong>Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity</strong></a>, Neural Information Processing Systems (NeurIPS), Oral (1% submissions), 2021 <strong><span style="color: #008b8b;">[NeurIPS21]</span></strong> (<a href="http://nerdslab.github.io/SwapVAE"><strong>Code</strong></a>)</li>
<li>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: <a href="https://arxiv.org/abs/2102.10106"><strong>Mine Your Own vieW: Self-supervised learning through across-sample prediction</strong></a>, Neural Information Processing Systems (NeurIPS), Workshop on Self-supervised Learning: Theory and Practice, Oral, Dec 2021 <strong><span style="color: #008b8b;">[NeurIPS21]</span></strong> (<a href="https://nerdslab.github.io/myow"><strong>Code</strong></a>)</li>
<li>M. Azabou<sup>+</sup>, M. Dabagia<sup>+</sup>, R. Liu<sup>+</sup>, C-H. Lin, K.B. Hengen, E.L. Dyer: <a href="https://sslneurips21.github.io/files/CameraReady/neural_ssl_workshop.pdf"><strong>Using self-supervision and augmentations to build insights into neural coding</strong></a>, Neural Information Processing Systems (NeurIPS), Workshop on Self-supervised Learning: Theory and Practice, Dec 2021 <strong><span style="color: #008b8b;">[NeurIPS21]</span></strong></li>
<li>F. Pei<sup>+</sup>, J. Ye<sup>+</sup>, 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: <a href="https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/979d472a84804b9f647bc185a877a8b5-Paper-round2.pdf"><strong>Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity</strong></a>, Neural Information Processing Systems (NeurIPS), Benchmark and Datasets Track, 2021 <strong><span style="color: #008b8b;">[NeurIPS21]</span></strong> (<a href="https://neurallatents.github.io/"><strong>Page</strong></a>)</li>
<li>A. Balwani<sup>+</sup>, J. Miano<sup>+</sup>, R. Liu, L. Kitchell, J.A. Prasad, E.C. Johnson, W. Gray-Roncal, E.L. Dyer: <a href="https://sites.gatech.edu/dyerlab/files/2021/06/ICIP_2021___XRay_MTL-1.pdf"><strong>Multi-scale modeling of neural structure in X-ray imagery</strong></a>, IEEE International Conference on Image Processing, 2021 <strong><span style="color: #008b8b;">[ICIP21]</span></strong></li>
<li>C-H. Lin, M. Azabou, E.L. Dyer: <a href="http://proceedings.mlr.press/v139/lin21a.html"><strong>Making transport more robust and interpretable by moving data through a small number of anchor points</strong></a>, International Conference on Machine Learning, 2021 (<a href="https://sites.gatech.edu/dyerlab/files/2021/07/icml_lot_final.pdf">Poster</a>, <a href="http://nerdslab.github.io/latentOT">Code</a>) <strong><span style="color: #008b8b;">[ICML21]</span></strong></li>
<li>C-H. Lin, J.D. Miano, E.L. Dyer: <a href="https://proceedings.mlr.press/v161/lin21c/lin21c.pdf"><strong>Bayesian optimization for modular black-box systems with switching costs</strong></a>, Conference on Uncertainty in Artificial Intelligence (UAI), July 2021 <strong><span style="color: #008b8b;">[UAI21]</span></strong></li>
</ul>
<h4><span style="text-decoration: underline;">2020</span></h4>
<ul>
<li>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: <a href="https://doi.org/10.1093/gigascience/giaa147"><strong>Toward a Reproducible, Scalable, Framework for Processing Large Neuroimaging Datasets</strong></a>, GigaScience, Volume 9, Issue 12, December 2020</li>
<li>R. Liu, C. Subakan, A. Balwani, J. Whitesell, J.A. Harris, S. Koyejo, E.L. Dyer: <a href="https://doi.org/10.1007/978-3-030-59722-1_25"><strong>A generative modeling approach for interpreting population-level variability in brain structure</strong></a>, International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI), October 2020</li>
