Threading Space is a kinetic sculpture that explores how spatial perception can be transformed by dynamically and geometrically reconfiguring physical lines of thread. As the threads in motion interact, they become a hypnotic medium for three-dimensional patterns. Through a physical installation and an interactive GUI, Threading Space invites the audience to explore the potential of using swarm robots and line elements to create, morph, and interact with space.

We address the Virtual Network Embedding (VNE) problem in which the task is to map a virtual network onto a given physical substrate network so that the CPU and bandwidth capacity constraints are met. Following the success of Boolean Satisfiability (SAT) methods in areas such as Multi-Agent Path Finding (MAPF), we propose in this paper a novel SAT-based approach for solving the VNE problem. As in MAPF, the various constraints that define the VNE problem are encoded into the SAT models incrementally and via lazy refinements so as to keep the models simple. We also propose various model relaxations and concomitant solution extraction post-processing procedures. Through experiments, we show that our SAT-based approach outperforms other state-of-the-art approaches on a number of VNE instances.

Virtualization is the mechanism of creating virtual representations of physical resources. It is now integrated into almost every facet of computing and is pervasive on the Internet: ranging from data center services and cloud computing services to services on our phones. The common goal for virtualization providers is to ensure that the physical resources are managed efficiently and effectively. This goal induces the Virtual Network Embedding (VNE) problem: the task of properly allocating the physical resources of a network to satisfy virtual requests for resources under various constraints while ensuring the quality of service and maximizing resource utilization. The VNE problem captures many resource allocation tasks arising in computer systems and computer networks. In this paper, we present Improved VNE-CBS (iVNE-CBS) as an efficient and effective algorithm for solving the VNE problem. iVNE-CBS builds on Conflict-Based Search (CBS), a heuristic search framework borrowed from the Multi-Agent Path Finding literature. We show that iVNECBS significantly outperforms popular baseline VNE algorithms: it scales to networks with several hundreds of vertices and thousands of edges, while also producing better-quality solutions.

(Dis)Appearables is an approach for actuated Tangible User Interfaces (TUIs) to appear and disappear. This technique is supported by Stages: physical platforms inspired by theatrical stages. Self-propelled TUI’s autonomously move between front and back stage allowing them to dynamically appear and disappear from users’ attention. This platform opens up a novel interaction design space for expressive displays with dynamic physical affordances.

Multi-Agent Path Finding (MAPF) is the combinatorial problem of finding collision-free paths for multiple agents on a graph. This paper describes MAPF-based software for solving train planning and replanning problems on large-scale railway networks under uncertainty. The software recently won the 2020 Flatland Challenge, a NeurIPS competition trying to determine how to efficiently manage dense traffic on rail networks. The software incorporates many state-of-the-art MAPF, or in general, optimization technologies, such as prioritized planning, large neighborhood search, safe interval path planning, minimum communication policies, parallel computing, and simulated annealing. It can plan collision-free paths for thousands of trains within a few minutes and deliver deadlock-free actions in real-time during execution.