Блог Макса Данилова Форекс

R. Xiong, A. Koenecke, M. Powell, Z. Shen, J. T. Vogelstein, and S. Athey. "Federated Causal Inference in Heterogeneous Observational Data" Statistics in Medicine, [DOI]

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, [URL]

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, [URL]

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, [URL]

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, [DOI]

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), [DOI]

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 AlphaAdrenergic Receptor Blocking Agents Reduces Mortality in Male COVID Patients" Front. Med., [URL]

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, [URL]

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 [eunic-brussels.eu], [URL]

J. Chung, B. Varjavand, J. Arroyo-Relión, A. Alyakin, J. Agterberg, M. Tang, C. E. Priebe, and J. T. Vogelstein. "Valid two-sample graph testing via optimal transport Procrustes and multiscale graph correlation with applications in connectomics" Stat, (1)e,

T. Hartung, L. Smirnova, I. E. M. Pantoja, A. Akwaboah, D. A. E. Din, C. Berlinicke, J. L. Boyd, B. S. Caffo, B. Cappiello, T. Cohen-Karni, L. Curley, R. Etienne-Cummings, R. Dastgheyb, D. H. Gracias, F. Gilbert, C. W. Habela, F. Han, T. Harris, K. Herrmann, E. J. Hill, Q. Huang, R. E. Jabbour, E. C. Johnson, B. J. Kagan, C. Krall, A. Levchenko, P. Locke, A. Maertens, M. Metea, A. R. Muotri, R. Parri, B. L. Paulhamus, J. D. Plotkin, P. Roach, J. C. Romero, J. C. Schwamborn, F. Sille, A. Szalay, K. Tsaioun, D. Tornero, J. T. Vogelstein, K. Wahlin, and D. J. Zack. "The Baltimore Declaration toward the exploration of organoid intelligence" Frontiers in Science,

M. Powell, C. Clark, A. Alyakin, J. T. Vogelstein, and B. Hart. "Exploration of Residual Confounding in Analyses of Associations of Metformin Use and Outcomes in Adults With Type 2 Diabetes" JAMA Network Open, (11)5:e–e, [URL]

V. Chandrashekhar, D. J. Tward, D. Crowley, A. K. Crow, M. A. Wright, B. Y. Hsueh, F. Gore, T. A. Machado, A. Branch, J. S. Rosenblum, K. Deisseroth, and J. T. Vogelstein. "CloudReg: automatic terabyte-scale cross-modal brain volume registration" Nature Methods, [DOI]

M. Powell, A. Koenecke, J. Byrd, A. Nishimura, M. Konig, R. Xiong, S. Mahmood, V. B. Mucaj, L. Rose, S. Tamang, A. Sacarny, B. Caffo, S. Athey, E. Stuart, and J. Vogelstein. "Ten Rules for Conducting Retrospective Pharmacoepidemiological Analyses: Example COVID Study" Frontiers in Pharmacology, [DOI]

T. L. Athey, J. Teneggi, J. T. Vogelstein, D. Tward, U. Mueller, and M. I. Miller. "Fitting Splines to Axonal Arbors Quantifies Relationship between Branch Order and Geometry" Frontiers in Neuroinformatics, [URL]

A. Koenecke, M. Powell, R. Xiong, Z. Shen, N. Fischer, S. Huq, A. M. Khalafallah, M. Trevisan, P. Sparen, J. J. Carrero, A. Nishimura, B. Caffo, E. A. Stuart, R. Bai, V. Staedtke, D. L. Thomas, N. Papadopoulos, K. W. Kinzler, B. Vogelstein, S. Zhou, C. Bettegowda, M. F. Konig, B. Mensh, J. T. Vogelstein, and S. Athey. ""Alpha-1 adrenergic receptor antagonists to prevent hyperinflammation and death from lower respiratory tract infection",journal=Elife" None, [DOI]

C. Shen, S. Panda, and J. T. Vogelstein. "The Chi-Square Test of Distance Correlation" Journal of Computational and Graphical Statistics, (ja)–21, [DOI]

Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, and Joshua T. Vogelstein. "mvlearn: Multiview Machine Learning in Python" Journal of Machine Learning Research, (), [URL]

J. T. Vogelstein, E. W. Bridgeford, M. Tang, D. Zheng, C. Douville, R. Burns, and M. Maggioni. "Supervised dimensionality reduction for big data" Nature Communications, (), [DOI]

S. Li, T. Jun, Z. Wang, Y. Kao, E. Schadt, M. F. . B. Konig, J. T. Vogelstein, N. Papadopoulos, R. E. Parsons, and others. "COVID outcomes among hospitalized men with or without exposure to alphaadrenergic receptor blocking agents" Frontiers in Medicine, [URL]

S. Wang, J. Arroyo, J. T. Vogelstein, and C. E. Priebe. "Joint Embedding of Graphs" Transactions on Pattern Analysis and Machine Intelligence, [URL]

L. Rose, L. Graham, A. Koenecke, M. Powell, R. Xiong, Z. Shen, B. Mench, K. W. Kinzler, C. Bettegowda, B. Vogelstein, and others. "The association between Alpha-1 adrenergic receptor antagonists and in-hospital mortality from COVID" Frontiers in Medicine, [DOI]

M. P. Milham, J. T. Vogelstein, and T. Xu. "Removing the Reliability Bottleneck in Functional Magnetic Resonance Imaging Research to Achieve Clinical Utility" JAMA Psychiatry, [DOI]

J. Arroyo, A. Athreya, J. Cape, G. Chen, C. E. Priebe, and J. T. Vogelstein. "Inference for Multiple Heterogenous Networks with a Common Invariant Subspace" Journal of Machine Learning Research, (), [URL]

E. W. Bridgeford, S. Wang, Z. Wang, T. Xu, C. Craddock, J. Dey, G. Kiar, W. Gray-Roncal, C. Colantuoni, C. Douville, and others. "Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics" PLoS computational biology, (9)e, [URL]

R. M. Lawrence, E. W. Bridgeford, P. E. Myers, G. C. Arvapalli, S. C. Ramachandran, D. A. Pisner, P. F. Frank, A. D. Lemmer, A. Nikolaidis, and J. T. Vogelstein. "Standardizing human brain parcellations" Scientific data, (1)–9, [URL]

S. Hong, T. Xu, A. Nikolaidis, J. Smallwood, D. S. Margulies, B. Bernhardt, J. T. Vogelstein, and M. P. Milham. "Toward a connectivity gradient-based framework for reproducible biomarker discovery" NeuroImage, [DOI]

Ting Xu, Karl-Heinz Nenning, Ernst Schwartz, Seok-Jun Hong, Joshua T. Vogelstein, Alexandros Goulas, Damien A. Fair, Charles E. Schroeder, Daniel S. Margulies, Jonny Smallwood, Michael P. Milham, and Georg Langs. "Cross-species functional alignment reveals evolutionary hierarchy within the connectome" NeuroImage, [DOI]

J. W. Chow, A. Korchmaros, J. T. Vogelstein, M. P. Milham, and T. Xu. "Impact of concatenating fMRI data on reliability for functional connectomics" Neuroimage, [DOI]

Karl-Heinz Nenning, Ting Xu, Ernst Schwartz, Jesus Arroyo, Adelheid Woehrer, Alexandre R. Franco, Joshua T. Vogelstein, Daniel S. Margulies, Hesheng Liu, Jonathan Smallwood, Michael P. Milham, and Georg Langs. "Joint embedding: A scalable alignment to compare individuals in a connectivity space" NeuroImage, [DOI]

N. Wang, R. J. Anderson, D. G. Ashbrook, V. Gopalakrishnan, Y. Park, C. E. Priebe, Y. Qi, J. T. Vogelstein, R. W. Williams, and A. G. Johnson. "Variability and heritability of mouse brain structure: Microscopic MRI atlases and connectomes for diverse strains" NeuroImage (Cover Story), [DOI]

M. A. Haendel, C. G. Chute, T. D. Bennett, D. A. Eichmann, J. Guinney, W. A. Kibbe, P. R. O. Payne, E. R. Pfaff, P. N. Robinson, J. H. Saltz, H. Spratt, C. Suver, J. Wilbanks, A. B. Wilcox, A. E. Williams, C. Wu, C. Blacketer, R. L. Bradford, J. J. Cimino, M. Clark, E. W. Colmenares, P. A. Francis, D. Gabriel, A. Graves, R. Hemadri, S. S. Hong, G. Hripscak, D. Jiao, J. G. Klann, K. Kostka, A. M. Lee, H. P. Lehmann, L. Lingrey, R. T. Miller, M. Morris, S. N. Murphy, K. Natarajan, M. B. Palchuk, U. Sheikh, H. Solbrig, S. Visweswaran, A. Walden, K. M. Walters, G. M. Weber, X. T. Zhang, R. L. Zhu, B. Amor, A. T. Girvin, A. Manna, N. Qureshi, M. G. Kurilla, S. G. Michael, L. M. Portilla, J. L. Rutter, C. P. Austin, and K. R. Gersing. "The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment" Journal of the American Medical Informatics Association, [DOI]

C. Shen and J. T. Vogelstein. "The exact equivalence of distance and kernel methods in hypothesis testing" AStA Advances in Statistical Analysis, [DOI]

M. Madhyastha, G. Li, V. Strnadov-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, [DOI]

M. Schulz, B. T. Yeo, J. T. Vogelstein, J. Mourao-Miranda, J. N. Kather, K. Kording, B. Richards, and D. Bzdok. "Different scalling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets" Nat Commun, [DOI]

M. Schulz, B. T. Yeo, J. T. Vogelstein, J. Mourao-Miranada, J. N. Kather, K. Kording, B. Richards, and D. Bzdok. "Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets" Nat Commun, [DOI]

Z. Wang, H. Sair, C. Crainiceanu, M. Lindquist, B. A. Landman, S. Resnick, J. T. Vogelstein, and B. S. Caffo. "On statistical tests of functional connectome fingerprinting" The Canadian Journal of Statistics, [DOI]

A. S. Charles, B. Falk, N. Turner, T. D. Pereira, D. Tward, B. D. Pedigo, J. Chung, R. Burns, S. S. Ghosh, J. M. Kebschull, W. Silversmith, and J. T. Vogelstein. "Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics" Annual Review of Neuroscience, (1), [DOI]

Maximilian F. Konig, Mike Powell, Verena Staedtke, Ren-Yuan Bai, David L. Thomas, Nicole Fischer, Sakibul Huq, Adham M. Khalafallah, Allison Koenecke, Ruoxuan Xiong, Brett Mensh, Nickolas Papadopoulos, Kenneth W. Kinzler, Bert Vogelstein, Joshua T. Vogelstein, Susan Athey, Shibin Zhou, and Chetan Bettegowda. "Preventing cytokine storm syndrome in COVID using alpha-1 adrenergic receptor antagonists" The Journal of Clinical Investigation, (7), [DOI]

K. Mehta, R. F. Goldin, D. Marchette, J. T. Vogelstein, C. E. Priebe, and G. A. Ascoli. "Neuronal Classification from Network Connectivity via Adjacency Spectral Embedding" bioRxiv, [DOI]

G. Franca, M. Rizzo, and J. T. Vogelstein. "Kernel k-Groups via Hartigan's Method" IEEE Transactions on Pattern Analysis and Machine Intelligence, [DOI]

T. M. Tomita, J. Browne, C. Shen, J. Chung, J. L. Patsolic, B. Falk, J. Yim, C. E. Priebe, R. Burns, M. Maggioni, and J. T. Vogelstein. "Sparse Projection Oblique Randomer Forests" Journal of Machine Learninig Research, [URL]

S. Hong, J. T. Vogelstein, A. Gozzi, B. C. Bernhardt, B. T. Yeo, M. P. Milham, and A. D. Martino. "Toward Neurosubtypes in Autism" Biological Psychiatry, (1) - , [DOI]

A. Nikolaidis, A. S. Heinsfeld, T. Xu, P. Bellec, J. T. Vogelstein, and M. Milham. "Bagging Improves Reproducibility of Functional Parcellation of the Human Brain" NeuroImage, [URL]