<li>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: <a href="https://joss.theoj.org/papers/10.21105/joss.01959"><strong>Pyglmnet: Python implementation of elastic-net regularized generalized linear models</strong></a>, J Open Source Software, 5:47, 2020 (<a href="https://github.com/glm-tools/pyglmnet/"><strong>Code</strong></a>)</li>
<li>K. Shadi, E.L. Dyer, C. Dovrolis: <a href="https://doi.org/10.1162/netn_a_00164"><strong>Multi-sensory integration in the mouse cortical connectome using a network diffusion model</strong></a>, Network Neuroscience, October 2020</li>
<li>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 href="https://www.nature.com/articles/s41597-020-00692-y"><strong>A three-dimensional thalamocortical dataset for characterizing brain heterogeneity</strong></a>, Nature Scientific Data, October 2020 (<a href="http://bossdb.org/project/prasad2020"><strong>Interactive Atlas</strong></a>)</li>
<li><strong>[Nature Scientific Data]</strong>A. Balwani, E.L. Dyer: <a href="https://sites.gatech.edu/dyerlab/wp-content/uploads/sites/630/2020/06/Balwani_ICML_Interpretability_Workshop_2020.pdf"><strong>A deep feature learning approach for mapping the brain’s microarchitecture and organization</strong></a>, International Conference on Machine Learning (ICML), Workshop on ML Interpretability for Scientific Discovery, July 2020 (<a href="https://www.biorxiv.org/content/10.1101/2020.05.26.117473v1"><strong>Extended version</strong></a>)</li>
</ul>
<h4><strong><span style="text-decoration: underline;">2019</span></strong></h4><ul>
<li>A. Balwani, E.L. Dyer, <a href="https://sites.gatech.edu/dyerlab/wp-content/uploads/sites/630/2020/02/Balwani2019_FullPaper.pdf"><strong>Modeling variability in brain architecture with deep feature learning</strong></a>, <em>IEEE Asilomar Conference on Signals, Systems, and Computers</em>, Dec 2019</li>
<li>K. Milligan, A. Balwani, E.L. Dyer, <a href="https://www.sciencedirect.com/science/article/pii/S2468451119300625"><strong>Brain Mapping at High Resolutions: Challenges and Opportunities</strong></a>, <em>Current Opinion in Biomedical Engineering</em>, 2019</li>
<li>J. Lee, M. Dabagia, E.L. Dyer+, C. Rozell+, <a href="https://sites.gatech.edu/dyerlab/wp-content/uploads/sites/630/2019/11/NeurIPS2019_Final_wSupp.pdf"><strong>Hierarchical Optimal Transport for Multimodal Distribution Alignment</strong></a>, <em>Neural Information Processing Systems (NeurIPS)</em>, Dec 2019. (+ equal contribution) (<span style="color: #993366;"><a style="color: #993366;" href="https://github.com/nerdslab/PyHiWA">Code</a></span>)</li>
<li>D. Rolnick, E.L. Dyer, <a href="http://sites.gatech.edu/dyerlab/wp-content/uploads/sites/630/2019/03/Rolnick_CurrOp_2019.pdf"><strong>Generative models and abstractions for large-scale neuroanatomy datasets</strong></a>, <em>Current Opinion in Neurobiology</em>, Feb 2019</li>
</ul>
<h4><strong><span style="text-decoration: underline;">2018</span></strong></h4><ul>
<li>C. Pandarinath, K.C. Ames, A.A. Russo, A. Farshchian, L.E. Miller, E.L. Dyer, J.C. Kao, <a href="https://doi.org/10.1523/JNEUROSCI.1669-18.2018"><strong>Latent factors and dynamics in motor cortex and their application to brain-machine interfaces</strong></a>, <span class="highwire-cite-metadata-journal highwire-cite-metadata"><em>Journal of Neuroscience</em>, </span><span class="highwire-cite-metadata-date highwire-cite-metadata">31 October 2018, </span><span class="highwire-cite-metadata-volume highwire-cite-metadata">38 </span><span class="highwire-cite-metadata-issue highwire-cite-metadata">(44) </span><span class="highwire-cite-metadata-pages highwire-cite-metadata">9390-9401</span>.</li>
<li>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, <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0206172"><strong>Large-scale neuroanatomy using LASSO: Loop-based Automated Serial Sectioning Operation</strong></a>, <em>Plos ONE</em>, October 2018</li>
