For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ECH5121 | Nanomedicine | 3 | 6 | Major | Master/Doctor | 1-4 | Chemical Engineering | - | No |
| Nanomedicine, an offshoot of nanotechnology, is emerging as the core technology to surmount the limitations of the conventional therapeutic and diagnostic agents. For therapeutic nanomedicine, the lecture will cover the approaches to maximize the therapeutic efficacy and to minimize the side effects. For diagnostic nanomedicine, the imaging agents to detect the intractable diseases using various imaging modalities (MRI, CT, US, Optical Imaging) will be introduced. In addition, the theranostic nanoparticles for simultaneous therapy and diagnosis will be discussed. | |||||||||
| GBE4001 | Electromagnetism in BiomedicineⅡ | 3 | 6 | Major | Bachelor/Master | - | No | ||
| This course discusses the key concepts and problem solution methods in classical electromagnetism related to (1) electricity in matters, (2) magnetism in matters, and (3) electromagnetic wave propagation in matters. These subjects are relevant for many applications and experiments in biomedical engineering research. For example, dielectric constants and magnetic susceptibility of materials will be discussed in detail. The students are expected to have basic background knowledge in electricity and magnetism in vacuum, including the Maxwell’s equations. | |||||||||
| GBE4002 | BME Thermal physics | 3 | 6 | Major | Bachelor/Master | - | No | ||
| This course discusses the key concepts and problem solution methods in classical thermodynamics and statistical mechanics, with emphasis on applications in biomedical engineering. The contents will include: Definition of temperature and entropy, heat capacity, thermodynamic laws, Boltzmann distribution, and Diffusion. Solid and principled physical understanding will be emphasized, and implications of the thermodynamic concepts on medical imaging (MRI) will be discussed. | |||||||||
| GBE4003 | BME Advanced Medical Imaging | 3 | 6 | Major | Bachelor/Master | English | Yes | ||
| his class offers basic introduction of magnetic resonance imaging (MRI). As MRI do not involve any injection of special dye or radio-active isotope, it can easily apply into many clinical and research environment. This class discuss what is MRI with emphasis on physical principles. This classes also introduce real examples of MRI application in clinical and research settings. | |||||||||
| GBE4004 | biomedical imaging and reconstruction | 3 | 6 | Major | Bachelor/Master | - | No | ||
| This course is to provide undergraduate and graduate students a chance to learn fundamental principles of medical imaging particularly focusing on tomographic imaging system (CT, MRI, PET). To this end, we deal with imaging physics for signal generation and acquisition, mathematical models and optimization for signal encoding and image reconstruction, and metrics for validation. | |||||||||
| GBE4005 | Principles of Human Brain Mapping | 3 | 6 | Major | Bachelor/Master | English | Yes | ||
| Thiscoursewillfocusonhowfunctionalmagneticresonanceimaging(fMRI)isusedtounderstandhumanbrainfunction.WewillfirstexaminewhatfMRIis,howthemachineworks,andhowdataisgeneratedandprocessed.Next,wewilldiscusshowfMRItechnologycanbeusedtogainunderstandingofhowthehumanbrainoperates,bycoveringtopicsofexperimentaldesign,analysis,andproblemsinherenttobrainimagingresearch.Asaclass,wewillcollectadatasetoffMRIscans,andstudentswillhaveanopportunitytobeanexperimentalsubject.Followingdatacollection,wewillconductlabsessionswherestudentswilllearntoanalyzefMRIdata,runstatisticaltests,andwriteupexperimentalresults. | |||||||||
| GBE4006 | Fundamentals in Network Neuroscience | 3 | 6 | Major | Bachelor/Master | - | No | ||
| This class aims at educating senior bechalor and master/master-PhD course students about basics of large-scale network neuroscience, a recently emerging research field to study functional dynamics and underlying physical substrates of brain circuits and networks. The weekly lecture will teach history and motivation of this research field as well as practical analytics and essential mathematical principles for graph theory. This class is a prerequisite for a graduate course “Brain Network Modeling”. The class will be offered in both online and offline format, although I highly recommend students to participate in the offline class for active discussion and efficient learning. | |||||||||
| GBE4007 | Neurobiology of Decision Making | 3 | 6 | Major | Bachelor/Master | - | No | ||
| This course introduces seminal studies that investigated how the brain makes decisions using experimental and computational methods. The course consists of three parts: 1) Perceptual decision making using detection or discrimination tasks 2) Value-based decision making 3) Social decision making. It explores neural circuits and computations underlying decision making. We will also learn about disorders that result from a malfunction of the circuits. | |||||||||
