Department of Biomedical Sciences
Professor
Education
- Ph.D., Electrical Engineering, Clemson University, 1996
- M.S., Electrical Engineering, Clemson University, 1990
- B.S., Electrical Engineering, University of Cincinnati, 1988
Teaching
Biomedical instrumentation introduces the principles and applications of biomedical instrumentation used for patient monitoring, diagnosis, and medical imaging. Topics include biomedical sensors and transducers, physiological measurements (ECG, heart rate, temperature, blood pressure, and oxygen saturation), analog and digital signal processing, and methods for extracting meaningful information from noisy biological signals. Students examine the physical principles and applications of major imaging technologies, including X-ray, computed tomography, ultrasound, magnetic resonance imaging, nuclear medicine, and image reconstruction.
The course also introduces artificial intelligence and machine learning for biomedical data analysis, clinical laboratory measurements, and the ethical and safety considerations of medical devices, including data privacy, risk, and patient safety.
Clinical Interests
My research integrates intelligent systems, nonlinear control, robotics, and biofabrication to develop predictive models and experimental platforms for complex engineering and biomedical systems. Across applications ranging from robotic systems and unmanned aerial vehicles to semiconductor manufacturing, bacterial cultures, and cancer biology, my work combines mathematical modeling, control theory, sensing, instrumentation, and experimental validation to create systems that can monitor, predict, and optimize dynamic behavior.
A major current focus is the development of biomedical digital twins for breast cancer. Digital twins combine computational models with experimental and clinical data to create patient-specific representations of disease progression. As new data become available, the models are continuously updated, enabling prediction of disease evolution and evaluation of potential treatment strategies. This emerging paradigm has been recognized by the National Academies and the National Cancer Institute as a promising direction for accelerating scientific discovery and advancing precision oncology.
My laboratory contributes to this effort by developing advanced in vitro tissue models that provide biological information unavailable from clinical imaging or animal studies alone. We design and fabricate three-dimensional bone scaffolds and tissue test systems using custom biofabrication technologies to study the interactions between breast cancer and the bone microenvironment. These experimental platforms generate quantitative data that improve computational models of bone metastasis.
A particular emphasis of our work is modeling the dynamic role of bone density during metastatic progression. Existing digital twin approaches have largely overlooked this critical component despite its importance in tumor growth and therapeutic response. By integrating data from engineered tissue models with multiscale computational models, we are developing a bone-density “agent” that can interact with other model components—including tumor growth, cellular behavior, and tissue remodeling—to improve prediction of disease progression and ultimately support personalized patient care.
Publications