E. W. Bridgeford, S. Wang, Z. Yang, Z. Wang, . Xu, C. Craddock, G. Kiar, W. Gray-Roncal, C. E. Priebe, B. Caffo, M. Milham, X. Zuo, (CoRR), and J. T. Vogelstein. "Optimal Experimental Design for Big Data: Applications in Brain Imaging" bioRxiv, [URL]

Y. Lee, C. Shen, C. E. Priebe, and J. T. Vogelstein. "Network dependence testing via diffusion maps and distance-based correlations" Biometrika, [DOI]

R. Perry, T. M. Tomita, J. Patsolic, B. Falk, and J. T. Vogelstein. "Manifold Forests: Closing the Gap on Neural Networks" arXiv, [URL]

J. Chung, B. D. Pedigo, E. W. Bridgeford, B. K. Varjavand, and J. T. Vogelstein. "GraSPy: Graph Statistics in Python" Journal of Machine Learning Research, ()–7, [URL]

J. T. Vogelstein, E. W. Bridgeford, B. D. Pedigo, J. Chung, K. Levin, B. Mensh, and C. E. Priebe. "Connectal Coding: Discovering the Structures Linking Cognitive Phenotypes to Individual Histories" Current Opinion in Neurobiology, [DOI]

C. E. Priebe, Y. Park, J. T. Vogelstein, J. M. Conroy, V. Lyzinski, M. Tang, A. Athreya, J. Cape, and E. Bridgeford. "On a two-truths phenomenon in spectral graph clustering" Proceedings of the National Academy of Sciences of the United States of America, (13)–, [DOI]

J. J. Son, J. C. Clucas, C. White, A. Krishnakumar, J. T. Vogelstein, M. P. Milham, and A. Klein. "Thermal sensors improve wrist-worn position tracking" npj digital medicine, [DOI]

H. Patsolic, S. Adali, J. T. Vogelstein, Y. . P. Park, G. Li, and V. Lyzinski. "Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability" arXiv, [URL]

J. T. Vogelstein, E. W. Bridgeford, Q. Wang, C. E. Priebe, M. Maggioni, and C. Shen. "Discovering and deciphering relationships across disparate data modalities" eLife, [DOI]

R. Tang, M. Ketcha, A. Badea, E. D. Calabrese, D. S. Margulies, J. T. Vogelstein, C. E. Priebe, and D. L. Sussman. "Connectome Smoothing via Low-rank Approximations" Transactions in Medical Imaging, [URL]

J. T. Vogelstein, E. Bridgeford, M. Tang, D. Zheng, R. Burns, and M. Maggioni. "Geometric Dimensionality Reduction for Subsequent Classification" arXiv, [URL]

C. Shen, C. E. Priebe, and J. T. Vogelstein. "From Distance Correlation to Multiscale Graph Correlation" Journal of the American Statistical Association, [URL]

J. T. Vogelstein, E. Perlman, B. Falk, A. Baden, W. Gray Roncal, V. Chandrashekhar, F. Collman, S. Seshamani, J. L. Patsolic, K. Lillaney, M. Kazhdan, R. Hider, D. Pryor, J. Matelsky, T. Gion, P. Manavalan, B. Wester, M. Chevillet, E. T. Trautman, K. Khairy, E. Bridgeford, D. M. Kleissas, D. J. Tward, A. K. Crow, B. Hsueh, M. A. Wright, M. I. Miller, S. J. Smith, R. J. Vogelstein, K. Deisseroth, and R. Burns. "A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data" Nature Methods, (11)–, [DOI]

A. Athreya, D. E. Fishkind, M. Tang, C. E. Priebe, Y. Park, J. T. Vogelstein, K. Levin, V. Lyzinski, Y. Qin, and D. L. Sussman. "Statistical Inference on Random Dot Product Graphs: a Survey" Journal of Machine Learning Research, [URL]

J. D. Cohen, L. Li, Y. Wang, C. Thoburn, B. Afsari, L. Danilova, C. Douville, A. A. Javed, F. Wong, A. Mattox, R. H. Hruban, C. L. Wolfgang, M. G. Goggins, M. D. Molin, T. L. Wang, R. Roden, A. P. Klein, J. Ptak, L. Dobbyn, J. Schaefer, N. Silliman, M. Popoli, J. T. Vogelstein, J. D. Browne, R. E. Schoen, R. E. Brand, J. Tie, P. Gibbs, H. L. Wong, A. S. Mansfield, J. Jen, S. M. Hanash, M. Falconi, P. J. Allen, S. Zhou, C. Bettegowda, L. A. Diaz, C. Tomasetti, K. W. Kinzler, B. Vogelstein, A. M. Lennon, and N. Papadopoulos. "Detection and localization of surgically resectable cancers with a multi-analyte blood test" Science, ()–, [DOI]

E. L. Dyer, W. G. Roncal, H. L. Fernandes, D. Gürsoy, V. De Andrade, R. Vescovi, K. Fezzaa, X. Xiao, J. T. Vogelstein, C. Jacobsen, K. P. Körding, and N. Kasthuri. "Quantifying Mesoscale Neuroanatomy Using X-Ray Microtomography" eNeuro, [DOI]

D. Durante, D. B. Dunson, and J. T. Vogelstein. "Rejoinder: Nonparametric Bayes Modeling of Populations of Networks" Journal of the American Statistical Association, [DOI]

D. Durante, D. B. Dunson, and J. T. Vogelstein. "Nonparametric Bayes Modeling of Populations of Networks" Journal of the American Statistical Association, ()–, [DOI]

G. Kiar, K. J. Gorgolewski, D. Kleissas, W. G. Roncal, B. Litt, B. Wandell, R. A. Poldrack, M. Wiener, R. J. Vogelstein, R. Burns, and J. T. Vogelstein. "Science in the cloud (SIC): A use case in MRI connectomics" GigaScience, (5)–10, [DOI]

N. Binkiewicz, J. T. Vogelstein, and K. Rohe. "Covariate-assisted spectral clustering" Biometrika, (2)–, [DOI]

S. Chen, K. Liu, Y. Yang, Y. Xu, S. Lee, M. Lindquist, B. S. Caffo, and J. T. Vogelstein. "An M-estimator for reduced-rank system identification" Pattern Recognition Letters, [DOI]

D. G. C. Hildebrand, M. Cicconet, R. M. Torres, W. Choi, T. M. Quan, J. Moon, A. W. Wetzel, A. Scott Champion, B. J. Graham, O. Randlett, G. S. Plummer, R. Portugues, I. H. Bianco, S. Saalfeld, A. D. Baden, K. Lillaney, R. Burns, J. T. Vogelstein, A. F. Schier, W. C. A. Lee, W. K. Jeong, J. W. Lichtman, and F. Engert. "Whole-brain serial-section electron microscopy in larval zebrafish" Nature, ()–, [DOI]

C. Shen, J. T. Vogelstein, and C. E. Priebe. "Manifold matching using shortest-path distance and joint neighborhood selection" Pattern Recognition Letters, [DOI]

A. K. Simhal, C. Aguerrebere, F. Collman, J. T. Vogelstein, K. D. Micheva, R. J. Weinberg, S. J. Smith, and G. Sapiro. "Probabilistic fluorescence-based synapse detection" PLoS Computational Biology, [DOI]

Q. Wang, M. Zhang, T. Tomita, J. T. Vogelstein, S. Zhou, N. Papadopoulos, K. W. Kinzler, and B. Vogelstein. "Selected reaction monitoring approach for validating peptide biomarkers" Proceedings of the National Academy of Sciences of the United States of America, (51)–, [DOI]

D. Zheng, D. Mhembere, V. Lyzinski, J. T. Vogelstein, C. E. Priebe, and R. Burns. "Semi-external memory sparse matrix multiplication for billion-node graphs" IEEE Transactions on Parallel and Distributed Systems, (5)–, [DOI]

L. Chen, C. Shen, J. T. Vogelstein, and C. E. Priebe. "Robust Vertex Classification" IEEE Transactions on Pattern Analysis and Machine Intelligence, (3)–, [DOI]

D. Koutra, N. Shah, J. T. Vogelstein, B. Gallagher, and C. Faloutsos. "DeltaCon: Principled Massive-Graph Similarity Function with Attribution" ACM Transactions on Knowledge Discovery from Data, [DOI]

V. Lyzinski, D. E. Fishkind, M. Fiori, J. T. Vogelstein, C. E. Priebe, and G. Sapiro. "Graph Matching: Relax at Your Own Risk" IEEE Transactions on Pattern Analysis and Machine Intelligence, (1)–73, [DOI]

R. D. Airan, J. T. Vogelstein, J. J. Pillai, B. Caffo, J. J. Pekar, and H. I. Sair. "Factors affecting characterization and localization of interindividual differences in functional connectivity using MRI" Human Brain Mapping, (5)–, [DOI]

C. E. Priebe, D. L. Sussman, M. Tang, and J. T. Vogelstein. "Statistical Inference on Errorfully Observed Graphs" Journal of Computational and Graphical Statistics, (4)–, [DOI]

L. Chen, J. T. Vogelstein, V. Lyzinski, and C. E. Priebe. "A Joint Graph Inference Case Study: the eunic-brussels.eus Chemical and Electrical Connectomes" Worm, [DOI]

W. R. Gray Roncal, D. M. Kleissas, J. T. Vogelstein, P. Manavalan, K. Lillaney, M. Pekala, R. Burns, R. J. Vogelstein, C. E. Priebe, M. A. Chevillet, and G. D. Hager. "An automated images-to-graphs framework for high resolution connectomics" Frontiers in Neuroinformatics, [DOI]

K. M. Harris, J. Spacek, M. E. Bell, P. H. Parker, L. F. Lindsey, A. D. Baden, J. T. Vogelstein, and R. Burns. "A resource from 3D electron microscopy of hippocampal neuropil for user training and tool development" Scientific Data, [DOI]

N. Kasthuri, K. J. Hayworth, D. R. Berger, R. L. Schalek, J. A. Conchello, S. Knowles-Barley, D. Lee, A. Vázquez-Reina, V. Kaynig, T. R. Jones, M. Roberts, J. L. Morgan, J. C. Tapia, H. S. Seung, W. G. Roncal, J. T. Vogelstein, R. Burns, D. L. Sussman, C. E. Priebe, H. Pfister, and J. W. Lichtman. "Saturated Reconstruction of a Volume of Neocortex" Cell, (3)–, [DOI]

V. Lyzinski, D. L. Sussman, D. E. Fishkind, H. Pao, L. Chen, J. T. Vogelstein, Y. Park, and C. E. Priebe. "Spectral clustering for divide-and-conquer graph matching" Parallel Computing, [DOI]

J. T. Vogelstein, J. M. Conroy, V. Lyzinski, L. J. Podrazik, S. G. Kratzer, E. T. Harley, D. E. Fishkind, R. J. Vogelstein, and C. E. Priebe. "Fast Approximate Quadratic programming for graph matching" PLoS ONE, [DOI]

J. T. Vogelstein and C. E. Priebe. "Shuffled Graph Classification: Theory and Connectome Applications" Journal of Classification, (1)–20, [DOI]

D. E. Carlson, J. T. Vogelstein, Q. Wu, W. Lian, M. Zhou, C. R. Stoetzner, D. Kipke, D. Weber, D. B. Dunson, and L. Carin. "Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling" IEEE Transactions on Biomedical Engineering, (1)–54, [DOI]

E. M. Sweeney, J. T. Vogelstein, J. L. Cuzzocreo, P. A. Calabresi, D. S. Reich, C. M. Crainiceanu, and R. T. Shinohara. "A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI" PLoS ONE, [DOI]

J. T. Vogelstein, Y. Park, T. Ohyama, R. A. Kerr, J. W. Truman, C. E. Priebe, and M. Zlatic. "Discovery of brainwide neural-behavioral maps via multiscale unsupervised structure learning" Science, ()–, [DOI]

N. C. Weiler, F. Collman, J. T. Vogelstein, R. Burns, and S. J. Smith. "Synaptic molecular imaging in spared and deprived columns of mouse barrel cortex with array tomography" Scientific Data, [DOI]

