Pre-prints

1. K. P. Kording, A. Arkhipov, D. Deng, S. Escola, S. G. N. Grant, G. Haspel, M. Januszewski, N. Kasthuri, N. Khera, R. E. Kohman, G. Lindsay, J. Lunshof, A. Marblestone, D. A. Markowitz, J. Matelsky, B. Mensh, P. Mineault, A. Payne, J. Peng, X. Pitkow, P. Shiu, G. Schuhknecht, S. Truckenbrodt, J. T. Vogelstein, and E. S. Boyden. Compiling molecular ultrastructure into neural dynamics. arXiv, 2026.
2. A. Lubonja, J. Yoon, H. Xu, Y. Wan, Y. Xu, R. Stotz, M. Guillame-Bert, J. T. Vogelstein, and R. Burns. Vectorized Adaptive Histograms for Sparse Oblique Forests. arXiv, 2026.
3. Q. Wang, N. Randel, Y. Yin, C. Shand, A. Strange, M. Winding, A. Cardona, M. Zlatic, J. T. Vogelstein, and C. E. Priebe. Measuring the functional complexity of nanoscale connectomes: polarity matters. bioRxiv, 2025.
4. J. Chung, E. W. Bridgeford, M. Powell, D. Pisner, T. Xu, and J. T. Vogelstein. Are human connectomes heritable? bioRxiv, 2024.
5. C. Shen, S. Panda, and J. T. Vogelstein. Learning Interpretable Characteristic Kernels via Decision Forests. arXiv, 2023.
6. Eric W. Bridgeford, Jaewon Chung, Brian Gilbert, Sambit Panda, Adam Li, Cencheng Shen, Alexandra Badea, Brian Caffo, and Joshua T. Vogelstein. Learning sources of variability from high-dimensional observational studies. arXiv, 2023.
7. Haoyin Xu, Kaleab A. Kinfu, Will LeVine, Sambit Panda, Jayanta Dey, Michael Ainsworth and Yu-Chung Peng, Madi Kusmanov, Florian Engert, Christopher M. White, Joshua T. Vogelstein, and Carey E. Priebe. When are Deep Networks really better than Decision Forests at small sample sizes, and how? arXiv, 2021.
8. S. Panda, S. Palaniappan, J. Xiong, E. W. Bridgeford, Mehta, Ronak, C. Shen, and J. T. Vogelstein. hyppo: A Multivariate Hypothesis Testing Python Package. arXiv, 2021.