Senior Research Scientist | Generative AI | Computer Vision | Deep Learning | Digital Twins | Industrial AI

I am a Senior Research Scientist specializing in Generative AI, Computer Vision, and Multimodal Learning for civil infrastructure and large-scale industrial systems.

I hold a Ph.D. in Civil Engineering with a strong emphasis on applied computer vision and machine learning, and I have worked across academia, industry, and applied R&D to build scalable, production-ready AI systems.

Current and recent roles include:

• Senior Research Scientist, Augrade Inc., USA — Generative AI & Computer Vision
• Research Scientist, Siemens AI, USA — Industrial Computer Vision & Digital Twins
• Senior Research Associate, University of Southern California (Dept. of Civil Engineering)
• Journal Reviewer — SAGE Structural Health Monitoring, Elsevier Automation in Construction, Springer Nonlinear Dynamics

My research lies at the intersection of Generative AI, Computer Vision, Deep Learning, and Image Processing, with a strong emphasis on real-world deployment and data-centric AI.

Core focus areas include:

• Generative AI

  • Synthetic data generation for vision tasks
  • GANs, DDPMs, Latent Diffusion Models (LDMs)
  • Unpaired image-to-image translation
  • Semantic mask synthesis and augmentation

• Computer Vision & Deep Learning

  • Crack detection, segmentation, and change detection
  • Semantic segmentation, object detection, defect classification
  • CNNs, Transformers, RNNs, and hybrid architectures
  • Classical + deep learning fusion pipelines

• Synthetic Dataset Generation

  • Large-scale synthetic pipelines for:
    • Structural cracks (concrete, pavement, steel)
    • AEC drawings (architectural, structural, MEP)
  • Algorithmic crack generation (~1.5s per sample)
  • Data diversity beyond physics-based CG and FEM approaches

• Digital Twins & Industrial AI

  • 2D-to-3D CAD model generation
  • Digital twin synchronization using:
    • Point clouds
    • RGB and RGB-D images
    • Multimodal sensor data
  • Graph-based representations and hierarchical assembly graphs
  • GNN-based parametrization and reconfiguration

• Multimodal Data

  • RGB, depth, RGB-D
  • 3D point clouds
  • Image–geometry–graph fusion

• Concrete, pavement, and steel infrastructure
• Structural and mechanical systems
• AEC, industrial facilities, and large-scale assets
• Vision-based inspection and monitoring systems

My work is driven by:

• Data-centric AI
• Algorithmic rigor
• Scalable and reproducible pipelines
• Bridging academic research with industrial deployment

I focus on building systems that actually work in practice—not just benchmarks.

This GitHub profile contains:

• End-to-end synthetic data generation pipelines
• Computer vision tools for structural inspection
• Point cloud annotation and review tools
• Multimodal learning experiments
• Reproducible research-grade codebases

Most repositories emphasize:

• Clean architecture
• Reproducibility
• Performance-aware design
• Practical usability

I am open to:

• Research collaborations
• Industrial R&D projects
• Synthetic data and Generative AI projects
• Open-source collaborations

Best ways to reach me:

• LinkedIn (professional inquiries)
• GitHub Issues / Discussions (technical discussions)

This profile reflects ongoing research and applied development in Generative AI, Computer Vision, and Digital Twin technologies for civil and industrial AI systems.