د.عبدالإله الشهري (Dr. Abdulelah S. Alshehri)

Dr. Abdulelah S. Alshehri is an Assistant Professor of Chemical Engineering at King Saud University, where he leads the College of Engineering's AI for Engineering Initiatives and establishing collaborations with National University of Singapore, University College London, Tsinghua University, and Zhejiang University. Dr. Alshehri’s research fuses the physical sciences with the power of generative and agentic AI, large language models, and multi-modal foundation models. Through this interdisciplinary approach, he addresses complex challenges in molecular design, energy systems, and sustainability, ensuring innovations translate into feasible, real-world performance. He has been recognized with several awards and research features, he has been invited as an invited speaker at several prestigious universities and conferences. He served as a Guest Editor for the Journal of King Saud University – Engineering Sciences, and frequent reviewer for high-impact journals including Science Advances, Digital Discovery and the Chemical Engineering Journal.

Education

• Ph.D. in Chemical Engineering (Minor in Computer Science) – Cornell University, 2024
• M.S. in Chemical Engineering – Carnegie Mellon University, 2017
• B.Ch.E. in Chemical Engineering – Cleveland State University, 2016

Highlights & Credentials

• Global Engagement: Establishing collaborations with premier global institutions including University College London (UCL), National University of Singapore (NUS), Tsinghua University, and Zhejiang University aligning with the Saudi Vision 2030 - National Transformation Program. Keynote Speaker on LLMs at ChemIndix (2024) and the GenAI for Materials Discovery Hackathon (2025).
• Industry & Academic Experience: Led the AI4Science subgroup at Cornell University (2019–2024) and R&D Machine Learning Modeling Engineer at Corning Incorporated (2023), developing novel multimodal LLMs for defect detection.
• Awards, Honors & Recognitions:
• (2026) Best Paper Award (Energy Systems Track) – IEEE International Conference on Sustainable Engineering and Digital Innovation (ICSEDI'26)
• (2026) Best Presentation Award – 10th International Conference on Control Engineering and Artificial Intelligence
• (2025) Invited Speaker – University College London, National University of Singapore, Tsinghua University, and Zhejiang University
• (2025) Keynote Speaker – Large Language Models at the GenAI for Materials Discovery Hackathon
• (2025) Front Cover Research Article – American Chemical Society Industrial & Engineering Chemistry Research 
• (2024) Keynote Speaker – Large Language Models at ChemIndix 2024 (Watch YouTube Recording)
• (2024) Front Cover Research Article – American Chemical Society Journal of Chemical Information & Modeling
• (2023) Top Downloaded and Cited Paper – American Institute of Chemical Engineers (AIChE) Journal
• (2022) Top Cited Paper – Computers & Chemical Engineering Journal
• (2022) Instructional Support Award in Deep Learning – Cornell University
• (2021) Instructional Support Award in Machine Learning – Cornell University
• (2014) Sophomore Merit Scholarship – Awarded by Cleveland State University

Selected Publications

• Ashraf, W. M., Keshavarzzadeh, A. H., Alshehri, A. S., et al. "Domain-informed operation excellence of gas turbine system with machine learning." Energy Conversion and Management, 348, 120789 (2026).
• Alshehri, A. S., Bergman, M., Hall, C. K., & You, F. "Biophysics-Guided Uncertainty-Aware Deep Learning Uncovers High-Affinity Plastic-Binding Peptides." Digital Discovery, 4, 561 (2025).
• Alshehri, A. S., Horstmann, K. A., & You, F. "Versatile Deep Learning Pipeline for Transferable Chemical Data Extraction." Journal of Chemical Information and Modeling, 64(15), 5888-5899 (2024).
• Decardi-Nelson, B., Alshehri, A. S., Ajagekar, A., & You, F. "Generative AI and process systems engineering: The next frontier." Computers & Chemical Engineering, 108723 (2024).
• Preuss, N., Alshehri, A. S., & You, F. "Large language models for life cycle assessments: Opportunities, challenges, and risks." Journal of Cleaner Production, 142824 (2024).
• Alshehri, A. S., Gani, R., & You, F. "Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions." Computers & Chemical Engineering, 141, 107005 (2020).

News & Media Features

• Arab News: Leading the AI for Engineering Initiative at KSU
• Arab News: Feature on AI capabilities in Saudi Arabia
• Middle East AI News: Researchers use deep learning for peptides
• Cornell Chronicle: Website charts COVID-19 spread across NY State