Current research
submitted for review at a major conference
My current research, under the direction of Dr. Ali Jannesari and in coordination with both Hydrologists and other ML researchers at Iowa State and the University of Iowa, is on producing novel distributed approaches for Graph Neural Networks. We plan to use spatiotemporal versions of these approaches to predict water flow quickly, efficiently, and accurately.
An approach has already been submitted for review at a major conference, and additional research is ongoing.
presented at FIE 2023
This work presented results for tracking team-based learning attendance in an indoor setting using both cameras and LiDAR sensors. It found that the much more mature, camera-based camera methodology (which used YOLOV6) was more accurate, but that students found it much more intrusive. The LiDAR based approach was a very simple Convolutional Neural Network (CNN) which was slightly less accurate, but still obtained promising results.
Routedoc: Routing with Distance, Origin and Category Constraints
presented at SSTD 2023
This work demonstrated two new pathfinding algorithms which can be used to solve a modified version of the traveling salesman problem. The algorithms can be used to recommend hotels based on a number of constraints, namely distance and any number of points of interest (POI) within any number of POI categories. On average they are much faster and find reasonable paths to POI fitting the restraints. Our implementation ran on a server and used caching, allowing a user to provide a query through our frontend UI and receive a path very quickly. The algorithms were showcased using hotels in New York and Airbnbs in Chicago.
