Graduate

    • This course introduces basic optimization theory and methods, with applications in systems and control. The course will cover constrained and unconstrained optimization, linear programming, convex analysis, various algorithms and search methods for optimization, and their analysis. Examples from various engineering applications are given. Prerequisite of linear algebra and calculus of several variables.
      This course introduces mathematical representations of continuous and digital images, basic coding schemes and formats, picture enhancement, models of image degradation and restoration, segmentation, and pattern recognition.
      Intelligent Systems are studied with particular attentions to CI(Computational Intelligence)-based design techniques and their applications in uncertain/ambiguous environments. Topics includes fuzzy logic, artificial neural networks, evolutionary computation, support vector machine, swarm intelligence, immune systems with their real-life applications for automation system control and data/information processing including gesture and facial expression recognition.
      This course provides basic system theory for various engineering problems; solution of the linear system, equilibrium points and linearization, natural and forced response of state equations, system equivalence and Jordan form, BIBO stability, controllability and duality, control-theoretic concepts.
      This course extends the undergraduate linear algebra and focus on vector spaces, dual vector spaces, eigenvalues and eigen vectors, Positive definiteness, Jordan form, linear transformations (e.g., orthogonal and unitary transformations), matrix decompositions (e.g., QR and singular value decompositions), least square approximation and linear programming.
      This course covers digital transmission of information over the channels using modern communication technologies. The topics include source coding, channel coding, digital modulation, decision theory, fundamental limits in coding and modulation, capacity and throughput analysis, and wireless channel model.
      This course introduces advanced topics in robot control methods such as servo mechanism design, man machine interface, teleoperation, force control, and stereo vision.
      This course is offered to graduate students and introduces the researches in 3D Visual Processing. Topics include 3D data acquisition, 3D modeling, 3D data compression and transmission, 3D image processing, 3D rendering and visualization, and 3D display.
      This course studies concepts of acoustics and electroacoustic modeling for the analysis and design of microphones, loudspeakers, and crossover networks. Methods of analysis and design of audio power amplifiers are also covered.
      This course covers general connection methods of data networks and data communication architectures. The topics are: data link control (e.g., error correction, framing), message delay analysis (e.g., Markov processes, queuing), network delay analysis (e.g., Kleinrock independence, throughput analysis), and multiple access networks (e.g., ALOHA, carrier sensing).
      Based on mathematical foundations, this course concerns advanced control methods such as adaptive control, robust control, predictive control, fuzzy control, etc.
      This course is an introductory course for optimization of stochastic systems via mathematical modeling. The topics may include linear programming (e.g., simplex method, interior point method), convex optimization, dynamic programming (e.g., shortest path algorithm, infinite horizon problems, average cost optimization), and Markov decision process.
      This course covers probability theories such as probability measure, random variable, distribution, expectation, Markov chains, renewal theory and queuing theory, and stochastic processes such as Poisson process, random walks and Brown motion.
      Principles of modern medical imaging systems. For each modality the basic physics is described, leading to a mathematical systems model of the imager. Then, image reconstruction algorithm for each system will be derived. Modalities covered include radiography, x-ray computed tomography (CT), MRI, and ultra-sound.
      This course aims at learning how to extract valuable information from visual scenes using computers. Topics may include the basic theories for capturing images by cameras, human visual perception, filtering, edge detection, segmentation, stereo, motion analysis, feature extraction, and object recognition.
      Electric machine is an essential component in modern electric power applications such as electric vehicles, renewable energy generation, robotics, and industrial electronics. This course introduces the basic background of electric machines and drives, including the electromechanical energy conversion, steady-state and dynamic operations, control of AC and DC machines. As advanced topics, electromagnetic analysis and design of electric machines are also covered.
      This course is an introduction to the theory, design techniques, and applications of analog passive, active, and switched-capacitor filters.
      This course studies analysis and design techniques for the utilization of integrated circuit operational amplifiers for applications in electronic systems.
      This course studies analysis and design of MOS digital integrated circuit families necessary for Very Large Scale Integrated (VLSI) circuits and their applications in modern electronic systems. This course introduces full-custom (or semi-custom) integrated circuit design with help of several EDA tools (e.g., schematic and layout design, parasitic extraction, and DRC/LVS, etc). This course is highly project-oriented.
      The electronic packaging in real-world applications is compromised by artifacts of the analog and digital circuit design, IC package, and printed circuit boards. This course gives engineers the necessary skills in the circuit and electromagnetic designs to ensure signal quality between a driver and a receiver and electromagnetic compatibility.