| GBE4008 | Human-level Artificial IntelligenceⅠ | 3 | 6 | Major | Bachelor/Master | - | No | ||
| Artificial Intelligence (AI) has witnessed the unprecedented advance for the last decade. However, it still falls short of human-level intelligence in many aspects. For example, generalizing learned knowledge to novel situations, learning from a small number of data points, understanding other agents’ decisions and values, etc. With the current AI, we, as human beings, might not be able to enjoy our future with AI. To overcome these limitations, we need a deep understanding of the human brain and Intelligence, and further, we need to consider how all living organisms have survived with their adaptive behaviors. Thus, in this class, we will envision the future of AI by examining the intelligence of humans and living organisms. This class is recommended to students who are dreaming of future AI inspired by the human brain. | |||||||||
| GBE4009 | Human-level Artificial IntelligenceⅡ | 3 | 6 | Major | Bachelor/Master | - | No | ||
| Artificial Intelligence (AI) has witnessed the unprecedented advance for the last decade. However, it still falls short of human-level intelligence in many aspects. For example, generalizing learned knowledge to novel situations, learning from a small number of data points, understanding other agents’ decisions and values, etc. With the current AI, we, as human beings, might not be able to enjoy our future with AI. To overcome these limitations, we need a deep understanding of the human brain and Intelligence, and further, we need to consider how all living organisms have survived with their adaptive behaviors. Thus, in this class, we will envision the future of AI by examining the intelligence of humans and living organisms. This class is recommended to students who are dreaming of future AI inspired by the human brain. | |||||||||
| GBE4010 | Drug Delivery Systems | 3 | 6 | Major | Bachelor/Master | Korean | Yes | ||
| Drug delivery systems (DDS) is a system to deliver therapeutic or diagnostic agents to specific areas of action in the body in a spatiotemporal manner to effectively treat diseases without serious side effects. In this class, students will learn the fundamental concepts and principles of drug delivery technologies, and discuss current DDS applications and potentials future technologies. | |||||||||
| GBE4011 | Introduction to Human-level Artificial intelligence | 3 | 6 | Major | Bachelor/Master | 1-4 | - | No | |
| This class will provide a synoptic level of introduction i) for the beneficial relationship between neuroscience and artificial intelligence, the two fields on which both industry and academia recently show great interests to study, and ii) for how they have been inspiring each other to pioneer their own research domain. Above all, the class will offer the students a bird-eye view for knowledge of biological and computational science through several representative examples, in order to implement human-level intelligence. | |||||||||
| GBE4013 | Deep learning with Python and Brain | 3 | 6 | Major | Bachelor/Master | 1-4 | - | No | |
| In this course, students will learn the principles of deep learning. They will implement basic and popular neural network architectures and learn the similarity and differences between biological neural networks and artificial neural networks. Every week, they will do homework related to network training and interpret the training results. Students will have chances to experience how artificial intelligence and brain science interact with each other. | |||||||||
| GBE4014 | Special topics in computational neuroscience | 3 | 6 | Major | Bachelor/Master | 1-4 | English | Yes | |
| This class aims to discuss particular topics from Fundamentals of Computational Neuroscience that are widely used in academia and industry. In addition, some topics that are not discussed in Fundamentals of Computational Neuroscience (e.g., information theory) will be discussed. For the last couple of classes, the important papers applying those theories will be discussed. | |||||||||
| GBE4015 | Advanced Biotechnology | 3 | 6 | Major | Bachelor/Master | 1-4 | - | No | |
| Biotechnology and biomedical engineering technologies have been advancing rapidly and are driving the future development of various engineering fields. These technologies are actively applied in the fields of biomaterials, biosensors, drug delivery technologies and others.. In this class, we will introduce fundamentals of biotechnology and biomedical technologies and discuss the state-of-the-art future technologies. | |||||||||