Dynamically Reconfigurable Silicon Array of Spiking Neurons With Conductance-Based Synapses. - R. Jacob Vogelstein; Udayan Mallik; Joshua T. Vogelstein; Gert Cauwenberghs (IEEE Trans. Neural Networks, 2007)

Psychiatry: Insights into depression through normative decision-making models. - Quentin J. M. Huys; Joshua T. Vogelstein; Peter Dayan (NIPS, 2008)

A new look at state-space models for neural data. - Liam Paninski; Yashar Ahmadian; Daniel Gil Ferreira; Shinsuke Koyama; Kamiar Rahnama Rad; Michael Vidne; Joshua T. Vogelstein; Wei Wu 006 (J. Comput. Neurosci., 2010)

Network-Based Classification Using Cortical Thickness of AD Patients. - Dai Dai; Huiguang He; Joshua T. Vogelstein; Zengguang Hou (MLMI, 2011)

Fast Inexact Graph Matching with Applications in Statistical Connectomics - Joshua T. Vogelstein; John M. Conroy; Louis J. Podrazik; Steven G. Kratzer; Donniell E. Fishkind; R. Jacob Vogelstein; Carey E. Priebe (CoRR, 2011)

Sex Differences in the Human Connectome. - Vivek Kulkarni; Jagat Sastry Pudipeddi; Leman Akoglu; Joshua T. Vogelstein; R. Jacob Vogelstein; Sephira Ryman; Rex E. Jung (Brain and Health Informatics, 2013)

Computing scalable multivariate glocal invariants of large (brain-) graphs. - Disa Mhembere; William R. Gray Roncal; Daniel L. Sussman; Carey E. Priebe; Rex E. Jung; Sephira Ryman; R. Jacob Vogelstein; Joshua T. Vogelstein; Randal C. Burns (GlobalSIP, 2013)

MIGRAINE: MRI Graph Reliability Analysis and Inference for Connectomics. - William R. Gray Roncal; Zachary H. Koterba; Disa Mhembere; Dean M. Kleissas; Joshua T. Vogelstein; Randal C. Burns; Anita R. Bowles; Dimitrios K. Donavos; Sephira Ryman; Rex E. Jung; Lei Wu 013; Vince D. Calhoun; et al. (GlobalSIP, 2013)

Bayesian crack detection in ultra high resolution multimodal images of paintings. - Bruno Cornelis; Yun Yang; Joshua T. Vogelstein; Ann Dooms; Ingrid Daubechies; David B. Dunson (DSP, 2013)

Real-Time Inference for a Gamma Process Model of Neural Spiking. - David E. Carlson; Vinayak A. Rao; Joshua T. Vogelstein; Lawrence Carin (NIPS, 2013)

Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching. - Marcelo Fiori; Pablo Sprechmann; Joshua T. Vogelstein; Pablo Musé; Guillermo Sapiro (NIPS, 2013)

Multiscale Dictionary Learning for Estimating Conditional Distributions. - Francesca Petralia; Joshua T. Vogelstein; David B. Dunson (NIPS, 2013)

DELTACON: A Principled Massive-Graph Similarity Function. - Christos Faloutsos; Danai Koutra; Joshua T. Vogelstein (SDM, 2013)

The open connectome project data cluster: scalable analysis and vision for high-throughput neuroscience. - Randal C. Burns; Kunal Lillaney; Daniel R. Berger; Logan Grosenick; Karl Deisseroth; R. Clay Reid; William R. Gray Roncal; Priya Manavalan; Davi Bock; Narayanan Kasthuri; Michael M. Kazhdan; Stephen J. Smith; et al. (SSDBM, 2013)

Gradient-Domain Processing for Large EM Image Stacks. - Michael M. Kazhdan; Randal C. Burns; Bobby Kasthuri; Jeff Lichtman; R. Jacob Vogelstein; Joshua T. Vogelstein (CoRR, 2013)

Accurate prediction of AD patients using cortical thickness networks. - Dai Dai; Huiguang He; Joshua T. Vogelstein; Zengguang Hou (Mach. Vis. Appl., 2013)

