<b>Kunle S. Oguntoye</b>

Iowa State University

Ames, Iowa

Data Science & AI/ML Research

Data Science | Artificial Intelligence | Infrastructure Management | Digital Twin

I am a PhD candidate at Iowa State University working on the intersection of Civil Infrastructure Asset Management and Computer Science. My research centers on rethinking how infrastructure condition is assessed, shifting from labor-intensive manual inspection toward data-driven, AI-enabled decision making.

At heart, I’m a data nerd. For the past 6 years, I’ve been immersed in data-related projects utilizing major data stacks including Python, SQL, PySpark, Apache Airflow, Databricks, Docker, and cloud platforms like AWS. I love working with data and extracting meaningful insights that drive business intelligence and inform actionable decisions.

More on my peer-reviewed academic works can be found on Google Scholar.

Education

• Ph.D. in Intelligent Infrastructure Engineering and Computer Science, in view
Iowa State University

• Master of Science in Intelligent Infrastructure Engineering (2024) Iowa State University

• Bachelor of Science in Civil Engineering (2019) University of Ibadan, Nigeria

Research Focus

• Computer Vision • Large Language Model • Autonomous Data Acquisition and Processing with sUAS • Data Driven Infrastructure Analytics • Data/Decision Science
• Causal Inference

Contact

oguntoye@iastate.edu
LinkedIn
Twitter

news

Feb 08, 2026	Received the GPSS Research Excellence Award
Jan 16, 2026	First-Time Presenter at TRB 2026 Annual Meeting
Sep 12, 2025	Paper Published: Maintenance Record-Enriched ML Model for Pavement IRI Prediction
Jul 14, 2025	Second Runner-Up at ISU CCEE Graduate Student Poster Competition
Jun 30, 2025	Presented at ASCE ICTD-Pavement 2025 Conference

selected publications

IJPE

Maintenance record-enriched machine learning model for pavement international roughness index prediction

Kunle S. Oguntoye, Halil Ceylan, Sunghwan Kim, and 3 more authors

International Journal of Pavement Engineering, 2025

DOI Bib HTML

@article{oguntoye2025maintenance,
  title = {Maintenance record-enriched machine learning model for pavement international roughness index prediction},
  author = {Oguntoye, Kunle S. and Ceylan, Halil and Kim, Sunghwan and Gulmezoglu, Berk and Sourav, Md. Abdullah All and Mo, Yunjeong L.},
  journal = {International Journal of Pavement Engineering},
  volume = {26},
  number = {1},
  pages = {2555992},
  year = {2025},
  publisher = {Taylor & Francis},
  doi = {10.1080/10298436.2025.2555992},
  url = {https://www.tandfonline.com/doi/pdf/10.1080/10298436.2025.2555992},
}

Sensors

Probabilistic Method to Fuse Artificial Intelligence-Generated Underground Utility Mapping

Kunle S. Oguntoye, Simon Laflamme, Roy Sturgill, and 3 more authors

Sensors, 2024

DOI Bib HTML

@article{oguntoye2024probabilistic,
  title = {Probabilistic Method to Fuse Artificial Intelligence-Generated Underground Utility Mapping},
  author = {Oguntoye, Kunle S. and Laflamme, Simon and Sturgill, Roy and Salazar Martinez, D. A. and Ceylan, Halil and Alipour, Alice},
  journal = {Sensors},
  volume = {24},
  number = {11},
  pages = {3559},
  year = {2024},
  publisher = {MDPI},
  doi = {10.3390/s24113559},
  url = {https://www.mdpi.com/1424-8220/24/11/3559/pdf},
}

Sensors

Review of artificial intelligence applications for virtual sensing of underground utilities

Kunle S. Oguntoye, Simon Laflamme, Roy Sturgill, and 1 more author

Sensors, 2023

DOI Bib HTML

@article{oguntoye2023review,
  title = {Review of artificial intelligence applications for virtual sensing of underground utilities},
  author = {Oguntoye, Kunle S. and Laflamme, Simon and Sturgill, Roy and Eisenmann, David J.},
  journal = {Sensors},
  volume = {23},
  number = {9},
  pages = {4367},
  year = {2023},
  publisher = {MDPI},
  doi = {10.3390/s23094367},
  url = {https://www.mdpi.com/1424-8220/23/9/4367/pdf},
}