71 citations · 169 across the 18 of their papers we have counts for
18 papers
Detecting Perspective Shifts in Multi-agent Systems
Eric Bridgeford, Hayden Helm
Generative models augmented with external tools and update mechanisms (or \textit{agents}) have demonstrated capabilities beyond intelligent prompting of base models. As agent use…
Ten Simple Rules for AI-Assisted Coding in Science
Eric W. Bridgeford, Iain Campbell, Zijao Chen +4
While AI coding tools have demonstrated potential to accelerate software development, their use in scientific computing raises critical questions about code quality and scientific…
How causal perspectives can inform neuroscience data analysis
Eric W. Bridgeford, Brian S. Caffo, Maya B. Mathur +1
Over the past two decades, considerable strides have been made in advancing neuroscientific techniques, yet challenges remain in attributing causality to observed associations. Thi…
Learning sources of variability from high-dimensional observational studies
Eric W. Bridgeford, Jaewon Chung, Brian Gilbert +6
Causal inference studies whether the presence of a variable influences an observed outcome. As measured by quantities such as the "average treatment effect," this paradigm is emplo…
Polarity is all you need to learn and transfer faster
Qingyang Wang, Michael A. Powell, Ali Geisa +2
Natural intelligences (NIs) thrive in a dynamic world - they learn quickly, sometimes with only a few samples. In contrast, artificial intelligences (AIs) typically learn with a pr…
Why do networks have inhibitory/negative connections?
Qingyang Wang, Michael A. Powell, Ali Geisa +3
Why do brains have inhibitory connections? Why do deep networks have negative weights? We propose an answer from the perspective of representation capacity. We believe representing…
The Phantom Alignment Strength Conjecture: Practical use of graph matching alignment strength to indicate a meaningful graph match
Donniell E. Fishkind, Felix Parker, Hamilton Sawczuk +5
The alignment strength of a graph matching is a quantity that gives the practitioner a measure of the correlation of the two graphs, and it can also give the practitioner a sense f…
Multiscale Comparative Connectomics
Vivek Gopalakrishnan, Jaewon Chung, Eric Bridgeford +8
The connectome, a map of the structural and/or functional connections in the brain, provides a complex representation of the neurobiological phenotypes on which it supervenes. This…
Statistical Analysis of Data Repeatability Measures
Zeyi Wang, Eric Bridgeford, Shangsi Wang +2
The advent of modern data collection and processing techniques has seen the size, scale, and complexity of data grow exponentially. A seminal step in leveraging these rich datasets…
hyppo: A Multivariate Hypothesis Testing Python Package
Sambit Panda, Satish Palaniappan, Junhao Xiong +4
We introduce hyppo, a unified library for performing multivariate hypothesis testing, including independence, two-sample, and k-sample testing. While many multivariate independence…
GraSPy: Graph Statistics in Python
Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford +3
We introduce GraSPy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations. This package provides flexible a…
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Carey E. Priebe, Youngser Park, Joshua T. Vogelstein +6
Clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering - clustering the vertices of a graph based o…
A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data
Randal Burns, Eric Perlman, Alex Baden +27
Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies. We have developed a computational ecosystem that enables storage, visual…
NeuroStorm: Accelerating Brain Science Discovery in the Cloud
Gregory Kiar, Robert J. Anderson, Alex Baden +24
Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales. However, our ability to capitalize on existing datasets, tools, and intellectual capa…
Vertex nomination: The canonical sampling and the extended spectral nomination schemes
Jordan Yoder, Li Chen, Henry Pao +5
Suppose that one particular block in a stochastic block model is of interest, but block labels are only observed for a few of the vertices in the network. Utilizing a graph realize…
Supervised Dimensionality Reduction for Big Data
Joshua T. Vogelstein, Eric Bridgeford, Minh Tang +4
To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will b…
Discovering and Deciphering Relationships Across Disparate Data Modalities
Joshua T. Vogelstein, Eric Bridgeford, Qing Wang +3
Understanding the relationships between different properties of data, such as whether a connectome or genome has information about disease status, is becoming increasingly importan…
Small-World Propensity in Weighted, Real-World Networks
Sarah Feldt Muldoon, Eric W. Bridgeford, Danielle S. Bassett
Quantitative descriptions of network structure in big data can provide fundamental insights into the function of interconnected complex systems. Small-world structure, commonly dia…