At LS18, Katie Storey used a stochastic model of GBM response to standard treatment to investigate MGMT methylation in TMZ resistance. 

At LS18, William Holmes reconciles two existing decision-making hypotheses that were previously thought to be mutually exclusive.

Subekshya Bidari examined collective decision-making in honeybees foraging in a dynamic environment at LS18.

Paul Macklin used an agent-based modeling framework to explore the role of hypoxia in breast cancer propagation at LS18.

David Prager compared the Markov chain model with the ARIMA time series approach to analyze drought in the U.S. during LS18.

At LS18, Daniel Forger spoke about the math behind auditory processing, musical representation, and the neuroscience of performance.

The SIAM Activity Group on Imaging Science held the 9th SIAM Conference on Imaging Science (IS18) in Bologna, Italy.

In a more realistic view of cancer dynamics, fitness values of different genotypes are subject to microenvironmental variations.

Bebart M. Janbek and John M. Stockie present a multidimensional porous medium model that measures sap flow within a tree stem.

The difficulty of quantifying uncertainty in complex models, such as predicting a storm or hurricane's path, must be met with urgency.

At AN18, Kyle Gustafson of the U.S. Navy used parsimonious model selection to model the spread of Ebola in Sierra Leone.

Machine learning approaches can help choose the right locations for new oil wells and for identifying “sweet spots” for drilling.

A comprehensive algorithm estimates the expected occupancy requirement for Amazon lockers based on shipping speed and date.