NCSI: Sequential Monte Carlo (SMC) Methods for Data Assimilation

Status	Completed
Mentor Name	Sanish Rai
Mentor's XSEDE Affiliation	Undergraduate Faculty
Mentor Has Been in XSEDE Community	Less than 1 year
Project Title	Sequential Monte Carlo (SMC) Methods for Data Assimilation
Summary	The student will need to work on implementing sequential monte carlo (SMC) methods using XSEDE resources for data assimilation purposes. SMC methods will be implemented for simulating real-time applications (building occupancy estimation).
Job Description	The student will need to work on building simulation software for creating various occupancy scenarios. Sensor data will be collected for occupancy behaviors for various time periods. Data analysis will be performed on the collected data to study the behaviors of occupants. Sequential Monte Carlo methods algorithm will be implemented to estimate the behavior in real time. Existing algorithm will be modified to make the process efficient. Other machine learning algorithms will also be explored for real-time application.
Computational Resources	Simulation process of occupancy behavior is computationally expensive. Due to the high number of agents and building structure, as the number of occupants increases the process becomes slow. As such computational resources of XSEDE will be utilized to increase the efficiency of the simulation. Parallel processing for agent modeling will be researched. Data assimilation is the process of using real-time (sensor) data with sequential monte carlo method to make dynamic estimation. Due to complex states of sequential monte carlo methods, when the occupancy is increased data assimilation become slow and efficient estimation cannot be achieved. The research aims to look into use of XSEDE resources to increase the computational efficiency of the overall real time estimation process.
Contribution to Community
Position Type	Learner
Training Plan	We will need assistance in developing a training plan.
Student Prerequisites/Conditions/Qualifications
Duration	Semester
Start Date	08/13/2018
End Date	11/30/2018