As semiconductor companies expand in Greater Sacramento, UC Davis is addressing the industry's research and workforce needs. In Commercial Property Executive, electrical and computer engineering professor Saif Islam discusses the need for skilled workers and how the Center for Nano-MicroManufacturing supports innovation with specialized equipment.
UC Davis engineers are developing noninvasive fetal monitoring technology that uses near-infrared light and machine learning to estimate fetal blood pH, with the goal of giving clinicians better information in the delivery room.
With three decades of experience in wireless communications, new adjunct professor Jin Yang discusses how she aims to bridge industry and academia while preparing students to lead the next generation of wireless innovation in this Q&A.
Inspired by the farming communities where he grew up, electrical and computer engineering undergraduate Luis Blas developed an AI-powered platform that assesses crop quality in real time.
Electrical engineering major and men’s water polo captain Tim Kerr finds energy in pursuing both of his passions, bringing curiosity, perseverance and a sense of fun to the lab, the pool and whatever comes next.
What happens when a Davis-based chemical engineering student trades nanoscale research for manufacturing facilities in Iowa? Roanna Pham spent her summer learning predictive maintenance, building a machine-learning tool and embracing the unexpected.
Nathan Gonzalez, a student studying scalable quantum technologies in the College of Engineering, has received a UC Noyce Graduate Student Fellowship for outstanding doctoral research. The program supports doctoral students at the forefront of computational health, quantum information science and cybersecurity.
In a new study published in Nature Communications, UC Davis researchers detail an infrared sensor that excels above freezing temperatures. The sensor combines a membrane-based design and radio frequencies to increase performance, detecting signals as low as 740 picowatts in room temperature environments.
Research led by a UC Davis Ph.D. student shows that AI models can maintain response quality while using far less energy on existing hardware. The work received the Best Paper Award at MLSys 2026, a leading machine learning and AI systems conference.