R. C. Craddock, S. Jbabdi, C. G. Yan, J. T. Vogelstein, F. X. Castellanos, A. Di Martino, C. Kelly, K. Heberlein, S. Colcombe, and M. P. Milham. "Imaging human connectomes at the macroscale" Nature Methods, (6)–, [DOI]

C. E. Priebe, J. Vogelstein, and D. Bock. "Optimizing the quantity/quality trade-off in connectome inference" Communications in Statistics - Theory and Methods, (19)–, [DOI]

J. T. Vogelstein, W. G. Roncal, R. J. Vogelstein, and C. E. Priebe. "Graph classification using signal-subgraphs: Applications in statistical connectomics" IEEE Transactions on Pattern Analysis and Machine Intelligence, (7)–, [DOI]

D. Dai, H. He, J. T. Vogelstein, and Z. Hou. "Accurate prediction of AD patients using cortical thickness networks" Machine Vision and Applications, (7)–, [DOI]

D. E. Fishkind, D. L. Sussman, M. Tang, J. T. Vogelstein, and C. E. Priebe. "Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown" SIAM Journal on Matrix Analysis and Applications, (1)–39, [DOI]

W. R. Gray, J. A. Bogovic, J. T. Vogelstein, B. A. Landman, J. L. Prince, and R. J. Vogelstein. "Magnetic Resonance Connectome Automated Pipeline: An Overview" IEEE Pulse, (2)–48, [DOI]

N. J. Roberts, J. T. Vogelstein, G. Parmigiani, K. W. Kinzler, B. Vogelstein, and V. E. Velculescu. "The predictive capacity of personal genome sequencing" Science Translational Medicine, [DOI]

S. B. Hofer, H. Ko, B. Pichler, J. Vogelstein, H. Ros, H. Zeng, E. Lein, N. A. Lesica, and T. D. Mrsic-Flogel. "Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex" Nature Neuroscience, (8)–, [DOI]

Y. Mishchencko, J. T. Vogelstein, and L. Paninski. "A Bayesian approach for inferring neuronal conectivity from calcium fluorescent imaging data" The annals of applied statistics, [DOI]

J. T. Vogelstein, R. J. Vogelstein, and C. E. Priebe. "Are mental properties supervenient on brain properties?" Scientific Reports, [DOI]

L. Paninski, Y. Ahmadian, D. G. Ferreira, S. Koyama, K. Rahnama Rad, M. Vidne, J. Vogelstein, and W. Wu. "A new look at state-space models for neural data" Journal of Computational Neuroscience, ()–, [DOI]

J. T. Vogelstein, A. M. Packer, T. A. Machado, T. Sippy, B. Babadi, R. Yuste, and L. Paninski. "Fast non-negative deconvolution for spike train inference from population calcium imaging" Journal of Neurophysiology, [DOI]

J. T. Vogelstein, B. O. Watson, A. M. Packer, R. Yuste, B. Jedynak, and L. Paninski. "Spike inference from calcium imaging using sequential Monte Carlo methods" Biophysical Journal, (2)–, [DOI]

R. J. Vogelstein, U. Mallik, J. T. Vogelstein, and G. Cauwenberghs. "Dynamically reconfigurable silicon array of spiking neurons with conductance-based synapses" IEEE Transactions on Neural Networks, (1)–, [DOI]

J. T. Vogelstein, L. H. Snyder, and D. E. Angelaki. "Accuracy of saccades to remembered targets as a function of body orientation in space" Journal of Neurophysiology, (1)–, [DOI]

D. L. Greenspan, D. C. Connolly, R. Wu, R. Y. Lei, J. T. Vogelstein, Y. T. Kim, J. E. Mok, N. Muñoz, F. X. Bosch, K. Shah, and K. R. Cho. "Loss of FHIT expression in cervical carcinoma cell lines and primary tumors" Cancer Research, [URL]

S. Panda, C. Shen, R. Perry, J. Zorn, A. Lutz, C. E. Priebe, and J. T. Vogelstein. "High-Dimensional and Universally Consistent K-Sample Tests" None, [DOI]

C. Shen, S. Panda, and J. T. Vogelstein. "Learning Interpretable Characteristic Kernels via Decision Forests" arXiv, [URL]

E. W. Bridgeford, M. Powell, G. Kiar, S. Noble, J. Chung, S. Panda, R. Lawrence, T. Xu, M. Milham, B. Caffo, and J. T. Vogelstein. "Batch Effects are Causal Effects: Applications in Human Connectomics" bioRxiv, [URL]

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, [URL]

J. Dey, W. LeVine, H. Xu, A. De Silva, T. M. Tomita, A. Geisa, T. Chu, J. Desman, and J. T. Vogelstein. "Deep Discriminative to Kernel Generative Networks for In- and Out-of-distribution Calibrated Inference" arXiv, [URL]

T. Xu, J. Cho, G. Kiar, E. W. Bridgeford, J. T. Vogelstein, and M. P. Milham. "A Guide for Quantifying and Optimizing Measurement Reliability for the Study of Individual Differences" bioRxiv, [URL]

Haoyin Xu, Jayanta Dey, Sambit Panda, and Joshua T. Vogelstein. "Simplest Streaming Trees" arXiv, [URL]

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, [URL]

Ronan Perry, Ronak Mehta, Richard Guo, Eva Yezerets, Jesús Arroyo, Mike Powell and Hayden Helm, Cencheng Shen, and Joshua T. Vogelstein. "Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities" arXiv, [URL]

S. Panda, S. Palaniappan, J. Xiong, E. W. Bridgeford, . Mehta, C. Shen, and J. T. Vogelstein. "hyppo: A Multivariate Hypothesis Testing Python Package" arXiv, [URL]

Ali Saad-Eldin, Benjamin D. Pedigo, Carey E. Priebe, and Joshua T. Vogelstein. "Graph Matching via Optimal Transport" arXiv, [URL]

Jaewon Chung, Bijan Varjavand, Jesus Arroyo, Anton Alyakin, Joshua Agterberg, Minh Tang, Joshua T. Vogelstein, and Carey E. Priebe. "Valid Two-Sample Graph Testing via Optimal Transport Procrustes and Multiscale Graph Correlation with Applications in Connectomics" arXiv, [URL]

Jayanta Dey, Ali Geisa, Ronak Mehta, Tyler M. Tomita, Hayden S. Helm, Eric Eaton, Jeffery Dick, Carey E. Priebe, and Joshua T. Vogelstein. "Towards a theory of out-of-distribution learning" arXiv, [URL]

V. Gopalakrishnan, J. Chung, E. Bridgeford, B. D. Pedigo, J. Arroyo, L. Upchurch, G. A. Johnsom, N. Wang, Y. Park, C. E. Priebe, and J. T. Vogelstein. "Multiscale Comparative Connectomics" arXiv, [URL]

Guodong Chen, Jesús Arroyo, Avanti Athreya, Joshua Cape, Joshua T. Vogelstein, Youngser Park, Chris White, Jonathan Larson, Weiwei Yang, and Carey E. Priebe. "Multiple Network Embedding for Anomaly Detection in Time Series of Graphs" arXiv, [URL]

M. Madhyastha, K. Lillaney, J. Browne, J. Vogelstein, and R. Burns. "PACSET (Packed Serialized Trees): Reducing Inference Latency for Tree Ensemble Deployment" arXiv, [URL]

S. Shen and C. Cencheng. "High-dimensional independence testing and maximum marginal correlation" arXiv, [URL]

Tyler M. Tomita and Joshua T. Vogelstein. "Robust Similarity and Distance Learning via Decision Forests" arXiv, [URL]

J. T. Vogelstein, J. Dey, H. S. Helm, W. LeVine, Mehta, Ronak D, T. M. Tomita, H. Xu, A. Geisa, Q. Wang, . M. van de Ven, and others. "Representation Ensembling for Synergistic Lifelong Learning with Quasilinear Complexity" arXiv, [URL]

R. Mehta, C. Shen, T. Xu, and J. T. Vogelstein. "A Consistent Independence Test for Multivariate Time-Series" arxiv, [URL]

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),

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, [URL]

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,

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, [DOI]

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, [DOI]

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, [DOI]

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, [DOI]

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, [DOI]

D. Zheng, D. Mhembere, J. T. Vogelstein, C. E. Priebe, and R. Burns. "FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs" PPoPP, [URL]

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), [DOI]

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, [URL]

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 , [DOI]

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, [DOI]

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, [DOI]

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, [DOI]

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" IEEE Global Conference on Signal and Information Processing, GlobalSIP - Proceedings, [DOI]

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" IEEE Global Conference on Signal and Information Processing, [DOI]

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, [DOI]

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, [URL]

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, [DOI]

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, [URL]

D. Koutra, J. T. Vogelstein, and C. Faloutsos. "DELTACON: A principled massive-graph similarity function" Proceedings of the SIAM International Conference on Data Mining, SDM , [DOI]

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, [DOI]

F. Petralia, J. Vogelstein, and D. B. Dunson. "Multiscale Dictionary Learning for Estimating Conditional Distributions" Advances in Neural Information Processing Systems, [URL]

Q. J. Huys, J. Vogelstein, and P. Dayan. "Psychiatry: Insights into depression through normative decision-making models" Advances in Neural Information Processing Systems, [URL]

J. T. Vogelstein, R. Vogelstein, and C. E. Priebe. "A Neurocognitive Graph-Theoretical Approach to Understanding the Relationship Between Minds and Brains" CSHL conference on Neural Circuits,

T. L. Athey, M. A. Wright, M. Pavlovic, V. Chandrashekhar, K. Deisseroth, M. I. Miller, and J. T. Vogelstein. "BrainLine: An Open Pipeline for Connectivity Analysis of Heterogeneous Whole-Brain Fluorescence Volumes" Neuroinformatics, [URL]

V. Chandrashekhar, D. J. Tward, D. Crowley, A. K. Crow, M. A. Wright, B. Y. Hsueh, F. Gore, T. A. Machado, A. Branch, J. S. Rosenblum, K. Deisseroth, and J. T. Vogelstein. "CloudReg: automatic terabyte-scale cross-modal brain volume registration" Nature Methods, [DOI]

H. S. Helm, R. D. Mehta, B. Duderstadt, W. Yang, C. M. White, A. Geisa, J. T. Vogelstein, and C. E. Priebe. "A partition-based similarity for classification distributions" arXiv, [URL]

Jaewon Chung, Eric Bridgeford, Jesus Arroyo, Benjamin D. Pedigo, Ali Saad-Eldin, Vivek Gopalakrishnan, Liang Xiang, Carey E. Priebe, and Joshua Vogelstein. "Statistical Connectomics" arXiv, [URL]

J. T. Vogelstein. "P-Values in a Post-Truth World" arXiv, [URL]

H. S. Helm, A. Basu, A. Athreya, Y. Park, J. T. Vogelstein, M. Winding, M. Zlatic, A. Cardona, P. Bourke, J. Larson, C. White, and C. E. Priebe. "Learning to rank via combining representations" arXiv, [URL]

C. E. Priebe, J. T. Vogelstein, F. Engert, and C. M. White. "Modern Machine Learning: Partition Vote" bioRxiv, [DOI]

Polina Golland, Jack Gallant, Greg Hager, Hanspeter Pfister, Christos Papadimitriou, Stefan Schaal, and Joshua T. Vogelstein. "A New Age of Computing and the Brain" arXiv, [URL]

Zeyi Wang, Eric Bridgeford, Shangsi Wang, Joshua T. Vogelstein, and Brian Caffo. "Statistical Analysis of Data Repeatability Measures" arXiv, [URL]

D. Mhembere, D. Zheng, J. T. Vogelstein, C. E. Priebe, and R. Burns. "Graphyti: A Semi-External Memory Graph Library for FlashGraph" arXiv, [URL]

H. Helm, J. V. Vogelstein, and C. E. Priebe. "Vertex Classification on Weighted Networks" arXiv, [URL]

J. Xiong, C. Shen, J. Arroyo, and J. T. Vogelstein. "Graph Independence Testing" arXiv, [URL]

D. Mhembere, D. Zheng, C. E. Priebe, J. T. Vogelstein, and R. Burns. "clusterNOR: A NUMA-Optimized Clustering Framework" arxiv, [URL]

