Thursday, January 19, 2017

​We propose an alternative data-driven method to infer networked nonlinear dynamical systems by using sparsity-promoting optimization to select a subset of nonlinear interactions representing dynam

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Tuesday, January 17, 2017
The integration of nonlinear dynamics and machine learning opens the door for principled versus heuristic methods for model construction, nonlinear control strategies, and sensor placement techniques.
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Wednesday, January 11, 2017
​In this paper, we develop low-dimensional models for how an SIR disease will spread if it transmits through a sexual contact network and some other transmission mechanism, such as direct contact or v
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Wednesday, January 11, 2017
Using a computational model of the Caenorhabditis elegans connectome dynamics, we show that proprioceptive feedback is necessary for sustained dynamic responses to external input. ​
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Friday, January 6, 2017
We develop an algorithm for model selection which allows for the consideration of a combinatorially large number of candidate models governing a dynamical system.
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Monday, January 2, 2017
The VMMC program for HIV prevention has proven successful in reaching a large population of uncircumcised men in western Kenya, but as of 2014, pockets of low circumcision coverage still existed.​​
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Tuesday, December 27, 2016

Mathematical modeling is used to evaluate mosquito gene-drive approaches in realistically seasonal spatial settings and to determine constraints on gene-construct parameters and release strategies.

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Thursday, December 1, 2016
​This work develops compressed sensing strategies for computing the dynamic mode decomposition (DMD) from heavily subsampled or compressed data. ​
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Friday, November 25, 2016

SParSE++ is an event-oriented stochastic parameter search algorithm that features various leaping methods and improved interpolation over the existing algorithm SParSE.​

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Wednesday, November 23, 2016

​In the Lake Kariba region of Zambia, villages with high and low malaria burden are interconnected, making elimination potentially very challenging.

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