Computer Science

This research internship program offers selected students the opportunity to participate in research projects or work as an intern in research centers or organizations at Yonsei University. Students are expected to participate in research projects for approximately 20 hours per week throughout the program. Projects will vary depending on placement. Graded Pass/No pass only.

This course introduces the field of parallel computing with hands-on parallel programming experience on real parallel machines. The course consists of four parts: parallel computation models and parallelism, parallel architectures, parallel algorithm design and programming, and new parallel computing models. Topics include: theory of parallelism and models; shared-memory architectures; distributed-memory architectures; data parallel architectures; interconnection networks, topologies and basic of communication operations; principles of parallel algorithm design; performance and scalability of parallel programs, overview of new parallel computing models such as grid, cloud, and GPGPU. The course requires students to take prerequisites.

Internet and Internet-related protocols have evolved to constitute the common network structure for all data and telephone communication. This course gives an overview of some of these protocols and takes a deep look into a selection. The course provides students with thorough knowledge, both theoretical and practical, about the constructions of the most central internet protocols. In particular, a deeper technical understanding is provided for (among others) the following: transport protocols, routing principles, and application examples.

This course studies the principles and practices of secure programming. Secure programming means writing programs in a safe fashion, to avoid vulnerabilities that can be exploited by attackers. It also means using security features provided by libraries, such as authentication and encryption, appropriately and effectively. A range of programming platforms is considered, ranging from low-level (e.g. Android OS), through web programming (e.g., JavaScript and Python) to high-level large-scale languages (e.g., Java). New and emerging language-based security mechanisms are examined, including ways of specifying and enforcing security policies statically and dynamically (e.g., to enforce access controls or information flow policies).

This course provides the basic tools and knowledge needed to design optical systems. At the end of the course, students will be able to take system requirements, select possible components and approaches, create candidate designs, and analyze and optimize their performance. Students learn and utilize standard optical design tools, particularly ray-tracing, as well as learning how to create custom system models with wave, polarization, or Gaussian-beam optical modeling. The course objectives include basic design techniques for ray optics; wave optics in isotopic media; design concepts for optical instruments (microscope, telescope, camera lenses); aberration in optical system (real world problems); how to select optical components (lenses, fibers, optical source and detectors); and optical CAD tools discussion (ZEMAX education version).

This course examines the importance of GIS to building smart cities and the ways in which the technology can be integrated with other ICT in order to support different aspects in urban development. It covers an introduction to smart city and its components; geospatial open data and common spatial data infrastructure; enabling technologies for smart city; delivering smart cities through a geospatial strategy; GIS basics; working with ArcGIS online; using web GIS and geospatial cloud in smart city applications delivery; using 3D GIS in smart city planning and development; using mobile GIS in smart city data collection and public engagement; handling real-time geospatial data for smart city parameters monitoring; applying spatial analytics to solve spatial problems and predictive analysis in smart city planning.

This course provides an overview of modern vision techniques used in man and machine. It focuses on both conceptual understanding of the models and methods as well as practical experience. The course covers state-of-the-art methods for image analysis including how to solve visual processing tasks such as object recognition and content-based image search and retrieval. It provides the necessary mathematical background to understand vision and image processing methods through programming exercises, which include converting a theoretical algorithmic description into a concrete program implementation, comparing computer vision and image analysis algorithms, and assessing their ability to solve a specific task. The course involves a mix of lectures and exercises.