Selected Publications
Universal limits to parallel processing capability of network architectures
The ability to learn tasks and generalize themis one of the most remarkable characteristics of general intelligence. So is the ability to perform multiple tasks simultaneously. Here, we show that these two characteristics are in tension, reflecting a fundamental tradeoff between interactive parallelism that supports learning and generalization, and independent parallelism that supports processing efficiency through concurrent multitasking.
in preparation
Analysis of Big Data in Gait Biomechanics: Current Trends and Future Directions
We provide a review of recent research and development in multivariate and machine learning methods-based gait analysis that can be applied to big data analytics and describe how TDA can contribute to the field.
In J. Med. Bio. Eng.
Construction of and efficient sampling from the simplicial configuration model
Simplicial complexes are a popular way to describe multi-node interactions explicitly. We propose here a candidate to a principled null model allowing for easy comparison with empirical data. We provide an efficient and uniform Markov chain Monte Carlo sampler for this model and show its usefulness in a number of case studies.
arXiv
Topological analysis of data [editorial]
Against the rapidly evolving landscape of data science, topological data analysis (TDA) has carved itself a niche for the analysis of datasets that present complex interactions and rich structures. Its distinctive feature, topology, allows TDA to characterize the mesoscopic structures of data and high order interactions. Here we introduce the TDA paradigm and some applications.
In EPJ Data Science
On the predictability of infectious disease outbreaks
We identify a fundamental entropy barrier for time series forecasting. For most diseases this barrier is well beyond the time scale of single outbreaks, the forecast horizon varies by disease and we demonstrate that both shifting model structures and social network heterogeneity are the most likely mechanisms for the observed differences in predictability across contagions.
In arXiv
Homological scaffolds of brain functional networks
We use a simplified network representation, called scaffolds, to show dramatic changes in the mesoscale homological structure of brain activity under the effect of psilocybin.
In JRS Int.