Rate My Professor Isabel Song

Song's academic research centers on Mobile Computing, Software Engineering, Entrepreneurship, On-device AI, and Machine Learning. She emphasizes student mentorship, co-authoring several peer-reviewed publications with SFSU undergraduates and collaborators. Notable works include: S. Mostafa et al., “A framework for predicting progression in primary progressive multiple sclerosis using machine learning and statistical methods,” IEEE International Conference on Bioinformatics and Biomedicine (BIBM), December 2021; T. Touati et al., “What makes a person obese?: An individual-level analysis of obesity,” IEEE International Conference on Biomedical and Health Informatics (BHI), July 2021; Ja-Hee Kim et al., “An Empirical Analysis of the Benefits of Agile Process,” Journal of Information Technology Services, March 2021; U. Khyoi Nu et al., “Who is physically active?: Classification and analysis of Physical activity using NHANES data,” IEEE Symposium Series on Computational Intelligence (SSCI), December 2020; and I. Song et al., “The self-upgrading mobile application for the automatic malaria detection,” 10th Annual Computing and Communication Workshop and Conference, 2020. Additionally, Song contributes to interdisciplinary efforts through the Lam-Larsen Initiatives and Centers in the Lam Family College of Business at SFSU, leveraging her industry background to guide students in agile practices for startups in digital health, mobile technologies, IoT, wireless systems, and big data analytics.