Graph Classification Using Signal-Subgraphs: Applications in Statistical Connectomics. - Joshua T. Vogelstein; William R. Gray Roncal; R. Jacob Vogelstein; Carey E. Priebe (IEEE Trans. Pattern Anal. Mach. Intell., 2013)

Consistent Adjacency-Spectral Partitioning for the Stochastic Block Model When the Model Parameters Are Unknown. - Donniell E. Fishkind; Daniel L. Sussman; Minh Tang; Joshua T. Vogelstein; Carey E. Priebe (SIAM J. Matrix Anal. Appl., 2013)

Covariate Assisted Spectral Clustering. - Norbert Binkiewicz; Joshua T. Vogelstein; Karl Rohe (CoRR, 2014)

Volumetric Exploitation of Synaptic Information using Context Localization and Evaluation. - William R. Gray Roncal; Verena Kaynig-Fittkau; Narayanan Kasthuri; Daniel R. Berger; Joshua T. Vogelstein; Lindsey R. Fernandez; Jeff W. Lichtman; R. Jacob Vogelstein; Hanspeter Pfister; Gregory D. Hager (CoRR, 2014)

An Automated Images-to-Graphs Pipeline for High Resolution Connectomics. - William R. Gray Roncal; Dean Kleissas; Joshua T. Vogelstein; Priya Manavalan; Randal C. Burns; R. Jacob Vogelstein; Carey E. Priebe; Mark A. Chevillet; Gregory D. Hager (CoRR, 2014)

Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes. - Ayushi Sinha; William R. Gray Roncal; Narayanan Kasthuri; Ming Chuang; Priya Manavalan; Dean Kleissas; Joshua T. Vogelstein; R. Jacob Vogelstein; Randal C. Burns; Jeff W. Lichtman; Michael M. Kazhdan (CoRR, 2014)

Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling. - David E. Carlson; Joshua T. Vogelstein; Qisong Wu; Wenzhao Lian; Mingyuan Zhou; Colin R. Stoetzner; Daryl Kipke; Douglas Weber; David B. Dunson; Lawrence Carin (IEEE Trans. Biomed. Eng., 2014)

VESICLE: Volumetric Evaluation of Synaptic Inferfaces using Computer Vision at Large Scale. - William R. Gray Roncal; Michael J. Pekala; Verena Kaynig-Fittkau; Dean M. Kleissas; Joshua T. Vogelstein; Hanspeter Pfister; Randal C. Burns; R. Jacob Vogelstein; Mark A. Chevillet; Gregory D. Hager (BMVC, 2015)

FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs. - Da Zheng; Disa Mhembere; Randal C. Burns; Joshua T. Vogelstein; Carey E. Priebe; Alexander S. Szalay (FAST, 2015)

Shuffled Graph Classification: Theory and Connectome Applications. - Joshua T. Vogelstein; Carey E. Priebe (J. Classif., 2015)

Gradient-Domain Fusion for Color Correction in Large EM Image Stacks. - Michael M. Kazhdan; Kunal Lillaney; William R. Gray Roncal; Davi Bock; Joshua T. Vogelstein; Randal C. Burns (CoRR, 2015)

Randomer Forests. - Tyler M. Tomita; Mauro Maggioni; Joshua T. Vogelstein (CoRR, 2015)

An Automated Images-to-Graphs Framework for High Resolution Connectomics. - William R. Gray Roncal; Dean M. Kleissas; Joshua T. Vogelstein; Priya Manavalan; Kunal Lillaney; Michael J. Pekala; Randal C. Burns; R. Jacob Vogelstein; Carey E. Priebe; Mark A. Chevillet; Gregory D. Hager (Frontiers Neuroinformatics, 2015)

Spectral clustering for divide-and-conquer graph matching. - Vince Lyzinski; Daniel L. Sussman; Donniell E. Fishkind; Henry Pao; Li Chen; Joshua T. Vogelstein; Youngser Park; Carey E. Priebe (Parallel Comput., 2015)

Quantifying mesoscale neuroanatomy using X-ray microtomography. - Eva L. Dyer; William R. Gray Roncal; Hugo L. Fernandes; Doga Gürsoy; Xianghui Xiao; Joshua T. Vogelstein; Chris Jacobsen; Konrad P. Körding; Narayanan Kasthuri (CoRR, 2016)