      The objective of this course is to study and discuss the recent technology of power electronics. Main topics will cover topology of new dc-dc converter, resonant converters, bidirectional converters, and PFCs. In addition, new control scheme for power electronics and hot applications such as smart gird, renewable energy, EV, and DC distribution/transmission will be treated.
      This course is designed for understanding the fundamental theory of antennas used in various wireless applications. The course covers electromagnetic radiation theory, small antennas, array antennas, resonant antennas, broadband antennas, aperture antennas, and antenna synthesis theory. Practical aspects for antenna designs are also considered.
      Data converters are essential circuits to provide data conversions between analog signals and digital signals. Various ADC(Analog-to-Digital Converter) and DAC(Digital-to-Analog Converter) circuits and their recent technology trends are covered.
      A progression from the Analog Integrated Circuits course, this course covers advanced and state-of-the-art design of analog circuits using CMOS and bipolar technology with emphasis on practical implementation and examples.
      Wireless system specifications are translated to architectures and building blocks compatible with silicon technology. The course focuses on the analysis and design of these blocks.
      This course introduces the fundamentals of electric power systems, which covers power generation, transmission, and operation analysis. Topics include three-phase power analysis, transmission line modeling, distributions systems, power flow analysis, and grid stability. The effects of recent developments, such as renewable energy and distributed resources will also be discussed.
      In this course we provide the student with the basic knowledge of electrodynamics, which are necessary to understand the advanced electrodynamics. The electrostatics, magnetostatics, boundary value problems, Maxwell equations, and wave propagations are covered.
      This course introduces popular numerical techniques for simulating electromagnetic fields: the finite difference method, the finite element method and the method of moments. To assess the accuracy of numerical methods, von Neumann stability analysis, convergence analysis and dispersion analysis are used. As applications, we develop numerical codes for simulating scattering and antenna design.
      This course covers from the fundamentals of RF/microwave engineering to applications of RF/microwave devices based on in-depth knowledge of microwave components. The emerging millimeter, submillieter, and THz technology will be also introduced. Basic principles of RF oscillators, amplifiers, and passive components, and circuits will be introduced. Modern usage of RF/microwave/millimeter-wave components will be broadly covered.
      This course intends to provide knowledge for a research in the field of photonics. It covers a few fundamental and advanced topics related to photonics, especially integrated waveguide based photonics. The topics include: electromagnetic waves in anisotropic media, Gaussian beam propagation, resonance, coupled-mode theory, nonlinear optical effect, and optical modulation.
      This course supplies students hands-on experiences on semiconductor device fabrication processes (oxidation, chemical cleaning/etching, lithography, diffusion, metalization) by actually making planar diodes and transistors on a silicon wafer in cleanroom environment. Students also learn about the methodologies of characterizing the fabricated devices.
      The main purpose of this course is to teach the basic knowledges of semiconductor governing equations such as Poisson’s equation and continuity equations, and carrier transport equations on the numerical TCAD (technology computer-aided design) platform. In addition the course teaches discretization methods and how to solve nonlinear algebraic equations.
      This course introduces new research topics in the field of Communication, Control, and Signal Processing I.
      This course introduces new research topics in the field of Communication, Control, and Signal Processing II.
      This course introduces new research topics in the field of Communication, Control, and Signal Processing III.
      This course introduces new research topics in the field of Electrical Engineering IV.
      This course introduces new research topics in the field of Communication, Control, and Signal Processing V.
      This course introduces new research topics in the field of Electronic Design and Applications I.
      This course introduces new research topics in the field of Electronic Design and Applications II.
      This course introduces new research topics in the field of Electronic Design and Applications III.
      This course introduces new research topics in the field of Electronic Design and Applications IV.
      This course introduces new research topics in the field of Electronic Design and Applications V.
      This course introduces new research topics in the field of Device Physics I.
      This course introduces new research topics in the field of Device Physics II.
      This course introduces information theory which is a base for efficient data storage, compression, and transmission in communications. The topics include entropy, channel capacity, source coding theorems, channel coding theorems, and rate-distortion theory.
      This course introduces advanced signal processing methods. Topics include statistical and deterministic least square filters design, adaptive filtering, applications in beam-forming and spectral estimation.
      This course introduces optimal control theory, including calculus of variations, the maximum principle, and dynamic programming for linear-quadratic control, differential games, and H-infinity control synthesis.
