
معرفی
Enrique Barbieri is a Professor in the Department of Engineering Technology at the University of Houston's College of Technology. He holds a Ph.D. in Electrical Engineering from The Ohio State University. His academic career includes roles as department chair and associate dean, with leadership in research and education across Tulane University, the University of Houston, and the University of North Texas. Barbieri specializes in Systems and Control, focusing on applications in robotics, manufacturing, and biomedical systems. His work spans over 90 publications and $4M in grant funding, emphasizing interdisciplinary research and translational education.
Education:
- Ph.D. in Electrical Engineering (Control Systems), The Ohio State University, 1988
- M.S. in Electrical Engineering (Digital Systems), The Ohio State University, 1984
- B.S. in Electrical Engineering (Computer Option), The Ohio State University, 1981
Research Interests: Systems Control Technology, robotics, industrial processes, infectious disease modeling, and educational innovation. His projects include defibrillation waveform optimization, rocket propulsion control, and automation of manufacturing processes like heat shrink tubing systems.
Grants & Awards: Over $4M in research funding, including NSF, NASA, and industry partnerships. Notable awards include the 1995-96 Tulane Teaching Excellence Award and a U.S. patent for an ultrasonic ranging system. He has led major initiatives like the Invenciones de Nuestra Inventiva program to enhance Hispanic STEM awareness.
Service & Leadership: Served on ASEE committees, chaired the Texas Manufacturing Assistance Center Executive Council, and contributed to academic program development at multiple universities. His service includes external reviews for engineering programs and editorial roles in control systems conferences.
Labs & Teams: Directed the Center for Technology Literacy (2006–2010) and collaborated with industry partners like SBC/ATT for technology labs. His current research integrates SIR models for disease control and applied data science for industry needs.