A Diffeomorphic Approach to Multimodal Registration with Mutual Information: Applications to CLARITY Mouse Brain Images. - Kwame S. Kutten; Nicolas Charon; Michael I. Miller; J. Tilak Ratnanather; Karl Deisseroth; Li Ye; Joshua T. Vogelstein (CoRR, 2016)

Deformably Registering and Annotating Whole CLARITY Brains to an Atlas via Masked LDDMM. - Kwame S. Kutten; Joshua T. Vogelstein; Nicolas Charon; Li Ye; Karl Deisseroth; Michael I. Miller (CoRR, 2016)

NUMA-optimized In-memory and Semi-external-memory Parameterized Clustering. - Disa Mhembere; Da Zheng; Joshua T. Vogelstein; Carey E. Priebe; Randal C. Burns (CoRR, 2016)

An SSD-based eigensolver for spectral analysis on billion-node graphs. - Da Zheng; Randal C. Burns; Joshua T. Vogelstein; Carey E. Priebe; Alexander S. Szalay (CoRR, 2016)

Semi-External Memory Sparse Matrix Multiplication on Billion-node Graphs in a Multicore Architecture. - Da Zheng; Disa Mhembere; Vince Lyzinski; Joshua T. Vogelstein; Carey E. Priebe; Randal C. Burns (CoRR, 2016)

FlashMatrix: Parallel, Scalable Data Analysis with Generalized Matrix Operations using Commodity SSDs. - Da Zheng; Disa Mhembere; Joshua T. Vogelstein; Carey E. Priebe; Randal C. Burns (CoRR, 2016)

Robust Vertex Classification. - Li Chen; Cencheng Shen; Joshua T. Vogelstein; Carey E. Priebe (IEEE Trans. Pattern Anal. Mach. Intell., 2016)

Graph Matching: Relax at Your Own Risk. - Vince Lyzinski; Donniell E. Fishkind; Marcelo Fiori; Joshua T. Vogelstein; Carey E. Priebe; Guillermo Sapiro (IEEE Trans. Pattern Anal. Mach. Intell., 2016)

DeltaCon: Principled Massive-Graph Similarity Function with Attribution. - Danai Koutra; Neil Shah; Joshua T. Vogelstein; Brian Gallagher; Christos Faloutsos (ACM Trans. Knowl. Discov. Data, 2016)

knor: A NUMA-Optimized In-Memory, Distributed and Semi-External-Memory k-means Library. - Disa Mhembere; Da Zheng; Carey E. Priebe; Joshua T. Vogelstein; Randal C. Burns (HPDC, 2017)

A Large Deformation Diffeomorphic Approach to Registration of CLARITY Images via Mutual Information. - Kwame S. Kutten; Nicolas Charon; Michael I. Miller; J. Tilak Ratnanather; Jordan Matelsky; Alexander D. Baden; Kunal Lillaney; Karl Deisseroth; Li Ye; Joshua T. Vogelstein (MICCAI (1), 2017)

ROFLMAO: Robust Oblique Forests with Linear MAtrix Operations. - Tyler M. Tomita; Mauro Maggioni; Joshua T. Vogelstein (SDM, 2017)

Joint Embedding of Graphs. - Shangsi Wang; Joshua T. Vogelstein; Carey E. Priebe (CoRR, 2017)

Energy Clustering. - Guilherme França; Joshua T. Vogelstein (CoRR, 2017)

Statistical Inference on Random Dot Product Graphs: a Survey. - Avanti Athreya; Donniell E. Fishkind; Minh Tang; Carey E. Priebe; Youngser Park; Joshua T. Vogelstein; Keith D. Levin; Vince Lyzinski; Yichen Qin; Daniel L. Sussman (J. Mach. Learn. Res., 2017)

Whole-brain serial-section electron microscopy in larval zebrafish. - David Grant Colburn Hildebrand; Marcelo Cicconet; Russel Miguel Torres; Woohyuk Choi; Tran Minh Quan; Jungmin Moon; Arthur W. Wetzel; Andrew Scott Champion; Brett J. Graham; Owen Randlett; George S. Plummer; Ruben Portugues; et al. (Nat., 2017)