A. Branch, D. Tward, J. T. Vogelstein, Z. Wu, and M. Gallagher. "An optimized protocol for iDISCO+ rat brain clearing, imaging, and analysis" bioRxiv, [DOI]

D. S. Greenberg, D. J. Wallace, K. Voit, S. Wuertenberger, U. Czubayko, A. Monsees, T. Handa, J. T. Vogelstein, R. Seifert, Y. Groemping, and J. N. Kerr. "Accurate action potential inference from a calcium sensor protein through biophysical modeling" bioRxiv, [DOI]

G. Kiar, E. Bridgeford, W. G. Roncal, (CoRR), V. Chandrashekhar, D. Mhembere, S. Ryman, X. Zuo, D. S. Marguiles, R. C. Craddock, C. E. Priebe, R. Jung, V. Calhoun, B. Caffo, R. Burns, M. P. Milham, and J. Vogelstein. "A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability" bioRxiv, [DOI]

G. Kiar, R. J. Anderson, A. Baden, A. Badea, E. W. Bridgeford, A. Champion, V. Chandrashekhar, F. Collman, B. Duderstadt, A. C. Evans, F. Engert, B. Falk, T. Glatard, W. R. G. Roncal, D. N. Kennedy, J. Maitin-Shepard, R. A. Marren, O. Nnaemeka, E. Perlman, S. Seshamani, E. T. Trautman, D. J. Tward, P. A. Valdés-Sosa, Q. Wang, M. I. Miller, R. Burns, and J. T. Vogelstein. "NeuroStorm: Accelerating Brain Science Discovery in the Cloud" arXiv, [URL]

S. Wang, C. Shen, A. Badea, C. E. Priebe, and J. T. Vogelstein. "Signal Subgraph Estimation Via Vertex Screening" arXiv, [URL]

G. Kiar, E. Bridgeford, V. Chandrashekhar, D. Mhembere, R. Burns, W. R. G. Roncal, and J. T. Vogelstein. "A comprehensive cloud framework for accurate and reliable human connectome estimation and meganalysis" bioRxiv, [URL]

R. Tang, M. Tang, J. T. Vogelstein, and C. E. Priebe. "Robust Estimation from Multiple Graphs under Gross Error Contamination" arXiv, [URL]

C. E. Priebe, Y. Park, M. Tang, A. Athreya, V. Lyzinski, J. T. Vogelstein, Y. Qin, B. Cocanougher, K. Eichler, M. Zlatic, and A. Cardona. "Semiparametric spectral modeling of the Drosophila connectome" arXiv, [URL]

D. Zheng, D. Mhembere, J. T. Vogelstein, C. E. Priebe, and R. Burns. "FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs" CoRR, abs/, [URL]

D. Zheng, R. Burns, J. Vogelstein, C. E. Priebe, and A. S. Szalay. "An SSD-based eigensolver for spectral analysis on billion-node graphs" arXiv, [URL]

D. Zheng, D. Mhembere, J. T. Vogelstein, C. E. Priebe, and R. Burns. "Flashmatrix: parallel, scalable data analysis with generalized matrix operations using commodity ssds" arXiv, [URL]

A. Sinha, W. Roncal, and N. Kasthuri. "Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes" arXiv, [URL]

M. Kazhdan, R. Burns, B. Kasthuri, J. Lichtman, J. Vogelstein, and J. Vogelstein. "Gradient-Domain Processing for Large EM Image Stacks" arXiv, [URL]

A. Banerjee, J. Vogelstein, and D. Dunson. "Parallel inversion of huge covariance matrices" arXiv, [URL]

Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, and Joshua T. Vogelstein. "The Value of Out-of-Distribution Data" Workshop on Out-of-Distribution Generalization in Computer Vision, European Conference on Computer Vision, [URL]

E. W. Bridgeford, D. Sussman, V. Lyzinski, Y. Qin, Y. Park, B. Caffo, C. E. Priebe, and J. T. Vogelstein. "What is Connectome Coding?" SfN course book, [URL]

J. Caplis and J. T. Vogelstein. "Glass box vs. black box" Pensions Investments,

J. T. Vogelstein, K. Amunts, A. Andreou, D. Angelaki, G. A. Ascoli, C. Bargmann, R. Burns, C. Cali, F. Chance, G. Church, H. Cline, T. Coleman, D. Stephanie de La Rochefoucauld, A. B. Elgoyhen, R. E. Cummings, A. Evans, K. Harris, M. Hausser, S. Hill, S. Inverso, C. Jackson, V. Jain, R. Kass, B. Kasthuri, A. Kepecs, G. Kiar, K. Kording, S. P. Koushika, J. Krakauer, S. Landis, J. Layton, Q. Luo, A. Marblestone, D. Markowitz, J. McArthur, B. Mensh, M. P. Milham, P. Mitra, P. Neskovic, M. Nicolelis, R. O'Brien, A. Oliva, G. Orban, H. Peng, E. Perlman, M. Picciotto, M. Poo, J. Poline, A. Pouget, S. Raghavachari, J. Roskams, A. P. Schaffer, T. Sejnowski, F. T. Sommer, N. Spruston, L. Swanson, A. Toga, R. J. Vogelstein, A. Zador, R. Huganir, and M. I. Miller. "Grand challenges for global brain sciences" FResearch, [DOI]

J. T. Vogelstein, B. Mensh, M. Häusser, N. Spruston, A. C. Evans, K. Kording, K. Amunts, C. Ebell, J. Muller, M. Telefont, S. Hill, S. P. Koushika, C. Calì, P. A. Valdés-Sosa, P. B. Littlewood, C. Koch, S. Saalfeld, A. Kepecs, H. Peng, Y. O. Halchenko, G. Kiar, M. M. Poo, J. B. Poline, M. P. Milham, A. P. Schaffer, R. Gidron, H. Okano, V. D. Calhoun, M. Chun, D. M. Kleissas, R. J. Vogelstein, E. Perlman, R. Burns, R. Huganir, and M. I. Miller. "To the Cloud! A Grassroots Proposal to Accelerate Brain Science Discovery" Neuron, (3)–, [DOI]

R. Burns, J. T. Vogelstein, and A. S. Szalay. "From cosmos to connectomes: The evolution of data-intensive science" Neuron, (6)–, [DOI]

P. Golland, J. Gallant, G. Hager, H. Pfister, C. Papadimitriou, S. Schaal, and J. T. Vogelstein. "A New Age of Computing and the Brain: Report of the CCC Brain Workshop" CCC Brain Workshop, [URL]

V. Vogelstein and J. T. "Q and A: What is the Open Connectome Project?" Neural Systems and Circuits, [DOI]

R. Yuste, J. MacLean, J. Vogelstein, and L. Paninski. "Imaging action potentials with calcium indicators" Cold Spring Harbor Protocols, (8)–, [DOI]

V. Vogelstein and J. T. "Oopsi: a family of optimal optical spike inference algorithms for inferring neural connectivity from population calcium imaging" Learning, [URL]

J. T. Vogelstein, J. V. Vogelstein, and B. Vogelstein. "NIH Grant Application Testing the effects of genetic variations using MINIME technology" Science, ()–, [DOI]

PI: F. Engert
Role on Project: Co-Investigator
Term: Sep to Aug
Funding to lab, entire period: $, (total)
Funding to lab, current year:

Johns Hopkins University will be responsible for developing all algorithms and software in support of the Atlas project, as well as running the Data Core. This will include writing software to store, manage, and visualize the data, as well as algorithms for scalable analysis and support of modeling.

PI: C. Shen
Role on Project: Co-Investigator
Term: Sep to Aug
Funding to lab, entire period: $73, (total)
Funding to lab, current year:

Goal of this project is methodological development, theoretical investigation, and simulation and real data experimentation toward the end goal of principled understanding and advancement of the mathematics and science of graph neural network

PI: T. Xu
Role on Project: Co-Investigator
Term: Sep to Aug
Funding to lab, entire period: $, (total)
Funding to lab, current year:

We will continue collecting, organizing, and analyzing another cohort of the NKI-Rockland Sample.

PI: M. Milham
Role on Project: Co-Investigator
Term: Jul to Apr
Funding to lab, entire period: $3,, (total)
Funding to lab, current year:

The major goal is to establish multimodal MRI and electrophysiology lifespan sample to open and prospectively share with the larger scientific community.

PI: R. Vidal
Role on Project: Co-Investigator
Term: Sep to Aug
Funding to lab, entire period: $1,, (direct)
Funding to lab, current year: $, (direct)

The goal of this project is to develop a mathematical, statistical and computational frame- work that helps explain the success of current network arcitectures, understand its pitfalls, and guide the design of novel architectures with guaranteed confidence, robustness, inter- pretability, optimality, and transferability

PI: M. Powell
Role on Project: Co-Investigator
Term: Aug to current
Funding to lab, entire period: N/A
Funding to lab, current year: N/A

This project will conduct federated retrospective analyses designed to assess the benefit of off-label drug use by pooling multiple disparate databases, to help prioritize and guide subsequent initiation and recruitment of randomized clinical trials. This will include evaluating the impact of the target drugs on patient outcomes from diseases similar to COVID, such as pneumonia or acute respiratory distress, generating artificial datasets using generative adversarial networks to asses performance of methods when 'ground truth' is known, applying the best methods to analyze the effect of the target drugs on the outcomes of COVID patients across hospital systems, and using the results to evaluate the potential of these drugs and suggest guidelines for clinical trials.

PI: J. Vogelstein
Role on Project: Principal Investigator
Term: Jul to Jun
Funding to lab, entire period: $, (direct) $1,, (total)
Funding to lab, current year: $, (direct) $, (total)

The goal of this project is to establish a state-of-the-art toolbox for analysis of connectomes, spanning taxa, scale, and complexity. we will develop and extend implementations to enable neurobiologists to (1) estimate latent structure from attributed connectomes, (2) identify meaningful clusters among populations of connectomes, and (3) detect relationships be- tween connectomes and multivariate phenotypes

PI: K. Harris
Role on Project: Co-Investigator
Term: Apr to Mar
Funding to lab, entire period: $, (direct) $, (total)
Funding to lab, current year: $, (direct) $, (total)

The goal is to develop the requisite technology to understand the impact of synaptic weight on functional networks

PI: J. Vogelstein
Role on Project: Principal Investigator
Term: Jan to Dec
Funding to lab, entire period: $, (total) $, (direct)
Funding to lab, current year: $, (total) $76, (direct)

The goal is to establish foundaitonal theory and methods for analyzing populations of attributed connectomes

PI: A. Badea
Role on Project: Co-Investigator
Term: Dec to Nov
Funding to lab, entire period: N/A
Funding to lab, current year: $,

The goal of this grant is to generate connectomes and RNA-seq transcriptomes to characterize and differentiate APOE mice as a model of aging

PI: J. Vogelstein
Role on Project: Principal Investigator
Term: Unrestricted Gift
Funding to lab, entire period: $50, (total)
Funding to lab, current year: N/A

Research and development of neuroscience and connectomes around neuronal circuit and system modeling, application of time-series-of-graphs and dynamics to neuronal signaling analysis and connectomes, and in the abstractions of matter, math, machines that point toward complex systems composed of low-level components

PI: K. Kording
Role on Project: Co-Investigator
Term: Oct to Jul
Funding to lab, entire period: N/A
Funding to lab, current year: \$79, (direct)

The goal of this project is to plan an AI institution via several meetings and workshops

PI: M. Muller
Role on Project: Co-Investigator
Term: Sep to Aug
Funding to lab, entire period: \$1,, (total) \$, (direct)
Funding to lab, current year: \$, (total) \$, (direct)

The overall goal of the proposal is to develop technologies for the brain wide reconstruction of axonal arbors of molecularly defined neurons. The proposal aims at overcoming barriers in neuronal labeling, imaging and computation to achieve this goal, and to develop a technology platform that can be scaled to all neurons of the brain

PI: T. Satterthwaite
Role on Project: Co-Investigator
Term: Aug to May
Funding to lab, entire period: \$, (total) \$, (direct)
Funding to lab, current year: N/A

