Faculty Mentors & Research Opportunities

Faculty mentors represent expertise across biomedical AI, wearable sensing, bioinformatics, STEM education, data science, health informatics, XR, and biomedical engineering. Teachers can review these opportunity areas to identify the kinds of research experiences that best match their teaching interests, technical background, and curriculum goals.

Faculty Mentor	Department	Research Opportunity Area
Mahdi Pedram	Computer Science and Engineering / Biomedical Engineering	Embedded AI, wearable health monitoring, biosensors, real-time signal processing, digital health systems, and project-based biomedical device design.
Mark V. Albert	Data Science / Computer Science and Engineering	Biomedical AI, wearable sensor analytics, motion analysis, predictive modeling, and machine learning for health outcomes.
Lauren Eutsler	Learning Technologies / TCET	STEM education, instructional technology, AI-integrated learning, curriculum design, and classroom implementation support.
Serdar Bozdag	Computer Science and Engineering / Math	Bioinformatics, computational biology, multi-omics data integration, biomedical data science, and AI for precision medicine.
Gahangir Hossain	Data Science	Biomedical signal processing, AI-driven healthcare technologies, medical diagnostics, image analysis, and secure health systems.
Saraju Mohanty	Computer Science and Engineering	Smart healthcare systems, hardware security, nanoelectronics, cyber-physical systems, edge AI, and wearable health technologies.
Rajeev Azad	Biological Sciences	Bioinformatics, computational biology, genomics, transcriptomics, genome comparison, and quantitative biological data analysis.
Ting Xiao	Data Science / Computer Science and Engineering	Deep learning, large-scale data analysis, wearable/video/audio data, predictive analytics, and data visualization for human-centered applications.
Ajita Rattani	Computer Science and Engineering	Computer vision, image analysis, biometrics, fairness in AI, on-device AI, and healthcare-related visual computing.
Brian Meckes	Biomedical Engineering	Nanotechnology, nanomaterials, therapeutic delivery systems, and biomedical engineering research methods.
Ana Cleveland	Information Science	Health informatics, medical information retrieval, evidence-based information systems, and biomedical knowledge access.
Xuan Guo	Computer Science and Engineering	Algorithms for high-dimensional biological data, metaproteomics, GWAS, metagenomics, fMRI analysis, and AI-enabled precision medicine.
Timothy (Fred) McMahan	Learning Technologies	Neurophysiological signal acquisition, EEG/EMG, brain-computer interfaces, physiological monitoring, and immersive technology for health and learning.

Participants may be matched with research groups working in areas such as biomedical AI, biosignal analytics, wearable health monitoring, embedded and edge computing, medical image analysis, bioinformatics, health informatics, XR-enabled health applications, and STEM curriculum translation. Mentor matching should consider educator background, stated interests, and classroom goals.