Probabilistic fluorescence-based synapse detection. - Anish K. Simhal; Cecilia Aguerrebere; Forrest Collman; Joshua T. Vogelstein; Kristina D. Micheva; Richard J. Weinberg; Stephen J. Smith; Guillermo Sapiro (PLoS Comput. Biol., 2017)

An M-estimator for reduced-rank system identification. - Shaojie Chen; Kai Liu; Yuguang Yang 002; Yuting Xu; Seonjoo Lee; Martin A. Lindquist; Brian S. Caffo; Joshua T. Vogelstein (Pattern Recognit. Lett., 2017)

Manifold matching using shortest-path distance and joint neighborhood selection. - Cencheng Shen; Joshua T. Vogelstein; Carey E. Priebe (Pattern Recognit. Lett., 2017)

Semi-External Memory Sparse Matrix Multiplication for Billion-Node Graphs. - Da Zheng; Disa Mhembere; Vince Lyzinski; Joshua T. Vogelstein; Carey E. Priebe; Randal C. Burns (IEEE Trans. Parallel Distributed Syst., 2017)

Building NDStore Through Hierarchical Storage Management and Microservice Processing. - Kunal Lillaney; Dean Kleissas; Alexander Eusman; Eric A. Perlman; William R. Gray Roncal; Joshua T. Vogelstein; Randal C. Burns (eScience, 2018)

FlashR: parallelize and scale R for machine learning using SSDs. - Da Zheng; Disa Mhembere; Joshua T. Vogelstein; Carey E. Priebe; Randal C. Burns (PPoPP, 2018)

The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing. - Cencheng Shen; Joshua T. Vogelstein (CoRR, 2018)

On a 'Two Truths' Phenomenon in Spectral Graph Clustering. - Carey E. Priebe; Youngser Park; Joshua T. Vogelstein; John M. Conroy; Vince Lyzinski; Minh Tang; Avanti Athreya; Joshua Cape; Eric Bridgeford (CoRR, 2018)

Decision Forests Induce Characteristic Kernels. - Cencheng Shen; Joshua T. Vogelstein (CoRR, 2018)

Forest Packing: Fast Parallel, Decision Forests. - James Browne; Disa Mhembere; Tyler M. Tomita; Joshua T. Vogelstein; Randal C. Burns (SDM, 2019)

clusterNOR: A NUMA-Optimized Clustering Framework. - Disa Mhembere; Da Zheng; Carey E. Priebe; Joshua T. Vogelstein; Randal C. Burns (CoRR, 2019)

GraSPy: Graph Statistics in Python. - Jaewon Chung; Benjamin D. Pedigo; Eric W. Bridgeford; Bijan K. Varjavand; Joshua T. Vogelstein (CoRR, 2019)

Vertex Classification on Weighted Networks. - Hayden S. Helm; Joshua T. Vogelstein; Carey E. Priebe (CoRR, 2019)

Estimating Information-Theoretic Quantities with Random Forests. - Richard Guo; Cencheng Shen; Joshua T. Vogelstein (CoRR, 2019)

mgcpy: A Comprehensive High Dimensional Independence Testing Python Package. - Sambit Panda; Satish Palaniappan; Junhao Xiong; Ananya Swaminathan; Sandhya Ramachandran; Eric W. Bridgeford; Cencheng Shen; Joshua T. Vogelstein (CoRR, 2019)

Geodesic Learning via Unsupervised Decision Forests. - Meghana Madhyastha; Percy Li; James Browne; Veronika Strnadová-Neeley; Carey E. Priebe; Randal C. Burns; Joshua T. Vogelstein (CoRR, 2019)

Graphyti: A Semi-External Memory Graph Library for FlashGraph. - Disa Mhembere; Da Zheng; Carey E. Priebe; Joshua T. Vogelstein; Randal C. Burns (CoRR, 2019)

A Consistent Independence Test for Multivariate Time-Series. - Ronak D. Mehta; Cencheng Shen; Ting Xu 001; Joshua T. Vogelstein (CoRR, 2019)

AutoGMM: Automatic Gaussian Mixture Modeling in Python. - Thomas L. Athey; Joshua T. Vogelstein (CoRR, 2019)

