Ph.D. candidate Adebiyi earns SCIPE Chishiki AI Fellowship

An attractive research environment was the selling point for the University of Houston for Taiwo Adebiyi, but the doctoral student has excelled in his own ways at the Cullen College of Engineering as well.

His work has been recognized after he was selected for the Strengthening the Cyberinfrastructure Professionals Ecosystem (SCIPE) Chishiki AI in Civil Engineering Graduate Fellowship. The program provides a significant stipend, and only five students were chosen from a national field of applicants.

Adebiyi described the experience of being selected as surreal.

“Winning a national fellowship like this was deeply encouraging, and my most immediate feeling was gratitude,” he said. “I felt grateful for the privilege of doing meaningful research in the Uncertainty Quantification Lab at the University of Houston (UQ@UH), grateful for the people who have believed in me and continued to push me toward competitive opportunities, and grateful to God for the journey. For me, this fellowship felt like a strong validation of work that has taken years to build, but also a reminder that the work is still growing.”

Adebiyi pointed to his advisor, assistant professor Ruda Zhang in the Civil and Environmental Engineering Department, as important in his decision to come to Cullen and in his development as a researcher.

“When I learned about Dr. Zhang’s work, it immediately felt connected to the future of engineering,” he said. “I accepted my Ph.D. offer before ChatGPT was released, but even then, I felt strongly that probabilistic machine learning, uncertainty quantification and optimization would become essential for trustworthy automation in science and engineering. Since then, the rapid growth of AI has only made that research direction feel even more important.”

He also noted that the Houston metro area and the College provided a “strong engineering environment,” thanks to the city’s deep connections to energy, infrastructure and industry.

“That combination of advanced research, practical engineering relevance, and Houston’s energy ecosystem made the University of Houston a very attractive place for my doctoral work,” he said. “The diversity of the university community has also been important to me.”

Adebiyi said his research is primarily dealing with uncertainty, and building mathematical tools to help engineering systems make better decisions when faced with uncertainty.

“In engineering, we often do not have perfect data, perfect models, or unlimited resources,” he said. “So the question becomes, ‘What should we test, simulate, measure, or optimize next to learn the most useful information?’”

He added, “That idea sounds technical, but it applies to many real problems. It can help with energy resource estimation, infrastructure monitoring, natural hazards, digital twins, scientific forecasting and autonomous experimentation. The broader goal of my work is to make AI more trustworthy for high-stakes science and engineering settings, where decisions need to be reliable, not just fast or impressive.”

Adebiyi is quick to credit Zhang for much of his success. He spoke about his research and his work with Zhang in a video with UH Energy earlier this month.

“His mentorship has shaped the rigor, direction, and ambition of my doctoral research,” he said. “My advisor has continually pushed me to compete globally, publish in strong venues, think from first principles, and pursue research that matters.”

Other strong influences on Adebiyi’s work at UH include Bach Do, a postdoctoral fellow that serves as a mentor for the lab, and Akash Yadav, a lab mate. He also wanted to thank his parents, his twin brother Kehinde Adebiyi, and his partner Blessing Ekarume for their continued support and guidance.

Adebiyi’s expected graduation is in Fall 2027. As he finishes earning his doctorate, he remains interested in subjects like trustworthy AI, uncertainty quantification, scientific computing and their applications to a variety of fields.

“My goal is to help advance the development and evaluation of AI models so they can be used more reliably in critical real-world settings,” he said. “I am excited by opportunities in industry research labs, research institutes, national laboratories, and application-focused academic environments, where I can contribute to translational work that connects strong methods with meaningful applications.”