About Me

I am a PhD student in Civil Engineering at Utah State University and a Graduate Research Assistant at the Utah Water Research Laboratory. My research focuses on the application of artificial intelligence, computer vision, and intelligent sensing technologies in civil and environmental engineering, with particular emphasis on smart hydrologic monitoring and autonomous field measurement systems.

Before starting my PhD, I completed my MS in Civil Engineering at Kyungpook National University in South Korea through a fully funded scholarship. During my master's research, I worked primarily on computer vision and deep learning applications for structural monitoring, including structural crack detection and monocular depth estimation. This experience established the foundation for my broader interest in applying artificial intelligence to practical civil engineering problems.

My current research focuses on developing camera based approaches for measuring river stage, surface velocity, and discharge, together with intelligent monitoring systems that integrate deep learning, edge computing, and field sensing. My broader interests include artificial intelligence applications in civil engineering, smart hydrology, computer vision, deep learning, structural health monitoring, synthetic data generation, and autonomous environmental sensing.

Research Interests

  • Artificial Intelligence in Civil and Environmental Engineering
  • Smart Hydrology and Intelligent Water Monitoring
  • Computer Vision and Deep Learning
  • Structural Health Monitoring
  • Edge AI and Autonomous Sensing

Education

PhD in Civil Engineering, 2023 to Present (Expected August 2027)
Utah State University
Dissertation: Advancing Camera Based Hydrologic Monitoring Using Artificial Intelligence and Computer Vision.
MS in Civil Engineering, 2021 to 2023Kyungpook National University, South Korea
Thesis: Crack Detection Using Efficient Nested Res-UNet with CBAM.
BS in Civil Engineering, 2016 to 2020University of Engineering and Technology, Pakistan
Thesis: Testing Fly Ash Based Geopolymer Concrete as a Full Replacement for Ordinary Portland Cement Concrete.

Research Projects

Selected projects connecting AI, computer vision, field sensing, hydrology, and infrastructure monitoring.

Reprojected surface velocity vectors over a river scene

River Surface Velocity and Discharge Estimation

Fixed camera monitoring with deep optical flow, camera calibration, surface velocity reconstruction, and discharge estimation in real river environments.

Optical flowRiver dischargeComputer vision
Water level segmentation mask over a river image

Camera Based Water Level Monitoring

AI enabled image analysis and segmentation workflows for continuous river stage and hydrologic monitoring using low cost camera systems.

Water levelSegmentationField sensing
Annotated landslide area highlighted on a hillside image

Landslide Monitoring with Fixed Cameras

Field deployment of high resolution camera systems and autonomous data collection for non contact monitoring of slope movement and changing terrain.

Natural hazardsRemote sensingMonitoring
Example RGB river images and corresponding depth maps

Synthetic River RGB and Depth Dataset

Development of paired RGB imagery and depth maps to study environmental variability and improve computer vision models for river monitoring.

Synthetic dataDepth estimationDataset
Crack segmentation examples showing input images and masks

Structural Crack Detection

Deep learning methods for automated crack segmentation from drone and handheld imagery, with attention based architectures for structural condition assessment.

Structural healthSegmentationDeep learning