Manifold Forests: Closing the Gap on Neural Networks. - Ronan Perry; Tyler M. Tomita; Jesse Patsolic; Benjamin Falk; Joshua T. Vogelstein (CoRR, 2019)

The Exact Equivalence of Independence Testing and Two-Sample Testing. - Cencheng Shen; Carey E. Priebe; Joshua T. Vogelstein (CoRR, 2019)

The Chi-Square Test of Distance Correlation. - Cencheng Shen; Joshua T. Vogelstein (CoRR, 2019)

GraSPy: Graph Statistics in Python. - Jaewon Chung; Benjamin D. Pedigo; Eric W. Bridgeford; Bijan K. Varjavand; Hayden S. Helm; Joshua T. Vogelstein (J. Mach. Learn. Res., 2019)

Thermal sensors improve wrist-worn position tracking. - Jake J. Son; Jon Clucas; Curt White; Anirudh Krishnakumar; Joshua T. Vogelstein; Michael P. Milham; Arno Klein (npj Digit. Medicine, 2019)

Connectome Smoothing via Low-Rank Approximations. - Runze Tang; Michael D. Ketcha; Alexandra Badea; Evan D. Calabrese; Daniel S. Margulies; Joshua T. Vogelstein; Carey E. Priebe; Daniel L. Sussman (IEEE Trans. Medical Imaging, 2019)

Geodesic Forests. - Meghana Madhyastha; Gongkai Li; Veronika Strnadová-Neeley; James Browne; Joshua T. Vogelstein; Randal C. Burns; Carey E. Priebe (KDD, 2020)

A general approach to progressive intelligence. - Joshua T. Vogelstein; Hayden S. Helm; Ronak D. Mehta; Jayanta Dey; Weiwei Yang 004; Bryan Tower; Will LeVine; Jonathan Larson; Christopher M. White; Carey E. Priebe (CoRR, 2020)

A New Age of Computing and the Brain. - Polina Golland; Jack L. Gallant; Greg Hager; Hanspeter Pfister; Christos H. Papadimitriou; Stefan Schaal; Joshua T. Vogelstein (CoRR, 2020)

Learning to rank via combining representations. - Hayden S. Helm; Amitabh Basu; Avanti Athreya; Youngser Park; Joshua T. Vogelstein; Michael Winding; Marta Zlatic; Albert Cardona; Patrick Bourke; Jonathan Larson; Christopher M. White; Carey E. Priebe (CoRR, 2020)

mvlearn: Multiview Machine Learning in Python. - Ronan Perry; Gavin Mischler; Richard Guo; Theo Lee; Alexander Chang; Arman Koul; Cameron Franz; Joshua T. Vogelstein (CoRR, 2020)

Robust Similarity and Distance Learning via Decision Forests. - Tyler M. Tomita; Joshua T. Vogelstein (CoRR, 2020)

PACSET (Packed Serialized Trees): Reducing Inference Latency for Tree Ensemble Deployment. - Meghana Madhyastha; Kunal Lillaney; James Browne; Joshua T. Vogelstein; Randal C. Burns (CoRR, 2020)

A partition-based similarity for classification distributions. - Hayden S. Helm; Ronak D. Mehta; Brandon Duderstadt; Weiwei Yang 004; Christopher M. White; Ali Geisa; Joshua T. Vogelstein; Carey E. Priebe (CoRR, 2020)

Sparse Projection Oblique Randomer Forests. - Tyler M. Tomita; James Browne; Cencheng Shen; Jaewon Chung; Jesse Patsolic; Benjamin Falk; Carey E. Priebe; Jason Yim; Randal C. Burns; Mauro Maggioni; Joshua T. Vogelstein (J. Mach. Learn. Res., 2020)

Toward a connectivity gradient-based framework for reproducible biomarker discovery. - Seok-Jun Hong; Ting Xu 001; Aki Nikolaidis; Jonathan Smallwood; Daniel S. Margulies; Boris C. Bernhardt; Joshua T. Vogelstein; Michael P. Milham (NeuroImage, 2020)

Joint embedding: A scalable alignment to compare individuals in a connectivity space. - Karl-Heinz Nenning; Ting Xu 001; Ernst Schwartz; Jesús Arroyo 001; Adelheid Woehrer; Alexandre R. Franco; Joshua T. Vogelstein; Daniel S. Margulies; Hesheng Liu; Jonathan Smallwood; Michael P. Milham; Georg Langs (NeuroImage, 2020)

