Conf


Peer-Reviewed Conference Proceedings
  1. A. De Silva, R. Ramesh, R. Yang*, S. Yu, J. T. Vogelstein, and P. Chaudhari. Prospective Learning: Learning for a Dynamic Future. Advances in neural information processing systems, 2024.
  2. Q. Wang, M. A. Powell, A. Geisa, E. Bridgeford, C. E. Priebe, and J. T. Vogelstein. Why do networks have inhibitory/negative connections? Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023.
  3. A. De Silva, R. Ramesh, C. Priebe, P. Chaudhari, and J. T. Vogelstein. The value of out-of-distribution data. International Conference on Machine Learning, 2023.
  4. Q. Wang, Michael A. Powell, Ali Geisa, Eric W. Bridgeford, and Joshua T. Vogelstein. Polarity is all you need to learn and transfer faster. Proceedings of the 40th International Conference on Machine Learning, 2023.
  5. J. T. Vogelstein, T. Verstynen, K. P. Kording, L. Isik, J. W. Krakauer, R. Etienne-Cummings, E. L. Ogburn, C. E. Priebe, R. Burns, K. Kutten, J. J. Knierim, J. B. Potash, T. Hartung, L. Smirnova, P. Worley, A. Savonenko, I. Phillips, M. I. Miller, R. Vidal, J. Sulam, A. Charles, N. J. Cowan, M. Bichuch, A. Venkataraman, C. Li, N. Thakor, J. M. Kebschull, M. Albert, J. Xu, M. H. Shuler, B. Caffo, T. Ratnanather, A. Geisa, S. Roh, E. Yezerets, M. Madhyastha, J. J. How, T. M. Tomita, J. Dey, N. Huang, J. M. Shin, K. A. Kinfu, P. Chaudhari, B. Baker, A. Schapiro, D. Jayaraman, E. Eaton, M. Platt, L. Ungar, L. Wehbe, A. Kepecs, A. Christensen, O. Osuagwu, B. Brunton, B. Mensh, A. R. Muotri, G. Silva, F. Puppo, F. Engert, E. Hillman, J. Brown, C. White, and W. Yang. Prospective Learning: Back to the Future. 2nd Conference on Lifelong Learning Agents, 2023, 2022.
  6. Haoyin Xu, Jayanta Dey, Sambit Panda, and Joshua T. Vogelstein. Extremely Simple Streaming Trees. 4th Conference on Lifelong Learning Agents, 2025, 2021.
  7. M. Madhyastha, K. Lillaney, J. Browne, J. T. Vogelstein, and R. Burns. BLOCKSET (Block-Aligned Serialized Trees): Reducing Inference Latency for Tree Ensemble Deployment. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021.
  8. M. Madhyastha, G. Li, V. Strnadová-Neeley, J. Browne, J. T. Vogelstein, R. Burns, and C. E. Priebe. Geodesic Forests. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2020.
  9. A. Nikolaidis, A. S. Heinsfeld, T. Xu, P. Bellec, J. Vogelstein, and M. Milham. Bagging Improves Reproducibility of Functional Parcellation of the Human Brain. bioRxiv, 2019.
  10. J. Browne, D. Mhembere, T. M. Tomita, J. T. Vogelstein, and R. Burns. Forest packing: Fast Parallel, Decision Forests. SIAM International Conference on Data Mining, SDM, 2018.
  11. K. Lillaney, D. Kleissas, A. Eusman, E. Perlman, W. Gray Roncal, J. T. Vogelstein, and R. Burns. Building NDStore through hierarchical storage management and microservice processing. Proceedings - IEEE 14th International Conference on eScience, e-Science, 2018.
  12. D. Zheng, D. Mhembere, J. T. Vogelstein, C. E. Priebe, and R. Burns. FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs. PPoPP, 2017.
  13. K. S. Kutten, N. Charon, M. I. Miller, J. T. Ratnanather, J. Matelsky, A. D. Baden, K. Lillaney, K. Deisseroth, L. Ye, and J. T. Vogelstein. A large deformation diffeomorphic approach to registration of CLARITY images via mutual information. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017.
