  Distributed, parallel crowd workers can accomplish simple tasks through
workflows, but teams of collaborating crowd workers are necessary for complex
goals. Unfortunately, a fundamental condition for effective teams - familiarity
with other members - stands in contrast to crowd work's flexible, on-demand
nature. We enable effective crowd teams with Huddler, a system for workers to
assemble familiar teams even under unpredictable availability and strict time
constraints. Huddler utilizes a dynamic programming algorithm to optimize for
highly familiar teammates when individual availability is unknown. We first
present a field experiment that demonstrates the value of familiarity for crowd
teams: familiar crowd teams doubled the performance of ad-hoc (unfamiliar)
teams on a collaborative task. We then report a two-week field deployment
wherein Huddler enabled crowd workers to convene highly familiar teams in 18
minutes on average. This research advances the goal of supporting long-term,
team-based collaborations without sacrificing the flexibility of crowd work.