Bagging improves reproducibility of functional parcellation of the human brain. - Aki Nikolaidis; Anibal Sólon Heinsfeld; Ting Xu 001; Pierre C. Bellec; Joshua T. Vogelstein; Michael P. Milham (NeuroImage, 2020)

Variability and heritability of mouse brain structure: Microscopic MRI atlases and connectomes for diverse strains. - Nian Wang 003; Robert J. Anderson; David G. Ashbrook; Vivek Gopalakrishnan; Youngser Park; Carey E. Priebe; Yi Qi; Rick Laoprasert; Joshua T. Vogelstein; Robert W. Williams; G. Allan Johnson (NeuroImage, 2020)

Cross-species functional alignment reveals evolutionary hierarchy within the connectome. - Ting Xu 001; 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; Georg Langs (NeuroImage, 2020)

BLOCKSET (Block-Aligned Serialized Trees): Reducing Inference Latency for Tree ensemble Deployment. - Meghana Madhyastha; Kunal Lillaney; James Browne; Joshua T. Vogelstein; Randal C. Burns (KDD, 2021)

Dynamic Silos: Modularity in intra-organizational communication networks before and during the Covid-19 pandemic. - Jonathan Larson; Tiona Zuzul; Emily Cox Pahnke; Neha Parikh Shah; Patrick Bourke; Nicholas Caurvina; Fereshteh Amini; Youngser Park; Joshua T. Vogelstein; Jeffrey Weston; Christopher M. White; Carey E. Priebe (CoRR, 2021)

Federated Causal Inference in Heterogeneous Observational Data. - Ruoxuan Xiong; Allison Koenecke; Michael A. Powell; Zhu Shen; Joshua T. Vogelstein; Susan Athey (CoRR, 2021)

When are Deep Networks really better than Random Forests at small sample sizes? - Haoyin Xu; Michael Ainsworth; Yu-Chung Peng; Madi Kusmanov; Sambit Panda; Joshua T. Vogelstein (CoRR, 2021)

Towards a theory of out-of-distribution learning. - Ali Geisa; Ronak D. Mehta; Hayden S. Helm; Jayanta Dey; Eric Eaton; Carey E. Priebe; Joshua T. Vogelstein (CoRR, 2021)

Streaming Decision Trees and Forests. - Haoyin Xu; Jayanta Dey; Sambit Panda; Joshua T. Vogelstein (CoRR, 2021)

Graph Matching via Optimal Transport. - Ali Saad-Eldin; Benjamin D. Pedigo; Carey E. Priebe; Joshua T. Vogelstein (CoRR, 2021)

Fitting Splines to Axonal Arbors Quantifies Relationship Between Branch Order and Geometry. - Thomas L. Athey; Jacopo Teneggi; Joshua T. Vogelstein; Daniel Jacob Tward; Ulrich Mueller; Michael I. Miller (Frontiers Neuroinformatics, 2021)

Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace. - Jesús Arroyo 001; Avanti Athreya; Joshua Cape; Guodong Chen 003; Carey E. Priebe; Joshua T. Vogelstein (J. Mach. Learn. Res., 2021)

mvlearn: Multiview Machine Learning in Python. - Ronan Perry; Gavin Mischler; Richard Guo; Theo Lee; Alexander Chang; Arman Koul; Cameron Franz; Hugo Richard; Iain Carmichael; Pierre Ablin; Alexandre Gramfort; Joshua T. Vogelstein (J. Mach. Learn. Res., 2021)

Impact of concatenating fMRI data on reliability for functional connectomics. - Jae Wook Cho; Annachiara Korchmaros; Joshua T. Vogelstein; Michael P. Milham; Ting Xu 001 (NeuroImage, 2021)

