Geospatial Optimization & Analytics Lab-

Antonio Medrano

GOAL Director

Antonio is an assistant professor of geospatial engineering in the Department of Computing Sciences at Texas A&M University–Corpus Christi (TAMUCC). He is a part of the Conrad Blucher Institute of Surveying and Science (CBI), and the GeoSpatial Computer Science (GSCS) PhD program. As the director of the Geospatial Optimization & Analytics Lab (GOAL), he helps to advise grad students on research related to location optimization, spatial logistics, high performance computing, and artificial intelligence & machine learning, with applications to drone systems, coastal environmental science, and facility location.

Abhishek Phadke

GSCS PhD Student, F2019 – present

Abhishek is working to develop resilient systems that can be scaled and applied to real-world applications such as unmanned aerial vehicle swarms and electric grids, with the intent of solving current issues that plague UAV flocks during mission execution. For developing the system backend algorithms, he uses machine learning and AI to ensure system adaptability and graceful extensibility. Prior to joining TAMUCC, he earned his master’s degree in Electrical and Computer Engineering from Texas A&M University–Kingsville.

More info: Website, CV

Marina Vicens Miquel

GSCS PhD Student, F2020 – present

Marina’s research focuses on using AI techniques to solve geospatial computer science problems, typically with UAV Imagery. She has worked in flood detection from UAVs using recurrent neural networks (RNN), and UAV damage assessment using deep learning. She is currently working on wet/dry shoreline geo-detection by applying deep learning analysis to UAV imagery as a part of the AI2ES NSF Institute. Prior to starting her PhD, Marina earned a double major BS in Math and Computer Science, and competed on the TAMUCC NCAA Women’s Tennis team.

More info: Website, CV

Adarsh Kesireddy

GSCS PhD Student, F2019 – present

Adarsh's research focuses on multi-agent, multi-objective optimization, developing new heuristics using evolutionary algorithms, game theory, and machine learning. This research is applied toward multi-robot systems where each agent is autonomous with it’s own priorities, while also maximizing overall system performance. Prior to joining TAMUCC, he earned his master’s degree in Mechanical Engineering from the University of Texas at Tyler.

More info: CV

Gabriel Nambila

GSEN MS Student, F2021 – present

Gabriel is studying to learn how to develop infrastructure for smart-cities. His goal is to find innovative ways to integrate geospatial technology and cloud computing to city architecture to improve urban development efficiency and to provide better service to its citizens. Gabriel earned his BS in GIST from Texas A&M University–College Station.

More info: LinkedIn