  14. D. Mhembere, C. E. Priebe, J. T. Vogelstein, and R. Burns. knor : A NUMA-Optimized In-Memory , Distributed and Semi-External-Memory k-means Library. Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing, 2017.
  15. T. M. Tomita, M. Maggioni, and J. T. Vogelstein. ROFLMAO: Robust oblique forests with linear MAtrix operations. Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017, 2017.
  16. K. S. Kutten, J. T. Vogelstein, N. Charon, L. Ye, K. Deisseroth, and M. I. Miller. Deformably registering and annotating whole CLARITY brains to an atlas via masked LDDMM. Optics, Photonics and Digital Technologies for Imaging Applications IV, 2016.
  17. W. G. Roncal, M. Pekala, V. Kaynig-Fittkau, D. M. Kleissas, J. T. Vogelstein, H. Pfister, R. Burns, R. J. Vogelstein, M. A. Chevillet, and G. D. Hager. VESICLE: Volumetric Evaluation of Synaptic Inferfaces using Computer Vision at Large Scale. British Machine Vision Conference, 2015.
  18. D. Zheng, D. Mhembere, R. Burns, J. T. Vogelstein, C. E. Priebe, and A. S. Szalay. FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs. USENIX Conference on File and Storage Technologies, 2015.
  19. D. Mhembere, W. Gray Roncal, D. Sussman, C. E. Priebe, R. Jung, S. Ryman, R. J. Vogelstein, J. T. Vogelstein, and R. Burns. Computing scalable multivariate glocal invariants of large (brain-) graphs. 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings, 2013.
  20. W. G. Roncal, Z. H. Koterba, D. Mhembere, D. M. Kleissas, J. T. Vogelstein, R. Burns, A. R. Bowles, D. K. Donavos, S. Ryman, R. E. Jung, L. Wu, V. Calhoun, and R. J. Vogelstein. MIGRAINE: MRI graph reliability analysis and inference for connectomics. 2013 IEEE Global Conference on Signal and Information Processing, 2013.
  21. R. Burns, W. G. Roncal, D. Kleissas, K. Lillaney, P. Manavalan, E. Perlman, D. R. Berger, D. D. Bock, K. Chung, L. Grosenick, N. Kasthuri, N. C. Weiler, K. Deisseroth, M. Kazhdan, J. Lichtman, R. C. Reid, S. J. Smith, A. S. Szalay, J. T. Vogelstein, and R. J. Vogelstein. The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience. ACM International Conference Proceeding Series, 2013.
  22. D. E. Carlson, V. Rao, J. T. Vogelstein, and L. Carin. Real-Time Inference for a Gamma Process Model of Neural Spiking. Advances in Neural Information Processing Systems 26, 2013.
  23. B. Cornelis, Y. Yang, J. T. Vogelstein, A. Dooms, I. Daubechies, and D. Dunson. Bayesian crack detection in ultra high resolution multimodal images of paintings. 18th International Conference on Digital Signal Processing, 2013.
  24. M. Fiori, P. Sprechmann, J. Vogelstein, P. Muse, and G. Sapiro. Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching. Advances in Neural Information Processing Systems, 2013.
  25. D. Koutra, J. T. Vogelstein, and C. Faloutsos. DELTACON: A principled massive-graph similarity function. Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013, 2013.
  26. V. Kulkarni, J. S. Pudipeddi, L. Akoglu, J. T. Vogelstein, R. J. Vogelstein, S. Ryman, and R. E. Jung. Sex differences in the human connectome. Brain and Health Informatics, 2013.
  27. F. Petralia, J. Vogelstein, and D. B. Dunson. Multiscale Dictionary Learning for Estimating Conditional Distributions. Advances in Neural Information Processing Systems, 2013.
  28. Q. J. Huys, J. Vogelstein, and P. Dayan. Psychiatry: Insights into depression through normative decision-making models. Advances in Neural Information Processing Systems, 2008.