The Historically Black Colleges and Universities and Other Minority Institutions (HBCU-OMI) Program—sponsored by the Office of Fossil Energy and Carbon Management (FECM) and administered by the National Energy Technology Laboratory (NETL)—invests in developing a U.S. workforce that is diverse and highly skilled in science, technology, engineering, and math (STEM) through training and education grants. The HBCU-OMI Program is transformative for student researchers who will shape the future of the clean energy sector.

From February through June 2022, FECM highlighted students conducting research in STEM fields through the HBCU-OMI Program. Learn more about one of these students below! 


Steve Yang

Steve Yang
Graduate Student in Materials Science & Engineering
University of California, Riverside

What made you start pursuing studies in a STEM field and your particular area of specialization?
I was interested in wide applications and the effectiveness of machine learning in various STEM fields.

What research topics and/or technical areas have you worked on through this HBCU-OMI Program?
Computational Fluid Dynamics1 and Machine Learning2

What aspect of your work through this program are you most proud of?
I am most proud of my results in accurately predicting erosion using my new machine learning approaches.

Have you published, or are you working on publishing any papers through your work in this program?
Yes, I currently have a paper undergoing revision based on my work in this program.

How has your research through the HBCU-OMI Program helped you to learn more about your field and career opportunities?
It has helped me in Python coding, various machine learning models and applications, understanding the basics of computational fluid dynamics, and 3D modeling.


To learn more about the HBCU-OMI Program, read our introductory blog post for this student spotlight series, download our infographic, and visit NETL’s University Training and Research page.  


1 Computational fluid dynamics is a branch of fluid mechanics that uses computer-based numerical analysis and algorithms to simulate, analyze, and solve problems in fluid flow.
2 Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.