Mixed-integer programming saves the day. Taking a cue from consumer supply chains and the data-driven advances that have revolutionized them in recent decades, Gabe Verzino walks us through a scheduling program that would empower both patients and healthcare providers to use their time more efficiently. Bayes' Theorem might sound, well, theoretical. As Khuyen Tran shows in her recent tutorial (based on the traffic patterns of her own website), it can also be a powerful tool for detecting and analyzing change points in your data. The road to the perfect shot of espresso passes through a lot of data.

MIT researchers have devised a method for assessing how robust machine-learning models known as neural networks are for various tasks, by detecting when the models make mistakes they shouldn't. Convolutional neural networks (CNNs) are designed to process and classify images for computer vision and many other tasks. But slight modifications that are imperceptible to the human eye -- say, a few darker pixels within an image -- may cause a CNN to produce a drastically different classification. Such modifications are known as "adversarial examples." Studying the effects of adversarial examples on neural networks can help researchers determine how their models could be vulnerable to unexpected inputs in the real world.

We consider multi-agent planning in which the agents' optimal plans are solutions to mixed-integer programs (MIP) that are coupled via integer constraints. While in principle, one could find the joint solution by combining the separate problems into one large joint centralized MIP, this approach rapidly becomes intractable for growing numbers of agents and large problem domains. To address this issue, we propose an iterative approach that combines conflict detection with constraint-generation whereby the agents plan repeatedly until all conflicts are resolved. In each planning iteration, the agents plan with as few other agents and interaction-constraints as possible. This yields an optimal method that can reduce computation markedly. We test our approach in the context of multi-agent collision avoidance in graphs with indivisible flows. Our initial simulations on randomized graph routing problems confirm predicted optimality and reduced computational effort.