Recognition highlights groundbreaking research in advanced nanomaterials and carbon nanotube manufacturing

Zhiyong (Richard) Liang, the Sprint Eminent Scholar Chair Professor in the FAMU-FSU College of Engineering and associate dean for research, has been selected for induction into the Academy of Science, Engineering and Medicine of Florida, recognizing his extraordinary contributions to advanced materials research and engineering innovation. He was one of six faculty inductees from Florida State University (FSU).

The aviation industry is experiencing unprecedented growth in data-driven decision-making.

According to The Business Research Company, the aviation analytics market is predicted to increase from $3.37 billion in 2024 to $3.82 billion in 2025, reflecting a compound annual growth rate (CAGR) of 13.3%.

This groundbreaking alliance exemplifies how HBCU engineering partnerships are shaping the future of scientific discovery and talent development

In a remarkable demonstration of how strategic university-industry partnerships drive innovation, the Florida A&M University (FAMU) and Florida State University (FSU) College of Engineering has established a comprehensive collaboration with Sandia National Laboratories.

Yanshuo Sun, Department of Industrial and Manufacturing Engineering, FAMU-FSU College of Engineering, leads a group of four distinguished associate professors recognized with Florida State University's prestigious Developing Scholar Award this year.

Sun employs sophisticated mathematical modeling and optimization methodologies to enhance transportation systems planning, operations and management across multiple domains including

FAMU-FSU College of Engineering researchers are leveraging artificial intelligence to develop advanced defect detection tools for powder-based 3D printing, supported by a $2.2 million Air Force grant.

The team, led by Associate Professor Hui Wang, is collaborating with Pennsylvania State University and HP Inc. to implement combinatorial generalization (CG) techniques that significantly improve defect prediction models in additive manufacturing processes.