Johns Hopkins School of Medicine

 https://neurodata.io/research/peer-reviewed/

• S. Cencheng, S. Wang, A. Badea, C. E. Priebe, and J. T. Vogelstein. Discovering the signal subgraph: An iterative screening approach on graphs. Pattern Recognition Letters, 2024.
•  S. Cencheng, C. Jaewon, M. Ronak, X. Ting, and J. T. Vogelstein. Independence Testing for Temporal Data. Transactions on Machine Learning Research, 2024.
•  X. Li, N. Bianchini Esper, L. Ai, S. Giavasis, H. Jin, E. Feczko, T. Xu, J. Clucas, A. Franco, A. Sólon Heinsfeld, A. Adebimpe, J. T. Vogelstein, C. Yan, O. Esteban, R. A. Poldrack, C. Craddock, D. Fair, T. Satterthwaite, G. Kiar, and M. P. Milham. Moving Beyond Processing and Analysis-Related Variation in Neuroscience. Nature Human Behaviour, 2024.
•  H. S. Helm, A. De Silva, J. T. and Vogelstein, C. E. Priebe, and W. Yang. Approximately Optimal Domain Adaptation with Fisher’s Linear Discriminant. Mathmatics, 2024.
•  Z. Wang, E. Bridgeford, S. Wang, J. T. Vogelstein, and . B. Caffo. Statistical Analysis of Data Repeatability Measures. International Statistical Review, 2024.
•  T. Zuzul, E. C. Pahnke, J. Larson, P. Bourke, N. Caurvina, N. P. Shah, F. Amini, J. Weston, Y. Park, J. Vogelstein, C. White, and C. E. Priebe. Dynamic Silos: Increased Modularity and Decreased Stability in Intraorganizational Communication Networks During the COVID-19 Pandemic. Management Science, 2024.
•  R. Xiong, A. Koenecke, M. Powell, Z. Shen, J. T. Vogelstein, and S. Athey. Federated Causal Inference in Heterogeneous Observational Data. Statistics in Medicine, 2023.
•  Benjamin D Pedigo, Mike Powell, Eric W Bridgeford, Michael Winding, Carey E Priebe, and Joshua T Vogelstein. Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome. eLife Sciences Publications, Ltd, 2023.
•  Michael Winding, Benjamin D Pedigo, Christopher L Barnes, Heather G Patsolic, Youngser Park, Tom Kazimiers, Akira Fushiki, Ingrid V Andrade, Avinash Khandelwal, Javier Valdes-Aleman, Feng Li, Nadine Randel, Elizabeth Barsotti, Ana Correia, Richard D Fetter, Volker Hartenstein, Carey E Priebe, Joshua T Vogelstein, Albert Cardona, and Marta Zlatic. The connectome of an insect brain. science, 2023.
•  B. D. Pedigo, M. Winding, C. E. Priebe, and J. T. Vogelstein. Bisected graph matching improves automated pairing of bilaterally homologous neurons from connectomes. Network Neuroscience, 2022.
•  T. L. Athey, D. J. Tward, U. Mueller, Vogelstein Joshua T, and M. I. Miller. Hidden Markov modeling for maximum probability neuron reconstruction. Communications Biology, 2022.
•  D. Kudithipudi, M. Aguilar-Simon, J. Babb, M. Bazhenov, D. Blackiston, J. Bongard, A. P. Brna, S. Chakravarthi Raja, N. Cheney, J. Clune, A. Daram, S. Fusi, P. Helfer, L. Kay, N. Ketz, Z. Kira, S. Kolouri, J. L. Krichmar, S. Kriegman, M. Levin, S. Madireddy, S. Manicka, A. Marjaninejad, B. McNaughton, R. Miikkulainen, Z. Navratilova, T. Pandit, A. Parker, P. K. Pilly, S. Risi, T. J. Sejnowski, A. Soltoggio, N. Soures, A. S. Tolias, D. Urbina-Meléndez, F. J. Valero-Cuevas, G. M. van de Ven, J. T. Vogelstein, F. Wang, R. Weiss, A. Yanguas-Gil, X. Zou, and H. Siegelmann. Biological underpinnings for lifelong learning machines. Nature Machine Intelligence, (3)4:196-210, 2022.
•  S. Li, T. Jun, J. Tyler, E. Schadt, Y. Kao, Z. Wang, M. F. Konig, C. Bettegowda, J. T. Vogelstein, N. Papadopoulos, R. E. Parsons, R. Chen, E. E. Schadt, L. Li, and W. K. Oh. Inpatient Administration of Alpha-1-Adrenergic Receptor Blocking Agents Reduces Mortality in Male COVID-19 Patients. Front. Med., 2022.
•  J. Poline, D. N. Kennedy, F. T. Sommer, G. A. Ascoli, D. C. Van Essen, A. R. Ferguson, J. S. Grethe, M. J. Hawrylycz, P. M. Thompson, R. A. Poldrack, S. S. Ghosh, D. B. Keator, T. L. Athey, J. T. Vogelstein, H. S. Mayberg, and M. E. Martone. Is Neuroscience FAIR? A Call for Collaborative Standardisation of Neuroscience Data. Neuroinformatics, 2022.
•  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. arXiv [cs.LG], 2022.