Postgraduate Courses

This course will introduce the basic concept in data science, as well as the application of several data science technologies in the study of biomedical problems. It will include principles in network biology, machine learning and statistical analysis, and basic methods for sequencing data processing. Students will be evaluated based on group projects and student presentation.

The course covers fundamentals of material science and its interface with medicine. The design principles of biomaterials will be described for different applications, ranging from implants to drug carriers and tissue regeneration. Latest technologies to advance biomaterials design and fabrication will be taught. Considerations in regulatory approval process, manufacturing, and commercialization will be discussed.

This course will introduce the fundamentals in the molecular and cellular bases of life from an engineering perspective. Topics include analysis and engineering of biomolecular structure and dynamics, enzyme function, molecular interactions, metabolic pathways, signal transduction, synthetic biology, and cellular mechanics, etc. The lectures will be complemented by tutorials in protein engineering, AAV preparation, bioinformatics and machine learning, and structure visualization.

The course will introduce the basic concepts in detection of biosignals. Students will learn about the biosensing components, transduction mechanisms, novel materials in sensing, and analytical techniques to capture diverse health information. The course will also touch upon the physical and chemical sensors used for healthcare applications. We will also discuss about the emerging flexible, wearable technology toward personalized healthcare. Students will have opportunities to design and propose biosensors and bioelectronics at the end of the course.

This course introduces various bioimaging methods and their data analysis approaches, including optical coherence tomography, fluorescence microscopy, nonlinear microscopy, super-resolution microscopy, ptychographic microscopy, and photoacoustic tomography. Students will be able to understand, design, and evaluate those imaging techniques. This course requires basic knowledge of linear algebra, calculus, and geometry. Familiarity with a programming language such as MATLAB is needed.

Synthetic biology, which echoes the assertion “What I cannot create, I do not understand” made by Richard Feynman, has been hailed as a revolution for modern science and technology. Unfledged as it is, the field, arising from the synergistic combination of chemistry, biology, engineering, data science, and information/control theory, has already shown great promise in addressing energy and health-related global challenges. This course aims to expose students with diverse backgrounds to this highly interdisciplinary field and furnish them with necessary guidance and ideas for taking on synthetic biology projects.

This course will introduce the theory and practice of omics technologies for the high-throughput characterization of complex mixtures of biomolecules, including DNAs, RNAs, proteins and metabolites. Applications of omics approaches in life science research and clinical practice will also be covered.

This course aims to equip students with broad knowledge in pharmaceutical engineering. The topics span from early drug discovery to late commercial manufacturing. Theory and practice of the chemical synthesis and the manufacture of active pharmaceutical ingredients (APIs), solid-state characterization of APIs, and formulation of various pharmaceutical dosage forms are covered. The course also introduces students to some of the main challenges in current pharmaceutical research and development related to selected topics such as continuous manufacturing and advanced process analytical technologies.

This course introduces fundamentals of protein science and common approaches for protein engineering. It will provide students with the basic knowledge of protein structure and function. The protein engineering part will cover two important approaches—computational design and directed evolution—toward protein engineering. The course will involve several case studies to illustrate the use of modern computational tools for designing protein molecules including catalysts, biosensors, biomaterials, etc. The course will also cover the topics concerning directed evolution, including theoretical basis of of biomolecular evolution, concept of fitness landscapes, important applications.

The course will cover the advanced concepts of bioprocessing, including fermentation, bioreactor design, and product recovery. It will explore the production of bioenergy from renewable sources, such as biomass, biogas, and biofuels. Students will gain an understanding of the technical, economic, and environmental aspects of bioproducts and bioenergy production.

Nowadays medical imaging is rapidly growing and plays an indispensable role in healthcare. Recent advances of deep learning techniques have made significant breakthroughs in medical imaging applications. This course will cover fundamental knowledge of medical imaging and various medical image analysis tasks, including computer-aided detection, segmentation, diagnosis and prognosis. State-of-the-art deep learning methods for solving these tasks will be introduced and discussed. This course will equip students with practical knowledge of medical imaging and analysis with deep learning techniques.

This course offers weekly seminars from various industrial and academic speakers to broaden students’ understanding of industrially relevant topics in the biotechnology industry and its drivers for innovation. Students will engage with regulatory cases and will be briefed on ethical subjects in biotechnology industry. Furthermore, industrial and academic speakers will discuss important innovative trends in biochemical engineering and health informatics. Overall, this course aims to better prepare students for a career in biotechnology industry.

Special topics in biomolecular engineering and health informatics. May be repeated for credit if different topics are covered.

An independent research project carried out under the supervision of a faculty member. May be graded PP.

A design project on a topic in the realm of biomolecular engineering and/or health informatics carried out as a team effort under the supervision of a faculty member. It focuses on design thinking and getting hands-on experience with the engineering design process in a broad sense, and aims to accommodate students with more industry-oriented interests. May be graded PP.