Kernel k-Groups via Hartigan's Method. - Guilherme França; Maria L. Rizzo; Joshua T. Vogelstein (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Joint Embedding of Graphs. - Shangsi Wang; Jesús Arroyo 001; Joshua T. Vogelstein; Carey E. Priebe (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics. - Eric W. Bridgeford; Shangsi Wang; Zeyi Wang; Ting Xu 001; R. Cameron Craddock; Jayanta Dey; Gregory Kiar; William R. Gray Roncal; Carlo Colantuoni; Christopher Douville; Stephanie Noble; Carey E. Priebe; et al. (PLoS Comput. Biol., 2021)

Prospective Learning: Back to the Future. - Joshua T. Vogelstein; Timothy D. Verstynen; Konrad P. Kording; Leyla Isik; John W. Krakauer; Ralph Etienne-Cummings; Elizabeth L. Ogburn; Carey E. Priebe; Randal C. Burns; Kwame S. Kutten; James J. Knierim; James B. Potash; et al. (CoRR, 2022)

Out-of-distribution Detection Using Kernel Density Polytopes. - Jayanta Dey; Ashwin De Silva; Will LeVine; Jong M. Shin; Haoyin Xu; Ali Geisa; Tiffany Chu; Leyla Isik; Joshua T. Vogelstein (CoRR, 2022)

Why Do Networks Need Negative Weights? - Qingyang Wang 002; Michael A. Powell; Ali Geisa; Eric Bridgeford; Joshua T. Vogelstein (CoRR, 2022)

The Chi-Square Test of Distance Correlation. - Cencheng Shen; Sambit Panda; Joshua T. Vogelstein (J. Comput. Graph. Stat., 2022)

Biological underpinnings for lifelong learning machines. - Dhireesha Kudithipudi; Mario Aguilar-Simon; Jonathan Babb; Maxim Bazhenov; Douglas Blackiston; Josh C. Bongard; Andrew P. Brna; Suraj Chakravarthi Raja; Nick Cheney; Jeff Clune; Anurag Reddy Daram; Stefano Fusi; et al. (Nat. Mach. Intell., 2022)

Is Neuroscience FAIR? A Call for Collaborative Standardisation of Neuroscience Data. - Jean-Baptiste Poline; David N. Kennedy; Friedrich T. Sommer; Giorgio A. Ascoli; David C. Van Essen; Adam R. Ferguson; Jeffrey S. Grethe; Michael Hawrylycz; Paul M. Thompson; Russell A. Poldrack; Satrajit S. Ghosh; David B. Keator; et al. (Neuroinformatics, 2022)

Prospective Learning: Principled Extrapolation to the Future. - Ashwin De Silva; Rahul Ramesh; Lyle H. Ungar; Marshall G. Hussain Shuler; Noah J. Cowan; Michael L. Platt; Chen Li; Leyla Isik; Seung-Eon Roh; Adam S. Charles; Archana Venkataraman; Brian Caffo; et al. (CoLLAs, 2023)

Why do networks have inhibitory/negative connections? - Qingyang Wang 002; Michael A. Powell; Ali Geisa; Eric Bridgeford; Carey E. Priebe; Joshua T. Vogelstein (ICCV, 2023)

Polarity Is All You Need to Learn and Transfer Faster. - Qingyang Wang 002; Michael Alan Powell; Eric W. Bridgeford; Ali Geisa; Joshua T. Vogelstein (ICML, 2023)

The Value of Out-of-Distribution Data. - Ashwin De Silva; Rahul Ramesh; Carey E. Priebe; Pratik Chaudhari; Joshua T. Vogelstein (ICML, 2023)

Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis. - Hayden S. Helm; Weiwei Yang 004; Ashwin De Silva; Joshua T. Vogelstein; Carey E. Priebe (CoRR, 2023)

Learning sources of variability from high-dimensional observational studies. - Eric W. Bridgeford; Jaewon Chung; Brian Gilbert; Sambit Panda; Adam Li; Cencheng Shen; Alexandra Badea; Brian Caffo; Joshua T. Vogelstein (CoRR, 2023)

Distance-based positive and unlabeled learning for ranking. - Hayden S. Helm; Amitabh Basu; Avanti Athreya; Youngser Park; Joshua T. Vogelstein; Carey E. Priebe; Michael Winding; Marta Zlatic; Albert Cardona; Patrick Bourke; Jonathan Larson; Marah Ihab Abdin; et al. (Pattern Recognit., 2023)

