About

At the forefront of innovation in parallel, distributed, and data-intensive computing and data-driven intelligence, our lab is dedicated to advancing the boundaries of fundamental and translational research. We specialize in developing cutting-edge algorithms, software, and solutions to tackle some of the most complex and impactful challenges in the field. Our research encompasses a broad range of areas, including parallel data structures, high-performance computation over spatio-temporal datasets, and sophisticated machine learning frameworks and applications.

Our work is distinguished by its multidisciplinary approach and significant contributions to parallel data structures and algorithms, data-intensive computations, discrete event simulation, and cloud-based high-performance computing. From pioneering scalable priority queue structures and GPU-based geographic information systems to advancing machine learning techniques for polar satellite data analytics and energy optimization on edge devices, we are committed to driving innovation and collaboration across diverse scientific and technological domains. Join us as we continue to push the boundaries of parallel processing and data intelligence, shaping the future of computing and machine learning. We are actively recruiting multiple PhD and MS students on new NSF-funded projects.

Ongoing Projects


ScooterLab - A Programmable and Participatory Sensing Testbed using Micromobility Vehicles

ScooterLab is a National Science Foundation (NSF) funded community research infrastructure initiative, currently under development at the University of Texas at San Antonio (UTSA). This publicly-available micromobility testbed and crowd-sensing/crowd-sourcing infrastructure will provide researchers access to a community of riders and a fully operational fleet of highly customizable dockless e-scooters.

Approximate Nearest Neighbor Similarity Search for Large Polygonal and Trajectory Datasets

Similarity searches are a critical task in data mining. Nearest neighbor similarity search over geometrical shapes - polygons and trajectories - are used in various domains such as digital pathology, solar physics, and geospatial intelligence. In digital pathology for tumor diagnosis, tissues are represented as polygons and Jaccard distance - ratio of areas of intersection to union - is used for similarity comparisons...

AI-driven Model for fault prediction in non-linear dynamic automatic system

Fault detection in automotive engine systems is one of the most promising research areas. Several works have been done in the field of model-based fault diagnosis. Many researchers have discovered more advanced methods and algorithms for better fault detection on the highly nonlinear dynamic engine of any automotive system...

Recent Publications