Aggregate, harmonize, and analyze existing large-scale pediatric neuroimaging datasets to identify normative and clinical brain growth curves

PI: E. Schulman
Role on Project: Co-Investigator
Term: Jul to Jun
Funding to lab, entire period: \$, (total) \$, (direct)
Funding to lab, current year: \$87, (total) \$57, (direct)

Provide neuroscience and machine learning expertise to guide the design of the computa- tional learning capabilities of the system

PI: J. Vogelstein
Role on Project: Principal Investigator
Term: Jan to Dec
Funding to lab, entire period: \$, (total)
Funding to lab, current year: N/A

Study learning and plasticity at an unprecedented scale, revealing the dynamics of large populations of synapses comprising an entire local cortical circuit. No previously conducted experiment could answer the questions about the dynamics of large populations of synapses, which is crucial to understanding the learning process

PI: A. Tolias
Role on Project: Co-Investigator
Term: Nov to Oct
Funding to lab, entire period: \$, (total) \$, (direct)
Funding to lab, current year: \$, (total) \$, (direct)

Develop the pre-processing analysis pipeline for the imaging data collected in this project

PI: J. Vogelstein
Role on Project: Principal Investigator
Term: Nov to Oct
Funding to lab, entire period: \$1,, (total) \$1,, (direct)
Funding to lab, current year: \$, (total) \$, (direct)

Lifelong Learning Forests (L2Fs) will learn continuously, selectively adapting to new environ- ments and circumstances utilizing top-down feedback to impact low-level processing, with provable statistical guarantees, while maintaining computational tractability at scale

PI: F. Engert
Role on Project: Co-Investigator
Term: Sep to Aug
Funding to lab, entire period: \$1,, (total) \$, (direct) (JHU sub-award)
Funding to lab, current year: \$, (total) \$, (direct)

Generate a realistic multiscale circuit model of the larval zebrafish’s brain – the multiscale virtual fish (MSVF). The model will span spatial ranges from the nanoscale at the synaptic level, to local microcircuits to inter-area connectivity - and its ultimate purpose is to explain and simulate the quantitative and qualitative nature of behavioral output across various timescales

PI: J. Vogelstein
Role on Project: Principal Investigator
Term: Sep to Aug (No Cost Extension)
Funding to lab, entire period: \$, (total) \$, (direct)
Funding to lab, current year: \$, (total) \$, (direct)

We propose to lower the barrier to connecting data to analyses and models by providing a coherent cloud computational ecosystem that minimizes current bottlenecks in the scientific process

PI: J. MRatnanather
Role on Project: Co-Investigator
Term: Jul to Jun
Funding to lab, entire period: \$, (total) \$, (direct)
Funding to lab, current year: N/A

Create a robust computational framework for analyzing the cortical ribbon in a specific region: the auditory cortex

PI: S. Cencheng
Role on Project: Co-Investigator
Term: May to Apr
Funding to lab, entire period: \$, (total) \$, (direct)
Funding to lab, current year: N/A

Establish a unified methodology framework for statistical testing in high-dimensional, noisy, big data, through theoretical advancements, comprehensive simulations, and real data experiments

PI: J. Vogelstein
Role on Project: Principal Investigator
Term: to
Funding to lab, entire period: \$,
Funding to lab, current year: N/A

We propose to lower the barrier to connecting data to analyses and models by providing a coherent cloud computational ecosystem that minimizes current bottlenecks in the scientific process

PI: J. Vogelstein
Role on Project: Principal Investigator
Term: to
Funding to lab, entire period: \$50, (total) \$50, (direct)
Funding to lab, current year: N/A

Take the first few steps towards building the international brain station

PI: B. Miller
Role on Project: Co-Investigator
Term: to
Funding to lab, entire period: \$, (total) \$, (direct)
Funding to lab, current year: N/A

The BrainLab CI prototype system will deploy an experimental-management infrastruc- ture that allows users to construct community-wide experiments that implement data and metadata controls on the inclusion and exclusion of data

PI: J. Vogelstein
Role on Project: Principal Investigator
Term: to
Funding to lab, entire period: \$92, (total) \$56, (direct)
Funding to lab, current year: N/A

Characterize the statistical properties of the individual graphs, to identify circuit motifs, both that specialize in a species specific fashion, and that are preserved across species. As a test, will compare the connectomes of sea lions and coyotes

PI: C. Priebe
Role on Project: Co-Investigator
Term: Oct to Sep
Funding to lab, entire period: \$4,, (total) \$2,, (direct)
Funding to lab, current year: N/A

Develop theory and methods for generating a discoverable archive of data modeling primi- tives and for automatically selecting model primitives and for composing selected primitives into complex modeling pipelines based on user-specified data and outcome(s) of interest

PI: J. Vogelstein
Role on Project: Principal Investigator
Term: to
Funding to lab, entire period: \$97, (total) \$97, (direct)
Funding to lab, current year:

This travel grant is for the expressed purposes of gathering researchers from around the globe to discuss the new way to further brain research during part one of a two day conference

PI: J. Vogelstein
Role on Project: Principal Investigator
Term: to
Funding to lab, entire period: \$16, (total) \$14, (direct)
Funding to lab, current year: N/A

This travel grant is for the expressed purposes of gathering researchers from around the globe to further discuss advancements in brain research during the second part of a two day conference

PI: J. Vogelstein
Role on Project: Principal Investigator
Term: Sep to Aug
Funding to lab, entire period: \$2,, (total) \$1,, (direct)
Funding to lab, current year: N/A

Multiple, large, multifarious brain imaging datasets are rapidly becoming standards in neuroscience. Yet, we lack the tools to analyze individual datasets, much less populations thereof. Therefore, we will develop theory and methods to analyze and otherwise make such data available

PI: R. Burns
Role on Project: Co-Investigator
Term: to
Funding to lab, entire period: \$39, (total) \$28, (direct)
Funding to lab, current year: N/A

Build software infrastructure to enable analytics on billion node, terabyte sized networks using commodity hardware

PI: S. Smith
Role on Project: Co-Investigator
Term: to
Funding to lab, entire period: \$, (total) \$, (direct)
Funding to lab, current year: N/A

The major goals of this project are to discover the synaptic diversity and complexity in mammalian brains, specifically comparing and contrasting humans with mice, the leading experimental animal

PI: R. Burns
Role on Project: Co-Investigator
Term: to
Funding to lab, entire period: \$70, (total) \$46, (direct)
Funding to lab, current year: N/A

Develop cyberinfrastructure to support management, visualization, storage, and analysis of large-scale electron microscopy data

J. T. Vogelstein. "Surprise! IID++ Out of Distribution & Prospective Learning" Simons Foundation, New York, NY,

B. D. Pedigo. "Hypothesis testing for connectome comparisons: a statistical analysis of bilateral symmetry in an insect brain connectome" Drexel University, Philadelphia, PA,

Ali Geisa. "Towards a theory of out-of-distribution learning" JHU BME, Baltimore, MD, USA,

E. Bridgeford. "Eliminating Accidental Deviations in Human Connectomics" JHU BME, Baltimore, MD, USA,

J. Chung. "Heritablity of Human Structural Connectomes" JHU BME, Baltimore, MD, USA,

J. Dey. "Omnidirectional Lifelong Learning via Ensembling Representations" JHU BME, Baltimore, MD, USA,

J. T. Vogelstein. "FIRM Guiding Principles for scientific software development and stewardship" JHU BME, Baltimore, MD, USA,

J. T. Vogelstein. "Jovo++" JHU BME, Baltimore, MD, USA,

J. T. Vogelstein. "Reality Transurfing: Chapter 1" JHU BME, Baltimore, MD, USA,

J. T. Vogelstein. "Lifelong Learning: Theory and Practice" Darpa L2M PI Meeting,

J. T. Vogelstein. "Lifelong Learning and Beyond" Darpa L2M PI Meeting,

J. T. Vogelstein. "Lifelong Learning: Theory and Context" Darpa L2M PI Meeting,

J. T. Vogelstein. "Lifelong Learning: Theory and Practice and Coresets" Darpa L2M PI Meeting,

J. T. Vogelstein. "Lifelong Learning" North Carolina State University, Raleigh, NC, USA,

J. T. Vogelstein. "Lifelong Learning" Morgan State University, Baltimore, MD, USA,

J. T. Vogelstein. "Lifelong Learning: Moving Beyond Avoiding Catastrophic Forgetting" Johns Hopkins Mathematical Institute for Data Science, Baltimore, MD, USA,

J. T. Vogelstein. "Open Access to the Brain: a Computer "Connectome" Links Brain Images in Fine Detail" JHM Boot Camp, Baltimore, MD, USA,

J. T. Vogelstein. "Big Biomedical Data Science" Sol Goldman International Conference, Baltimore, MD, USA,

J. T. Vogelstein. "Journey to Here" JHU BMES talks, Baltimore, MD, USA,

J. T. Vogelstein. "NeuroData (Science)" Kavli, Baltimore, MD, USA,

J. T. Vogelstein. "NeuroData Tools" NeuroData Hackashop, Baltimore, MD, USA,

J. T. Vogelstein. "Biomedical Big Data and Data Science" JHU BME, Baltimore, MD, USA,

J. T. Vogelstein. "Data Intensive Brain Science" Kavli Neuroscience Discovery Institute, Baltimore, MD, USA,

J. T. Vogelstein. "Using Big Data Science to Understand What Goes On in our Heads" SOHOP Faculty Spotlight, Baltimore, MD, USA,

J. T. Vogelstein. "Engineering the Future of Medicine: Data Intensive Biomedical Science" Johns Hopkins University Biomedical Engineering, Baltimore, MD, USA,

J. T. Vogelstein. "Data Coordination and Data Resources for the BRAIN Initiative" 4th Annual BRAIN Initiative Investigators Meeting, Rockville, MD, USA,

J. T. Vogelstein. "The International Brain Station (TIBS)" JHU BME and Tsinghua University, Baltimore, MD, USA,

J. T. Vogelstein. "Using Big Data Science to Understand What Goes on in Our Heads" SOHOP Faculty Spotlight, Baltimore, MD, USA,

J. T. Vogelstein. "Challenges and Opportunities in Big Data for Neuroscientists" Society for Neuroscience: DC Metro Area Chapter Keynote Address, Washington, DC, USA,

J. T. Vogelstein. "Opportunities and Challenges in Big Data Neuroscience" Society for Neuroscience, Washington D.C., USA,

J. T. Vogelstein. "NeuroStorm" Global Brain Workshop 2 JHU, Baltimore, MD, USA,

J. T. Vogelstein. "The International Brain Station (TIBS)" United Nations Global Brain Workshop Meeting, Baltimore, MD, USA,

J. T. Vogelstein. "Using Big Data Science to Understand What Goes on in Our Heads" SOHOP Faculty Spotlight, Baltimore, MD, USA,

J. T. Vogelstein. "The International Brain Station (TIBS)" Kavli Foundation, Baltimore, MD, USA,

J. T. Vogelstein. "NeuroData " NeuroData Lab Retreat,

J. T. Vogelstein. "Global Brain Workshop " Global Brain Workshop NSF+JHU at Kavli, Baltimore, MD, USA,

J. T. Vogelstein. "Global Brain Workshop " Kavli Neuroscience Discovery Institute & Center for Imaging Science, Baltimore, MD, USA,

J. T. Vogelstein. "Learning a Data-Driven Nosology:Progress, Challenges & Opportunities" Kavli Neuroscience Discovery Institute & Center for Imaging Science, Baltimore, MD, USA,

J. T. Vogelstein. "NeuroData:Enabling Terascale Neuroscience" Kavli Neuroscience Discovery Institute & Center for Imaging Science, Baltimore, MD, USA,

J. T. Vogelstein. "NeuroData:Enabling Terascale Neuroscience" JHU Kavli Neuroscience Discovery Institute, Baltimore, MD, USA,

J. T. Vogelstein, M. I. Miller, and R. Hunganir. "Global Brain Workshop " Kavli Neuroscience Discovery Institute & Center for Imaging Science @ JHU, Baltimore, MD, USA,

