Abstract: Staging is a powerful language construct that allows a program at one stage of evaluation to manipulate and specialize a program to be executed at a later stage. We propose a new staged language calculus, 〈ML〉, which extends the programmability of staged languages in two directions. First, 〈ML〉 supports dynamic type specialization: types can be dynamically constructed, abstracted, and passed as arguments, while preserving decidable typechecking via a System F≤-style semantics combined with a restricted form of λ ω -style runtime type construction. With dynamic type specialization the data structure layout of a program can be optimized via staging. Second, 〈ML〉 works in a context where different stages of computation are executed in different process spaces, a property we term staged process separation. Programs at different stages can directly communicate program data in 〈ML〉 via a built-in serialization discipline. The language 〈ML〉 is endowed with a metatheory including type preservation, type safety, and decidability as demonstrated constructively by a sound type checking algorithm. While our language design is general, we are particularly interested in future applications of staging in resource-constrained and embedded systems: these systems have limited space for code and data, as well as limited CPU time, and specializing code for the particular deployment at hand can improve efficiency in all of these dimensions. The combination of dynamic type specialization and staging across processes greatly increases the utility of staged programming in these domains. We illustrate this via wireless sensor network programming examples.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).