First Organoid Intelligence (OI) workshop to form an OI community. - Itzy E. Morales Pantoja; Lena Smirnova; Alysson R. Muotri; Karl J. Wahlin; Jeffrey Kahn; J. Lomax Boyd; David H. Gracias; Timothy D. Harris; Tzahi Cohen-Karni; Brian S. Caffo; Alexander S. Szalay; Fang Han; et al. (Frontiers Artif. Intell., 2023)

Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks. - Adam Li; Ronan Perry; Chester Huynh; Tyler M. Tomita; Ronak Mehta; Jesús Arroyo 001; Jesse Patsolic; Benjamin Falk; Sridevi V. Sarma; Joshua T. Vogelstein (SIAM J. Math. Data Sci., 2023)

BrainLine: An Open Pipeline for Connectivity Analysis of Heterogeneous Whole-Brain Fluorescence Volumes. - Thomas L. Athey; Matthew A. Wright; Marija Pavlovic; Vikram Chandrashekhar; Karl Deisseroth; Michael I. Miller; Joshua T. Vogelstein (Neuroinformatics, 2023)

Discovering a change point and piecewise linear structure in a time series of organoid networks via the iso-mirror. - Tianyi Chen; Youngser Park; Ali Saad-Eldin; Zachary Lubberts; Avanti Athreya; Benjamin D. Pedigo; Joshua T. Vogelstein; Francesca Puppo; Gabriel A. Silva; Alysson R. Muotri; Weiwei Yang 004; Christopher M. White; et al. (Appl. Netw. Sci., 2023)

T-Rex (Tree-Rectangles): Reformulating Decision Tree Traversal as Hyperrectangle Enclosure. - Meghana Madhyastha; Tamas Budavari; Vladimir Braverman; Joshua T. Vogelstein; Randal C. Burns (ICDE, 2024)

Prospective Learning: Learning for a Dynamic Future. - Ashwin De Silva; Rahul Ramesh; Rubing Yang; Siyu Yu; Joshua T. Vogelstein; Pratik Chaudhari (NeurIPS, 2024)

Discovering the signal subgraph: An iterative screening approach on graphs. - Cencheng Shen; Shangsi Wang; Alexandra Badea; Carey E. Priebe; Joshua T. Vogelstein (Pattern Recognit. Lett., 2024)

Independence Testing for Temporal Data. - Cencheng Shen; Jaewon Chung; Ronak D. Mehta; Ting Xu 001; Joshua T. Vogelstein (Trans. Mach. Learn. Res., 2024)

Preserving Derivative Information while Transforming Neuronal Curves. - Thomas L. Athey; Daniel Jacob Tward; Ulrich Mueller; Laurent Younes; Joshua T. Vogelstein; Michael I. Miller (Neuroinformatics, 2024)

Prospective Learning in Retrospect. - Yuxin Bai; Cecelia Shuai; Ashwin De Silva; Siyu Yu; Pratik Chaudhari; Joshua T. Vogelstein (AGI (1), 2025)

Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architectures. - Siyu Yu; Zihan Qin; Tingshan Liu; Beiya Xu; R. Jacob Vogelstein; Jason Brown; Joshua T. Vogelstein (AGI (2), 2025)

Multiple network embedding for anomaly detection in time series of graphs. - Guodong Chen 003; Jesús Arroyo 001; Avanti Athreya; Joshua Cape; Joshua T. Vogelstein; Youngser Park; Christopher M. White; Jonathan Larson; Weiwei Yang 004; Carey E. Priebe (Comput. Stat. Data Anal., 2025)

Dynamic Silos: Increased Modularity and Decreased Stability in Intraorganizational Communication Networks During the COVID-19 Pandemic. - Tiona Zuzul; Emily Cox Pahnke; Jonathan Larson; Patrick Bourke; Nicholas Caurvina; Neha Parikh Shah; Fereshteh Amini; Jeffrey Weston; Youngser Park; Joshua T. Vogelstein; Christopher M. White; Carey E. Priebe (Manag. Sci., 2025)

Simple Calibration via Geodesic Kernels. - Jayanta Dey; Haoyin Xu; Ashwin De Silva; Joshua T. Vogelstein (Trans. Mach. Learn. Res., 2025)