I am always open to expand my knowledge and experience by learning new information and working on different projects, to find better ways to express my ideas by learning new technologies, and to solve practical spatial problems in order to make the world a better place.
I am one of the first among graduate students in my department whose research is in the area of Spatial Data Science. With a master's degree in GIS Engineering and a bachelor's degree in Geodesy and Geomatics Engineering, my background includes a great deal of Math, Probability and (Geo-)Statistics, and programming and databases. Considering my background and skills and my research interests, I decided to conduct a research in the area of geospatial machine learning predictive modeling which focuses on Semi-supervised learning. Therefore, I refreshed my previous knowledge and developed a solid and excellent understanding of Machine Learning principles and concepts. Now, I am an active learner in Spatial Data Science skills.
Besides being a spatial data scientists, I have gained the required expertise in order to define myself as a full stack web gis developer and gis analyst. Regarding this, I have gained experience in designing and building web gis applications and have contributed to different spatial problems through different professional positions and academic projects.
Research Interests: Machine Learning · Spatial and spatiotemporal analysis and modeling · Navigation and wayfinding · Big (geo) data · Spatial Data Mining · Social Media Analysis · Volunteered Geographic Information · Web GIS and Mobile GIS.
Providing smartphone navigation services using wireless sensor networks and decentrilized spatial computing during hazards and emergency situations. more
An approach for safer navigation under severe hurricane damage
The aim of this paper is to present a conceptual model so as to consider “Fitness-for-Use” indicator as a key to better storage of data. more
Introducing a Conceptual Model to Improve the Quality of Storage of Volunteered Geographic Information: In the Field of "Fitness-for-Use" Indicator
