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Colloquium Talk with Seonghyeon Moon, April 19: An Integrated Platform For Joint Simulation of Occupant-building Interactions

Seonghyeon Moon, Ph.D. student at Rutgers University in the Department of Computer Science, will give a colloquium talk, titled “An Integrated Platform For Joint Simulation of Occupant-building Interactions” on Tuesday, April 19 from 12:30 – 1:30 PM in Science Complex P101.

See below for more information about Seonghyeon Moon and his research.

Abstract: Several approaches exist for simulating building properties (e.g. temperature, noise) and human occupancy (e.g. movement, actions) in an isolated fashion, providing limited ability to represent how environmental features affect human behaviour and vice versa. To systematically model building-occupant interactions, several requirements must be met, including the modelling of (a) interdependent multi-domain phenomena ranging from temperature and sound changes to human movement, (b) high-level occupant planning and low-level steering behaviours, (c) environmental and occupancy phenomena that unfold at different time scales, and (d) multiple strategies to represent occupancy using established models. In this work, we propose an integrated platform that satisfies the aforementioned requirements thus enabling the joint simulation of building-occupant interactions. To this end, we combine the benefits of a model-independent, discrete-event, general-purpose framework with an established crowd simulator. Our platform provides insights on a building’s performance while accounting for alternative design features and modelling strategies.

Speaker Bio: Seonghyeon Moon is a 4th year Ph.D. student in Computer Science from Rutgers University.  Seonghyeon has a background in simulation and computer vision. His previous works involve enhancing occupant behavior simulation engine and starting a new ensemble of SyDEVS models for buildings. Currently, going further from simulation, he is conducting research on pedestrians movement prediction and he’s working on few-shot object segmentation which is the most basic challenge to computer vision.