J. T. Vogelstein. "Special Symposium: Neuroscience in the 21st Century" Kavli, Baltimore, MD, USA,

J. T. Vogelstein. "Open Connectome Project: Lowering the Barrier to Entry of Big Data Neuroscience" Institute for Computational Medicine at Johns Hopkins University, Baltimore, MD, USA,

J. T. Vogelstein. "Open Source Platform for Heterogenous Brain Data" figshare,

J. T. Vogelstein. "Big (Neuro) Statistics" Kavli Salon, Chicago, IL, USA,

J. T. Vogelstein. "Open-Science Platform for Heterogeneous Brain Data: Opportunities and Challenges" Kavli, Baltimore, MD, USA,

J. T. Vogelstein. "Big (Neuro) Statistics" Kavli Salon, Baltimore, MD, USA,

J. T. Vogelstein. "Decision Theoretic Approach to Statistical Inference" Guest Lecture in Current Topics in Machine Learning, Johns Hopkins University, Baltimore, MD, USA,

J. T. Vogelstein. "Once we get connectomes, what the \%\#* are we going to do with them?" Institute of Neuroinformatics, Boston, MA, USA,

J. T. Vogelstein. "Inferring spike times given typical time-series fluorescence observations" Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA,

Qingyang Wang. "Why do networks need negative weights?" None,

E. Bridgeford. "Simulating a Realization of a Stochastic Block Model" ABCD-ReproNim Program,

E. Bridgeford. "Community Detection and Model Selection in SBMs" ABCD-ReproNim Program,

S. Panda, C. Shen, and J. T. Vogelstein. "Nonparametric MANOVA via Independence Testing" Global Young Scientists Summit,

B. D. Pedigo, M. Winding, M. Zlatic, A. Cardona, C. E. Priebe, and J. T. Vogelstein. "Maggot brain, mirror image? A statistical analysis of bilateral symmetry in an insect brain connectome" Neuromatch ,

B. D. Pedigo and J. T. Vogelstein. "graspologic: A python package for rigorous statistical analysis of populations of attributed connectomes" BRAIN Informatics Webinar,

S. Panda, C. Shen, and J. T. Vogelstein. "Nonparametric MANOVA via Independence Testing" BRAIN Initiative Meeting,

B. D. Pedigo. "Network data science for bilateral brains: Applications in the larval Drosophila connectome" NIH & DOE Brain Connectivity Workshop Series,

J. T. Vogelstein. "OOD DARPA Presentation" DARPA,

J. T. Vogelstein. "Lifelong Learning and Beyond" DARPA L2M,

J. Vogelstein. "The role of the connectome in achieving artificial general intelligence" Yale School of Medicine, Whistler Scientific Workshop, Whistler, BC, Canada,

J. Vogelstein. "Lifelong Learning via Ensembling General Representations" None,

H. Helm, R. Mehta, C. E. Priebe, R. Arora, and J. T. Vogelstein. "A Theory and Practice of Lifelong Learnable Forest" Kavli Neural Systems Institute, Rockefeller University, New York City, NY, USA,

J. T. Vogelstein. "Lifelong Learning" Columbia University, New York City, NY, USA,

J. T. Vogelstein. "Ailey in an Hour: (A "Soup-to-Nuts" Pipeline for Analysis of Whole Cleared Brain Data)" NeuroNex, Cornell University, Ithaca, NY, USA,

J. T. Vogelstein, H. Helm, R. Mehta, C. E. Priebe, and R. Arora. "A Theory and Practice of the Lifelong Learnable" L2M,

J. T. Vogelstein and R. Burns. "Data Science Core" Harvard University, Carmridge, MA, USA,

J. Chung. "Statistical Methods for Population of Connectomes" Organization of Human Brain Mapping, Rome, Italy,

J. Browne. "Forest Packing: Fast Parallel, Decision Forests" SIAM International Conference on Data Mining, Calgary, Alberta, Canada,

D. Tward. "Brain mapping tools for neuroscience research" NeuroNex, Cornell University, Ithaca, NY, USA,

J. T. Vogelstein. "Big Data and the Life Sciences" Sloan Foundation, New York City, NY, USA,

J. T. Vogelstein. "Statistical Foundations For Connectomics" Max Planck / HHMI Connectomics Meeting, Berlin, Germany,

J. T. Vogelstein. "Connectal Coding" Dipy Workshop, Bloomington, Indiana, USA,

J. T. Vogelstein. "Lifelong Learning Forests" L2M,

J. T. Vogelstein. "Connectome Coding" Society for Neuroscience, San Diego, CA, USA,

J. T. Vogelstein. "NeuroData: A Community-developed open-source computational ecosystem for big neuro data" NeuroNex, Cornell University, Ithaca, NY, USA,

J. T. Vogelstein. "A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data" Princeton University, Princeton, NJ, USA,

J. T. Vogelstein. "Multiscale Graph Correlation: A Knowledge Representation System for Discovering Latent Geometric Structure" DARPA SIMPLEX PI Review Meeting, New York City, NY, USA,

E. W. Bridgeford. "A High-Throughput Pipeline Identifies Robust Connectomes but Troublesome Variability" Organization of Human Brain Mapping, Suntec, Singapore,

E. Perlman. "NeuroData: Embracing Open Source for Big Data Neuroscience" NSF NeuroNex Workshop on Super 3DEM, Austin, TX, USA,

J. T. Vogelstein and V. Chandrashekhar. "NeuroNex + Stanford" NeuroNex-Stanford, Stanford, CA, USA,

G. Kiar. "Connectome Coding: what is it, how do we do it, and why do we care?" Data science in Neuroscience Symposium, Suntec, Singapore,

J. T. Vogelstein. "Lifelong Learning Forests" Darpa L2M PI Meeting, Arlington, VA, USA,

J. T. Vogelstein. "Discovering Relationships and their Geometry Across Disparate Data Modalities" Yale University, New Haven, CT, USA,

J. T. Vogelstein. "Connectome Coding" Schmidt Sciences,

J. T. Vogelstein. "Discovering Relationships and their Geometry Across Disparate Data Modalities" Stanford University, Stanford, CA, US,

D. Mhembere. "knor: a NUMA-Optimized In-Memory, Distributed and Semi-External-Memory k-means library" HPDC, Washington DC, USA,

G. Kiar. "Science in the Cloud (SIC): A use-case in MRI Connectomics" Open Science Special Interest Group, Oxford University, Oxford, England,

Y. Lee. "Network Dependence Testing via Diffusion Maps and Distance-Based Correlations" Joint Statistical Meetings, Baltimore, MD, USA,

T. M. Tomita. "ROFLMAO: Robust Oblique Forests with Linear Matrix Operations" SIAM International Conference on Data Mining, Houston, TX, USA, [DOI]

J. T. Vogelstein. "NeuroData: Enabling Terascale Neuroscience for Everyone" 3rd Annual BRAIN Iniative Investigators Meeting, Bethesda, MD, USA,

C. Shen. "Multiscale Generalized Correlation" Joint Statistical Meeting, Chicago, IL, USA,

J. T. Vogelstein. "NeuroData: Enabling Terascale Neuroscience for Everyone" Keystone Symposia: State of the Brain, Alpbach, Austria,

C. Shen. "Local Distance Correlation for Testing Independence" Temple University, Philadelphia, PA, USA,

J. T. Vogelstein. "Law of Large Graphs" DARPA Graphs, Columbia University, New York City, NY, USA,

J. T. Vogelstein. "Research Computing Support for Neuroscience and Other Life Sciences" CASC, Aachen, Germany,

J. T. Vogelstein. "From RAGs to Riches: Utilizing Richly Attributed Graphs to Reason from Heterogeneous Data" SIMPLEX Kickoff, New York City, NY, USA,

J. T. Vogelstein. "From RAGs to Riches: Utilizing Richly Attributed Graphs to Reason from Heterogeneous Data: Part 1" DARPA SIMPLEX PI Meeting, New York City, NY, USA,

J. T. Vogelstein. "From RAGs to Riches: Utilizing Richly Attributed Graphs to Reason from Heterogeneous Data: Part 2" DARPA SIMPLEX PI Meeting, New York City, NY, USA,

J. T. Vogelstein. "Opportunities and Challenges in Big Data Neuroscience" DoE,

J. T. Vogelstein and L. Paninski. "Spike inference from calcium imaging using sequential Monte Carlo methods" AMSI Program on Sequential Monte Carlo,

J. T. Vogelstein. "big time (series data in neuroscience)" figshare,

J. T. Vogelstein. "Top Challenges of Big Data Neuroscience" BRAIN Initiative Workshop, Bethesda, MD, USA,

J. T. Vogelstein. "Big Statistics for Brain Sciences" Baylor College of Medicine, Department of Neuroscience, Houston, TX, USA,

J. T. Vogelstein. "Beyond Little Neuroscience" Beyond Optogenetics workshop at Cosyne, Salt Lake City, UT, USA,

J. T. Vogelstein. "Statistical Inference on Graphs" University of Michigan, Ann Arbor, Michigan,

J. T. Vogelstein. "Statistical Inference on Graphs" Scientific Computing Institute, University of Utah, Salt Lake City, UT, USA,

J. T. Vogelstein. "Open Problems in Neuropsychiatry" Data Seminar, Duke University, Durham, NC, USA,

J. T. Vogelstein. "Statistical Models and Inference for big Brain-Graphs" NIPS Workshop on Acquiring and analyzing the activity of large neural ensembles, Lake Tahoe, NV, USA,

J. T. Vogelstein. "BIG NEURO" Theory and Neurobiology, Duke University, Durham, NC, USA,

J. T. Vogelstein. "Open Connectome Project" Academic Medical Center, Amsterdam, Netherlands,

J. T. Vogelstein. "Are mental properties supervenient on brain properties" None,

J. T. Vogelstein. "What can Translational neuroimaging Research do for Clinical Practice" Child Mind Institute, New York City, NY, USA,

J. T. Vogelstein. "Statistical Connectomics" Harvard University Connectomics Labs, Cambridge, MA, USA,

J. T. Vogelstein. "Once we get connectomes, what the \%\#* are we going to do with them?" Krasnow Institute for Advanced Study at George Mason Univeristy, Fairfax, VA, USA,

J. T. Vogelstein. "Consistent Connectome Classification" Math/Bio Seminar, Duke University, Durham, NC, USA,

J. T. Vogelstein. "Connectome Classification: Statistical Graph Theoretic Methods for Analysis of MR-Connectome Data" Organization for Human Brain Mapping, Quebec City, Canada,

J. T. Vogelstein. "Consistent Graph Classification" Guest Lecture in Deisseroth Lab, Stanford University, Stanford, CA, USA,

J. T. Vogelstein. "Neurocognitive Graph Theory" National Security Agency,

J. T. Vogelstein. "OOPSI: A Family of Optimal OPtical Spike Inference Algorithms for Inferring Neural Connectivity from Population Calcium Imaging" Dissertation Defense, Johns Hopkins University, Baltimore, MD, USA,

J. T. Vogelstein. "Sequential Monte Carlo in Neuroscience" SAMSI Program on Sequential Monte Carlo, Tracking Working Group,

J. T. Vogelstein. "Towards Inference and Analaysis of Neural Circuits Inferred from Population Calcium Imaging" Guest Lecture in Schnitzer Lab, Stanford University, Stanford, CA, USA,

J. T. Vogelstein. "Towards Inferring Neural Circuits from Calcium Imaging" Guest Lecture in Yuste Lab, Columbia University, New York City, NY, USA,

J. T. Vogelstein. "Inferring Spike Trains Given Calcium-Sensitive Fluorescence Observations" Statistical Analysis of Neural Data, Pittsburgh, PA, USA,

J. T. Vogelstein. "Inferring spike trains from Calcium Imaging" Redwood Center for Theoretical Neuroscience, University of California, Berkeley, CA, USA,

J. T. Vogelstein. "Inferring spike trains from Calcium Imaging" Cambridge University, Gatsby Unit, and University College London, Cambridge, England,

J. T. Vogelstein. "Model based optimal inference of spike times and calcium dynamics givern noisy and intermittent calcium-fluorescence observations" Neurotheory Center of Columbia University, New York City, NY, USA,