<li>T.J. LaGrow, M. Moore, J.A. Prasad, M.A. Davenport, E.L. Dyer, <a href="https://www.dropbox.com/s/vk9o6p6an1xndhe/LaGrow_EMBC2018.pdf?dl=0"><strong>Approximating Cellular Densities from High-Resolution Neuroanatomical Imaging Data</strong></a>, <em>IEEE Engineering in Medicine and Biology Annual Conference (EMBC)</em>, July 2018</li>
<li>X. Yang, V. De Andrade, W. Scullin, E. L. Dyer, F. De Carlo, N. Kasthuri, Doga Gürsoy, <a href="http://doi.org/10.1038/s41598-018-19426-7"><strong>Enhancing low-dose X-ray tomography through a deep convolutional neural network</strong></a>, <em>Nature Scientific Reports</em> 8, 2018</li>
</ul>
<h4><strong><span style="text-decoration: underline;">2017</span></strong></h4><ul>
<li>E.L. Dyer, M. Azar, H.L. Fernandes, M. Perich, L.E. Miller, and K.P. Körding, <a href="http://doi.org/10.1038/s41551-017-0169-7"><strong>A cryptography-based approach to movement decoding</strong></a>, <em>Nature Biomedical Engineering</em>, 1(12), 967. 2017. (<span style="color: #993366;"><a style="color: #993366;" href="http://kordinglab.com/DAD/">Code</a></span>)</li>
<li>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, <a href="https://doi.org/10.1523/ENEURO.0195-17.2017"><strong>Quantifying mesoscale neuroanatomy using X-ray microtomography</strong></a>, <span class="highwire-cite-metadata-journal highwire-cite-metadata"><em>eNeuro</em>, </span><span class="highwire-cite-metadata-date highwire-cite-metadata">25 September 2017, </span><span class="highwire-cite-metadata-volume highwire-cite-metadata">4 </span><span class="highwire-cite-metadata-issue highwire-cite-metadata">(5) </span><span class="highwire-cite-metadata-pages highwire-cite-metadata">ENEURO.0195-17. 2017</span>. (<span style="color: #993366;"><a style="color: #993366;" href="http://github.com/nerdslab/xbrain/">Code</a>, <a style="color: #993366;" href="http://github.com/nerdslab/xbrain/tree/master/data">Data</a></span>)</li>
<li>A. Mirhoseini, E.L. Dyer, E. Songhori, R.G. Baraniuk, and F. Koushanfar, <a href="http://doi.org/10.1109/TNNLS.2016.2631581"><strong>RankMap: A platform-aware framework for distributed learning from dense datasets</strong></a>, <em>IEEE Transactions on Neural Networks and Learning Systems</em>, 2017. (<a style="color: #993366;" href="https://github.com/azalia/RankMap"><span style="color: #993366;">Code</span></a>)</li>
</ul>
<h4><strong><span style="text-decoration: underline;">Before 2017</span></strong></h4><ul>
<li>M. Azar, E.L. Dyer, and K.P. Körding, <a href="http://arxiv.org/abs/1602.02191"><strong>Convex relaxation regression: Black-Box optimization of smooth functions by learning their convex envelopes</strong></a>, <em>Conference on Uncertainty in Artificial Intelligence (UAI)</em>, June 2016. (<span style="color: #993366;"><a style="color: #993366;" href="https://dl.dropboxusercontent.com/u/1260288/Posters/Dyer_UAI2016.png">Poster</a>, </span><a style="color: #993366;" href="https://dl.dropboxusercontent.com/u/1260288/Talks/Dyer_UAI2016.pdf"><span style="color: #993366;">Slides</span></a>)</li>
<li>E.L. Dyer, A.C. Sankaranarayanan, and R.G. Baraniuk, <a href="http://www.jmlr.org/papers/v14/dyer13a.html"><strong>Greedy feature selection for subspace clustering</strong></a>, The <em>Journal of Machine Learning Research (JMLR)</em> 14 (1), 2487-2517, September 2013</li>
<li>E.L. Dyer, C. Studer, J.T. Robinson, and R.G Baraniuk, <a href="http://dx.doi.org/10.1109/NER.2013.6696004"><strong>A robust and efficient method to recover neural events from noisy and corrupted data</strong></a>, <em>IEEE EMBS Neural Engineering (NER) Conference</em>, 2013. (<a style="color: #00ced1;" href="https://github.com/KordingLab/nerds"><span style="color: #993366;">Code</span></a>)</li>
</ul>
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