Associate Professor, Mechanical Engineering
Biography
Fatemeh Davoudi is an Associate Professor in the Department of Mechanical engineering. Her research is in the area of human-centered smart manufacturing, with a focus on human factors, physical and neuro ergonomics, and machine learning applications for safety analytics and AI-based quality engineering. Her work integrates wearable sensors, brain–computer interface systems, and collaborative robots to advance occupational risk assessment and quality engineering in manufacturing settings. Fatemeh holds a Ph.D. in Industrial Technology & Manufacturing Systems with a Ph.D. Minor in Statistics from Iowa State University. Prior to joining Santa Clara University in 2023, she was an Assistant and then Associate Professor of Manufacturing Systems at San José State University (2018–2023). She is the director of the Machine Learning & Safety Analytics Lab, where she leads interdisciplinary projects at the intersection of human factors, ergonomics, and manufacturing. Her academic background is in mathematics, applied engineering, industrial technology and manufacturing systems, and statistics.
Education
- Ph.D., Industrial Technology & Manufacturing Systems, Iowa State University 2018
- Ph.D. Minor in Statistics, Iowa State University 2018
Research & Scholarly Activities
Machine Learning & Safety Analytics (MLSA) Lab
The MLSA Lab advances research in Human-Centered Smart Manufacturing (HSM), applying machine learning and predictive analytics to design safer, smarter, and more resilient industrial systems. Our work bridges human well-being with manufacturing performance, creating systems that are efficient, sustainable, and worker-centered. Our research is driven by two core pillars of HSM:
- Human Factors and Occupational Ergonomics: advancing physical and neuro-ergonomics, and occupational risk assessment using assistive technologies, wearable sensors, and collaborative robots to create safer workplaces.
- Quality Engineering: developing AI-driven inspection methods and zero-defect manufacturing strategies to ensure reliability, precision, and long-term system resilience.
The MLSA Lab also provides mentoring and research opportunities for undergraduate and graduate students. Students interested in projects, theses, or gaining hands-on experience in machine learning, deep learning, and HSM concepts are encouraged to contact mlsa-lab@scu.edu or fdavoudikakhki@scu.edu.
Publications & Presentations
A complete list of publications is available at: Google Scholar
Davoudi's Personal Website: MLSA Lab