L. A. De Silva and J. T. Vogelstein. "Kernel density networks" From Neuroscience to Artificially Intelligent Systems (NAISys), Cold Spring Harbor Laboratory, NY, USA,

J. Dey, W. LeVine, L. A. De Silva, A. Geisa, and J. T. Vogelstein. "Out-of-distribution Detection Using Kernel Density Polytopes" From Neuroscience to Artificially Intelligent Systems (NAISys), Cold Spring Harbor Laboratory, NY, USA, [URL]

J. J. How, G. Schuhknecht, M. B. Ahrens, F. Engert, and J. T. Vogelstein. "Transfer learning in larval zebrafish (Danio rerio)" From Neuroscience to Artificially Intelligent Systems (NAISys), Cold Spring Harbor Laboratory, NY, USA, [URL]

B. D. Pedigo, M. Powell, E. W. Bridgeford, M. Winding, C. E. Priebe, and J. T. Vogelstein. "Generative network modeling reveals a first quantitative definition of bilateral symmetry exhibited by a whole insect brain connectome" From Neuroscience to Artificially Intelligent Systems (NAISys), Cold Spring Harbor Laboratory, NY, USA, [URL]

J. M. Shin, L. Isik, and J. T. Vogelstein. "Measure of human-likelihood in tree-based ensemble model and artificial neural networks" From Neuroscience to Artificially Intelligent Systems (NAISys), Cold Spring Harbor Laboratory, NY, USA, [URL]

H. Xu and J. T. Vogelstein. "Simplest streaming trees" From Neuroscience to Artificially Intelligent Systems (NAISys), Cold Spring Harbor Laboratory, NY, USA,

E. W. Bridgeford, M. Powell, A. Alyakin, B. Caffo, and J. T. Vogelstein. "Batch Effects are Causal Effects: Applications in Human Functional Connectomes" Neuromatch 3,

J. Chung, J. Dey, G. Kiar, C. E. Priebe, and J. T. Vogelstein. "Human Structural Connectomes are Heritable" Neuromatch 3,

V. Gopalakrishnan, J. Chung, E. Bridgeford, J. Arroyo, B. D. Pedigo, C. E. Priebe, and J. T. Vogelstein. "Statistical Methods for Multiscale Comparative Connectomics" Neuromatch 3,

B. D. Pedigo, M. Winding, T. Orujlu, M. Zlatic, Cardona,Albert, C. E. Priebe, and J. T. Vogelstein. "A quantitative comparison of a complete connectome to artificial intelligence architectures" NAIsys, Cold Spring Harbor, NY, USA,

B. D. Pedigo, M. Winding, A. Saad-Eldin, T. Liu, A. Cardona, M. Zlatic, C. E. Priebe, and J. T. Vogelstein. "Statistical tools for nanoscale connectomics: clustering neurons in Drosophila larva brain and other applications" Neuromatch 3,

R. Perry, J. Zorn, S. Czajko, D. S. Margulies, and J. T. Vogelstein. "Permutation-corrected independence testing for high-dimensional fMRI data" Neuromatch 3,

A. Saad-Eldin, B. D. Pedigo, Y. Park, C. E. Priebe, and J. T. Vogelstein. "NeuroGraphMatch" Neuromatch 3,

J. T. Vogelstein, H. Helm, B. D. Pedigo, R. Mehta, C. E. Priebe, and C. White. "A Biological Implementation of Lifelong Learning in the Pursuit of Artificial General Intelligence" NAIsys, Cold Spring Harbor, NY, USA,

J. Cho, A. Korchmaros, J. T. Vogelstein, M. P. Milham, and T. Xu. "Developing a gradient flow framework to guide the optimization of reliability for the study of individual differences" OHBM and Resting State, Fairmont, Dallas, TX, USA,

J. Cho, A. Korchmaros, J. T. Vogelstein, M. P. Milham, and T. Xu. "Impact of Concatenating fMRI Data on reliability for Functional Connectomics" OHBM and Resting State, Fairmont, Dallas, TX, USA,

J. Hecheng, J. S. Ramirez, J. T. Vogelstein, M. P. Milham, and T. Xu. "Assessing functional connectivity beyond Pearson's correlation" Fairmont, Dallas, TX, USA,

X. Li, J. Cho, M. P. Milham, and T. Xu. "Improving brain-behavior prediction using individual-specific components from connectivity-based shared response model" Resting State, Fairmont, Dallas, TX, USA,

E. Bridgeford and J. T. Vogelstein. "Optimal Experimental Design for Big Data: Applications in Brain Imaging" OHBM,

J. Cho, A. Korchmaros, J. T. Vogelstein, M. P. Milham, and T. Xu. "Impact of Concatenating fMRI Data on reliability for Functional Connectomics" OHBM and Resting State, Fairmont, Dallas, TX, USA,

J. Cho, A. Korchmaros, J. T. Vogelstein, M. P. Milham, and T. Xu. "Developing a gradient flow framework to guide the optimization of reliability for the study of individual differences" OHBM and Resting State, Fairmont, Dallas, TX, USA,

R. Perry and J. T. Vogelstein. "Identifying Differences Between Expert and Novice Meditator Brain Scans via Multiview Embedding" OHBM,

B. Falk and J. T. Vogelstein. "NeuroData's Open Data Cloud Ecosystem" Harvard University, Cambridge, MA, USA, [URL]

J. Chung, B. D. Pedigo, C. E. Priebe, and J. T. Vogelstein. "Clustering Multi-Modal Connectomes" OHBM, Rome Italy, [URL]

J. Chung, B. D. Pedigo, C. E. Priebe, and J. T. Vogelstein. "Human Structural Connectomes are Heritable" OHBM, Rome Italy, [URL]

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, Calgary, Alberta, Canada, [URL]

B. D. Pedigo, J. Chung, E. W. Bridgeford, B. Varjavand, C. E. Priebe, and J. T. Vogelstein. "GraSPy: an Open Source Python Package for Statistical Connectomics" Max Planck /HHMI Connectomics Meeting Berlin, Germany, [URL]

A. Baden, E. Perlman, F. Collman, S. Smith, J. T. Vogelstein, and R. Burns. "Processing and Analyzing Terascale Conjugate Array Tomography Data" Berlin, Germany, [URL]

P. Perlman and E. Eric. "NEURODATA: ENABLING BIG DATA NEUROSCIENCE" Kavli, Baltimore, MD, USA, [URL]

S. J. Smith, R. Burns, M. Chevillet, E. Lein, G. Sapiro, W. Seeley, J. Trimmer, J. T. Vogelstein, and R. Weinberg. "The Open Synaptome Project: Toward a Microscopy-Based Platform for Single-synapse Analysis of Diverse Populations of CNS Synapses" Society for Neuroscience, Chicago, IL, USA, [URL]

V. Vogelstein and J. T. "Open Connectome Project NeuroData: Enabling Data-Driven Neuroscience at Scale" Society for Neuroscience, Chicago, IL, USA, [URL]

S. Chen, J. T. Vogelstein, S. Lee, M. Lindquist, and B. Caffo. "High Dimensional State Space Model with L-1 and L-2 Penalties" ENAR , Miami, FL, USA, [URL]

S. Chen, K. Liu, Y. Yuguang, L. Seonjoo, M. Lindquist, B. Caffo, and J. T. Vogelstein. "A Sparse High Dimensional State-Space Model with an Application to Neuroimaging Data" Figshare, [URL]

E. L. Deyer, H. L. Fernandes, W. G. Roncal, D. Gursoy, J. T. Vogelstein, X. Xiao, C. Jacobsen, K. P. Kording, and N. Kasthuri. "X-Brain: Quantifying Mesoscale Neuroanatomy Using X-ray Microtomography" Figshare, [URL]

S. Wang, Z. Yang, X. Zuo, M. Milham, C. Craddock, C. E. Priebe, and J. T. Vogelstein. "Optimal Design for Discovery Science: Applications in Neuroimaging" Figshare, [URL]

S. Sikka, S. A. Cheung, B. A. Khanuja, R. A. Ghosh, S. A. Yan, C. A. Li, Q. A. Vogelstein, J. A. Burns, R. A. Colcombe, S. A. Craddock, C. A. Mennes, M. A. Kelly, C. A. Dimartino, A. A. Castellanos, F. A. Milham, and M. Michael. "Towards automated analysis of connectomes: The configurable pipeline for the analysis of connectomes (c-pac)" 5th INCF Congress of Neuroinformatics, Munich, Germany, [URL]

J. T. Vogelstein and C. E. Priebe. "Nonparametric Two-Sample Testing on Graph-Valued Data." Duke Workshop on Sensing and Analysis of HighDimensional Data, Durham, NC, USA,

D. Koutra, Y. Gong, S. Ryman, R. Jung, J. T. Vogelstein, and C. Faloutsos. "Are All Brains Wired Equally?" Proceedings of the 19th Annual Meeting of the Organization for Human Brain Mapping (OHBM), Seattle, WA, USA, (), [URL]

M. Mhembere, D. A. Burns, R. A. Vogelstein, J. T. A. Vogelstein, R. J. A. Sussman, D. A. Preibe, C. A. Jung, R. Rex, A. Ryman, and S. Sephira. "Multivariate Invariants from Massive Brain-Graphs" OHBM, Seattle, WA, USA, [URL]

Y. Qin, D. Mhembere, S. Ryman, R. Jung, R. J. Vogelstein, R. Burns, J. Vogelstein, and C. Priebe. "Robust Clustering of Adjacency Spectral Embeddings of Brain Graph Data via Lq-Likelihood" OHBM, Seattle, WA, USA, [URL]

G. Roncal, W. A. Kleissas, D. M. A. Burck, J. M. A. Manavalan, P. A. Vogelstein, J. T. A. Perlman, E. A. Burns, R. A. Vogelstein, and R. Jacob. "Towards a Fully Automatic Pipeline for Connectome Estimation from High-Resolution EM Data" OHBM, Seattle, WA, USA, [URL]

D. L. Sussman, D. Mhembere, S. Ryman, R. Jung, R. J. Vogelstein, R. Burns, J. T. Vogelstein, and C. E. Priebe. "Massive Diffusion MRI Graph Structure Preserves Spatial Information" OHBM, Seattle, WA, USA, [URL]

R. D, A. A. Vogelstein, J. A. Caffo, B. A. Pekar, J. J. A. H. I, and S. Sair. "Reproducible differentiation of individual of individual subjects with minimal acquisition time via resting state fMRI" Proc ISMRM, Salt Lake City, UT, USA, [URL]

S. Sismanis, N. A. Sussman, D. L. A. Vogelstein, J. T. A. Gray, W. A. Vogelstein, R. J. A. Perlman, E. A. Mhembere, D. A. Ryman, S. A. Jung, R. A. Burns, R. A. Priebe, C. E. A. Pitsianis, N. A. Sun, and X. X.. "Feature Clustering from a Brain Graph for Voxel-to-Region Classification" 5th Panhellic Conference on Biomedical Technology, Athens, Greece, [URL]

P. Pnevmatikakis, E. A. A. Machado, T. A. Grosenick, L. A. Poole, B. A. Vogelstein, and J. T. A. P. Liam. "Rank-penalized nonnegative spatiotemporal deconvolution and demixing of calcium inaging data" COSYNE, Salt Lake City, UT, USA, [URL]

J. T. Vogelstein and others. "Anomaly Screening and Clustering of Multi-OBject Movies via Multiscale Structure Learning" DARPA XDATA Colloquium,

V. Vogelstein, J. A. Sikka, S. A. Cheung, B. A. Khanuja, R. A. Li, Q. A. Y. C, .. A. Priebe, C. A. Calhoun, V. A. Vogelstein, R. J. A. Milham, M. A. Burns, and R. R.. "BRAINSTORM towards clinically and scientifically useful neuroimaging analytics" Neuroinformatics, Munich, Germany, [URL]

V. Vogelstein, J. T. A. Bock, D. A. Gray, W. A. Sussman, D. A. Burns, R. A. Kleissas, D. A. Marchette, D. A. Fishkind, D. E. A. Tang, M. A. Hager, G. A. Vogelstein, and R. J. A. P. C. E.. "Statistical Connectomics" Janelia Farm conference, Statistical Inference and Neuroscience, Loudoun County, VA, USA, [URL]

