Postgraduate Courses

This course covers emerging topics of big data analytics in cancer research. It will introduce the genetic basis of human cancer including mechanisms of mutations, mutation burden in cancer immunotherapy, integration of omics data, statistical method in differential gene expression, gene set enrichment analysis and so on. This course will also focus on the principles and applications of modern cancer therapeutic approaches and cutting-edge research in immunology. In addition, this course will discuss current practice of precision medicine and future developments.

This course covers the basic knowledge of nervous system, and the clinical manifestation, anatomy, physiology, pathophysiology, diagnosis, and treatment of common nervous system diseases, with focus on the underlying molecular mechanism of the diseases and treatments.

This course covers the basic knowledge of neuroscience, including neuronal communications in the brain, the fundamental information and electrophysiological properties of neurons, neuroanatomy, psychopharmacology, and how the activity of neurons and neural circuits, together with chemical systems in the brains, yield emotion, motivated behaviors, learning and memory.

The goal of this course is to provide a fundamental introduction to light microscopy and electronic imaging. In the past 20 years, much has happened to light microscopy into the forefront of biomedical research methodologies. Topics to be covered include principles and practice of optical imaging, light and color, the point spread function and resolution, phase contrast microscopy and darkfield microscopy, differential interference contrast microscopy, fluorescence microscopy, confocal laser scanning microscopy, two-photon excitation fluorescence microscopy, super resolution imaging, imaging living cells with the microscope, digital imaging introduction and noise, digital image processing and other related topics.

This course covers the basic knowledge of biomaterials, drug delivery and tissue engineering: natural biomaterials, synthetic biomaterials, biomaterials for drug delivery, biomaterials for tissue engineering, biomaterial formulations (nanoparticles, nanofibers and microspheres), biocompatibility, biodegradability, with focus on the structure-function of the biomaterials and how to develop biomaterials for a variety of biomedical applications.

This course covers the principles, implementation and applications of signal processing methods in biological research and biomedical engineering. Through a series of case studies and projects, the course will provide opportunities to acquire practical knowledge and skills of data analysis. General concepts of signals and systems, and a brief tutorial of MATLAB will be introduced at the beginning. Then data acquisition, filtering, coding, feature extraction, statistical inference, pattern classification, and modeling will be covered with emphasis on applications in real-world data. This course also includes a topic about neural signal processing.

Selected topics in bioscience of current interest in emerging areas and not covered by existing courses. May be repeated for credit if different topics are covered.

An independent research project carried out under the supervision of a faculty member on a Bioscience and Biomedical Engineering topic.

Seminar topics presented by students, faculty and guest speakers. Students are expected to attend regularly and demonstrate proficiency in presentation in accordance with the program requirements. Graded P or F.

Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.