Graduate Research/Teaching Assistant at Case Western Reserve University, United States
Mr. Samuel Ojo is a Ph.D. candidate in Civil Engineering at Case Western Reserve University, focusing on sustainable infrastructure and innovative building materials. His research includes developing machine learning models for enhancing organic photocatalysts to improve indoor air quality and exploring bio-sensing wearables. With a B.Tech in Civil Engineering from Ladoke Akintola University of Technology (First Class, second best in his class), Samuel has significant professional experience in construction management and structural engineering. He has contributed to various high-profile projects, including multi-story building constructions and research on concrete strength improvement.
Profile
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Education š
Mr. Samuel Ojo is pursuing a Ph.D. in Civil Engineering at Case Western Reserve University, where he has demonstrated exceptional academic performance and research capabilities. He previously earned a B.Tech in Civil Engineering from Ladoke Akintola University of Technology, graduating as the second-best student in his class of 120, with a GPA of 4.54/5.00 (First Class).
Experience š¼
Mr. Ojo has extensive field experience in civil engineering, particularly in supervising large-scale construction projects. At FBS Construction Engineering Services, he played a vital role in constructing an eight-story hotel, managing concrete batching, structural interpretations, and reinforcement supervision. His previous roles further highlight his hands-on expertise in structural engineering and project management.
Research Interests š
His research focuses on advancing sustainable infrastructure through innovative materials and methodologies. He applies machine learning models to enhance organic photocatalysts for air quality improvements and is actively exploring bio-sensing wearables. His interdisciplinary approach reflects a deep understanding of both traditional civil engineering principles and modern data-driven techniques.
Publications Top Notes š
Title: Optimizing Photodegradation Rate Prediction of Organic Contaminants: Models with Fine-Tuned Hyperparameters and SHAP Feature Analysis for Informed Decision Making
- Authors: R.T. Schossler, S. Ojo, X.B. Yu
- Journal: ACS ES&T Water
- Year: 2023
- Volume: 4
- Issue: 3
- Pages: 1131-1145
- Citations: 3
Title: A Novel Interpretable Machine Learning Model Approach for the Prediction of TiO2 Photocatalytic Degradation of Air Contaminants
- Authors: R.T. Schossler, S. Ojo, Z. Jiang, J. Hu, X. Yu
- Journal: Scientific Reports
- Year: 2024
- Volume: 14
- Issue: 1
- Article ID: 13070
- Citations: 1
Title: Ensembled Machine Learning Models for TiO2 Photocatalytic Degradation of Air Contaminants
- Authors: R.T. Schossler, S. Ojo, Z. Jiang, J. Hu, X. Yu
- Platform: Available at SSRN
- Year: 2023
- Article ID: 4435749
- Citations: 1
Title: Innovative Antifungal Photocatalytic Paint for Improving Indoor Environment
- Authors: S. Ojo, Y.H. Tsai, A.C.S. Samia, X. Yu
- Journal: Catalysts
- Year: 2024
- Volume: 14
- Issue: 11
- Article ID: 783
Conclusion
Mr. Samuel Ojoās outstanding academic record, innovative research contributions, and leadership activities position him as a deserving candidate for the Research for Best Researcher Award. His interdisciplinary approach to solving critical environmental and infrastructural challenges exemplifies the qualities of a leading researcher.