G. Gray, W. R. A. Kleissas, D. M. A. Burck, J. M. A. Vogelstein, J. T. A. Perlman, E. A. Burlina, P. M. A. Burns, and R. A. V. R. Jacob. "Towards a Fully Automatic Pipeline for Connectome Estimation from High-Resolution EM Data" Cold Spring Harbor Laboratory, Neuronal Circuits, Cold Spring Harbor, NY, USA, [URL]

W. R. Gray, J. A. Bogovic, J. T. Vogelstein, C. Ye, B. A. Landman, J. L. Prince, and R. J. Vogelstein. "Magnetic resonance connectome automated pipeline and repeatability analysis" Society for Neuroscience, Washington DC, USA, [URL]

J. T. Vogelstein, D. E. Fishkind, D. L. Sussman, and C. E. Priebe. "Large graph classification: theory and statistical connectomics applications" IMA conference on Large Graphs, University of Minnesota, Minneapolis, MN, USA, [URL]

J. T. Vogelstein, W. Gray, J. G. Martin, G. C. Coppersmith, M. Dredze, J. Bogovic, J. L. Prince, S. M. Resnick, C. E. Priebe, and R. J. Vogelstein. "Connectome Classification using statistical graph theory and machine learning" Society for Neuroscience, Washington DC, USA, [URL]

J. T. Vogelstein, D. L. Sussman, M. Tang, D. E. Fishkind, and C. E. Priebe. "Dot product embedding in large (errorfully observed) graphs with applications in statistical connectomics" IMA conference on Large Graphs, University of Minnesota, Minneapolis, MN, USA,

J. T. Vogelstein, E. Perlman, D. Bock, W. C. Lee, M. Chang, B. Kasthuri, M. Kazhdan, C. Reid, J. Lichtman, R. Burns, and R. J. Vogelstein. "Open Connectome Project: collectively reverse engineering the brain one synapse at a time" Neuroinformatics, Boston, MA, USA, [URL]

J. T. Vogelstein, W. R. Gray, R. J. Vogelstein, J. Bogovic, S. Resnick, J. Prince, and C. E. Priebe. "Connectome Classification: Statistical Graph Theoretic Methods for Analysis of MR-Connectome Data" Organization for Human Brain Mapping, Quebec City, Canada, [URL]

W. R. Gray, J. T. Vogelstein, J. Bogovic, A. Carass, J. L. Prince, B. Landman, D. Pham, L. Ferrucci, S. M. Resnick, C. E. Priebe, and R. J. Vogelstein. "Graph-Theoretical Methods for Statistical Inference on MR Connectome Data" DARPA Neural Engineering, Science and Technology Forum, San Diego, CA, USA, [URL]

J. T. Vogelstein, C. E. Priebe, R. Burns, R. J. Vogelstein, and J. Lichtman. "Measuring and reconstructing the brain at the synaptic scale: towards a biofidelic human brain in silico" DARPA Neural Engineeering, Science and Technology Forum, San Diego, CA, USA, [URL]

J. T. Vogelstein, J. Bogovic, A. Carass, W. Gray, J. Prince, B. Landman, D. Pham, L. Ferrucci, S. Resnick, C. E. Priebe, and R. Vogelstein. "Graph-Theoretical Methods for Statistical Inference on MR Connectome Data" Organization for Human Brain Mapping, Barcelona, Spain, [URL]

J. T. Vogelstein, R. Vogelstein, and C. E. Priebe. "A Neurocognitive Graph-Theoretical Approach to Understanding the Relationship Between Minds and Brains" CSHL conference on Neural Circuits, Cold Shore Harbor, NY, USA, [URL]

J. T. Vogelstein, Y. Mishchenki, A. Packer, T. Machado, R. Yuste, and L. Paninski. "Towards Confirming Neural Circuit Inference from Population Calcium Imaging" COSYNE, Salt Lake City, UT, USA, [URL]

J. T. Vogelstein, Y. Mishchenki, A. Packer, T. Machado, R. Yuste, and L. Paninski. "Towards Inferring Neural Circuit Inference from Population Calcium Imaging" COSYNE, Salt Lake City, UT, USA, [URL]

J. T. Vogelstein, Y. Mishchchenko, A. M. Packer, T. A. Machado, R. Yuste, and L. Paninski. "Towards Confirming Neural Circuits from Population Calcium Imaging" NIPS Workshop on Connectivity Inference in Neuroimaging, Whistler, BC, Canada, [URL]

J. T. Vogelstein, Y. Mishchenki, A. Packer, T. Machado, R. Yuste, and L. Paninski. "Towards Inferring Neural Circuit Inference from Population Calcium Imaging" COSYNE, Salt Lake City, UT, USA, [URL]

J. T. Vogelstein, B. Babadi, B. Watson, R. Yuste, and L. Paninski. "From Calcium Sensitive Fluorescence Movies to Spike Trains" Society for Neuroscience, Washington DC, USA, [URL]

J. T. Vogelstein, B. Babadi, and L. Paninski. "Model-Based Optimal Inference of Spike-Times and Calcium Dynamics given Noisy and Intermittent Calcium-Fluorescence Imaging" COSYNE, Salt Lake City, UT, USA, [URL]

J. T. Vogelstein and L. Paninski. "Inferring Spike Trains, Learning Tuning Curves, and Estimating Connectivity from Calcium Imaging" Integrative Approaches to Brain Complexity, [URL]

J. T. Vogelstein, B. Jedynak, K. Zhang, and L. Paninski. "Inferring Spike Trains, Neural Filters, and Network Circuits from in vivo Calcium Imaging" Society for Neuroscience, San Diego, CA, USA, [URL]

J. T. Vogelstein, K. Zhang, B. Jedynak, and L. Paninski. "Maximum Likelihood Inference of Neural Dynamics under Noisy and Intermittent Observations using Sequential Monnte Carlo EM Algorithms" COSYNE, Salt Lake City, UT, USA, [URL]

J. T. Vogelstein and K. Zhang. "A novel theory for simultaneous representation of multiple dynamic states in hippocampus" Society for Neuroscience, San Diego, CA, USA,

J. T. Vogelstein, L. Snyder, M. Warchol, and D. Angelaki. "Up-down asymmetry in memory guided saccadic eye movements are independent of head orientation in space" Society for Neuroscience, Orlando, FL, USA,

Finalizing three manuscripts on (1) uncertainty forests, (2) time-series dependence quantification, and (3) lifelong learning forests

Worked on various problems in statistical graph inference

Lead research efforts developing theory and methods for lifelong learning

ead Scientist in developing storage, transfer, and visualization solutions for large data in our cloud infrastructure

Lead developer converting our extensions to decision forests to be merged into sklearn

Currently investigating the effect of inductive bias innately coinciding with various machine learning models

Researching progressive and lifelong learning theory

Lead developer of our scalable Python implementation of LDDMM

Lead software engineer, oversees all development projects, solely responsible for all cloud infrastructure

Javier studies how larval zebrafish learn how to perform a task under one situation, and use this knowledge to learn another task more quickly. He hopes to use this understanding of biological transfer learning to improve machine learning, which tends to be unable to complete this feat.

Co-Advised by Prof. Richard Huganir, Department of Neuroscience. Working on understanding whole brain synaptic plasticity using genetic engineering and light microscopy imaging

Co-Advised by Assitant Prof. Kuchibhotla, Department of Psychological and Brain Sciences. Working on understanding learning and memory using two-photon calcium imaging

Worked on graph matching and joint graph embedding

Co-Advised by Prof Michela Gallagher, extending brain clearing experimental technology from mice to rats. Currently with a manuscript on biorxiv

Developed Multiscale Graph Correlation, which is currently the premiere hypothesis testing framework, and about to be integrated into SciPy, by far the world's leading scientific computing package. Currently an Assistent Professor in Department of Statistics at University of Delaware, and still an actice collaborator and grantee

Worked on non-parametric clustering, with an article about to be accepted in PAMI, the leading machine learning journal. Currently a postdoc for Rene Vidal

Went on to do a second postdoc with Leo Dunson (who I did my second postdoc with). Currently an Assistant Professor at University of Florida

Developed Sparse Projection Oblique Randomer Forest in his dissertation, currently the best performing machine learning algorithm on a standard suite of over benchmark problems. Currenly a postdoc with Assistant Prof. Chris Honey of Psychology and Brain Sciences

Skyler is a rotation student in the lab. He is interested in applied mathematics and machine learning. He is currently working on prospective learning theory. He is currently a Ph.D. student in BME at JHU.

Statistical Machine Learning

A Research Assistant who was also a Master's student in the NeuroData lab, maintainer of proglearn, working on streaming trees and forests

Kaleab studied double descent phenomena in decision forests and deep learning methods and developed 'Partition and Decode' – a framework that formalizes an implicit internal representation of several modern machine learning methods. He is currently a Ph.D. student in CS at JHU.

Implementing and applying clustering algorithms to the connectomes of inset nervous systems.

Dissertation will focus on statistics of human connectomes and mitigating batch effects. Already first author on several manuscripts under review, and spearheads collaboration with Prof Brian Caffo at Biostatistics

Mike has studied drug-repurposing options for potential COVID treatments, proposed methodological improvements and best practices for drug-repurposing studies, and developed a taxonomy for describing and quantifying feature importance in machine learning models.

Currently working on lifelong learning that aims at training a machine learning model on multiple tasks and transferring knowledge among tasks

A Ph.D. student who was also a Master's student in the NeuroData lab. Currently, the maintainer of the `hyppo` package, and works on creating more powerful and efficient multivariate hypothesis tests.

Data science for macroscale connectomes. Co-creator and maintainer of `graspologic`, a Python package for network statistics.

Tommy analyzes terabyte-scale full brain images from light microscopy with a focus on neuromorphology. His expertise is in statistics and computer vision.

Data science for nanoscale connectomes. Co-creator and maintainer of `graspologic`, a Python package for network statistics.

Dissertation will focus on computational aspects of accelerationg learning and inference using decision forests

Dissertation has focused on extending LDDMM to whole cleared brain datasets, spearheads collaboration with Prof. Karl Deisseroth’s lab at Stanford, one of the world’s leading neuroscientists

Worked on the ndmg pipeline, developing direct streamline normalization for structural connectome generation

Working on implementing and improving cominatorial optimization algorithms, specifically the Quadratic Assignment Problem

Exploring different sub-algorithms within progressive learning to alleviate harmful effects that resultfrom training on unhelpful data

brainlit' package, aws and azure data management

Lead m2g developer, maintainer of neuroparc, MRI connectome repositories

Submitted manuscript to PAMI on advancing statistics on populaitons of networks

Vivek developed multiscale hypothesis tests for multi-subject connectomics datasets, and is currently a PhD student in Medical Engineering and Medical Physics at the Harvard-MIT Program in Health Sciences and Technology.

Ronan studied random forest methods for structured data and improved prediction calibration, developed nonparametric hypothesis testing tools, and explored novel complexity measures of neural networks. He is currently a Fulbright Germany scholar with Bernhard Scholkopf.

graph stats book, 'graspologic' package, ndmg development

Preparing manuscript introducing novel specification for large attributed networks

Developer of m2g, the only existing "soup to nuts" pipeline for both functional and diffusion pipelines, co-first author of manuscript under review. Currently a PhD student at McGill University

Assisted with fixing issues in graspologic and hyppo

Assisting in the integration of brainlit packages with Azure services

Assisting in the maintenance of graspologic

Assisted Thomas Athey in his work with statistics and computer vision

Developed uncertainty forests, an approach for estimated posterior class probabilities, conditional entropy, and mutual information for high-dimensional data common in brain science applications

Developed big data visualization tools

Worked at a computational medicine start-up in Silicon Valley, worked on high-dimensional low-sample size theory

Currently a data scientist at APL, developed a number of simple WebApps in support of big data management

Currently an MD, PhD Candidate at the UPenn, winner of Soros Fellowship, worked on analysis of data from Dr. Daniel Amen, developed matrix exploratory data analysis package.