An introduction to tissue engineering and regenerative medicine.

This course covers basic concepts in electromagnetism and light-matter interactions of biomedical significance. Topics covered include: optical properties of biological cells, tissues and biomaterials; visible and near-infrared light absorption, scattering and fluorescence spectroscopy; advanced microscopy techniques, optical coherence tomography, vibrational spectroscopy, photoacoustic imaging, photodynamic therapy and their relevance to human disease diagnostic and therapeutic applications.

This course is offered to junior or senior undergraduate students and graduate students to introduce them to the field of engineered gene therapy. It covers how gene therapy works, the type of vectors used, and why/when certain vectors are employed. The course also includes how to make viral vectors and introduces some non-viral tools for gene therapy.

This course introduces the fundamental physical principles and instrumentation of diagnostic ultrasound, including wave propagation, acoustic interactions, transducer design, imaging methods, beamforming, and Doppler flow measurements. The course examines the advantages and limitations of ultrasound relative to other imaging modalities and highlights both conventional and emerging technologies such as elastography, coded excitation, and acoustic radiation force imaging. Students will gain exposure to clinical applications, high-frequency and intracavity imaging, and safety considerations.

This course covers the principles and technologies of modern drug delivery, focusing on how biomedical engineering helps develop safer and more effective therapies. Students will explore physiological barriers to drug transport, design of carrier systems, controlled release mechanisms, and strategies for clinical translation. The aim is to establish a solid foundation connecting fundamental biology, engineering design, and therapeutic applications.

This course is intended to provide biomedical engineering students with a basic understanding of the foundations of artificial intelligence (AI) and introduce them to the current AI landscape and a spectrum of biomedical applications that can benefit from AI. The course with start with a rigrorous mathematical introduction that will review essential knowledge in linear algebra and statistics and familiarize students with the basics of statistical estimation and numerical optimization.