      This course introduces estimation and decision theory applied to random processes and signals in noise: Bayesian, maximum likelihood, and least squares estimation; the Kalman filter; maximum likelihood and maximum a posteriori detection, and detection systems with learning features.
      This course introduces pattern recognition systems and their components. Topics include decision theories and classification, discriminant functions, supervised and unsupervised training, clustering, feature extraction and dimensional reduction, sequential and hierarchical classification, applications of training, feature extraction, and decision rules to engineering problems.
      This course introduces basic error-correcting codes by which channel errors in communications can be detected or corrected. The topics include introductory coding theory, basic algebra for linear codes, and encoding/decoding of cyclic codes, BCH and Reed-Solomon codes, convolutional codes, and Turbo codes.
      This course introduces various theories and tools to efficiently store and transmit source data. Topics cover quantization theory, rate-distortion theory, lossless and lossy compression methods, and their practical applications to multimedia data compressions including speech and image.
      This course covers the fundamentals of wireless communication underpinning the advances in leading-edge wireless technologies. The emphasis is on theory and algorithms for the most salient concepts including multi-input multi-output (MIMO) and OFDMA/CDMA and forefronts of commercialized systems such as WiFi and LTE-A.
      Students will study the design of analog systems using CMOS and bipolar technology. A higher level of design for analog and digital systems is presented. Practical examples for communication microsystems are presented.
      This course aims to convey a knowledge of application-specific integrated-circuit (ASIC) implementation. Emphasis is on the VLSI circuits and chip-level metrics such as power, area, speed and reliability; along with design automation techniques and methodologies (logic synthesis, physical design, design for testability, physical verification). In this semester, extra focus will be given to the following topics: RTL to tape-out using leading-edge EDA tool, and low-power System-on-Chip (SoC) design techniques.
      This course is a study of the sources of noise found in electronic instrumentation. It teaches the recognition of sources of noise and the design techniques to achieve noise reduction.
      Frequency synthesizers generate many discrete RF frequencies from one reference frequency. General synthesizers, digital PLL, direct digital, and hybrid synthesizers are covered.
      Starting from non-linear differential equations, this course presents a systematic approach to the design of electronic oscillators. Design of negative resistance and feedback oscillators is discussed. CAD techniques are employed.
      The aim of this course is to introduce you to the modern electrical circuits and systems with applications in automotive electronic industry. The underlying physics of devices, their functional characteristics, fabrication technologies, design, and simulation aspects of the devices will be covered.
      This course is a lecture for graduate students, especially in Ph.D. course, who are choosing a track in the school of ECE, especially the EE track. It is composed of three-hour lecture as a single course; however, this course requires partially organized student seminars for specific topics of Intelligent Power Interface such as resonant converters. It is designed to give graduate students (Ph.D. or M.S. graduate students who already took prerequisite lectures of Power Electronics) both the advanced principles and practical knowledge of power electronics, especially, practical design considerations of power converters and resonant converters for high power conversion efficiency.
      This course is intended to introduce the fundamental scientific principles and technologies of nano-scale electronic devices. We will start with discussing the basic and key concepts of semiconductor device physics, and then applying those concepts for several conventional electronic devices such as p-n junction, bipolar transistor, Schottky diode, and MOSFET. Finally, we will extend our scope to the new types of nanoscale devices that are currently under extensive research and development as candidates to overcome the limitation of current planar CMOS and flash memories, such as 3D structure transistors (dual-, tri-gate), CNT and nanowire applications, MRAM, FRAM and spintronics, etc.
      This course covers the material properties of III-V compound semiconductor and device fabrication process technologies including epitaxy, doping, and etching, bandgap engineering. Also, several important applications of compound semiconductor such as HEMT will be discussed in depth.
      Plasma is widely used for contemporary materials processing. In this course, the plasma processing of semiconductors and other electronic devices are introduced.
      With a boom of mobile and wearable devices, electromagnetic compatibility problems are becoming increasingly critical due to the decreasing form factor of the systems. This course covers the fundamental theories and necessary skills in the circuit and electromagnetic designs with respect to modeling and analysis of electromagnetic interference (EMI), electromagnetic immunity, electromagnetic susceptibility (EMS), and electrostatic discharge (ESD) issues on system-level, PCB-level, package-level, and IC-chip-level.
      The purpose of this course is to extend knowledge to the advanced electronic carrier transport physics, which include conductance from transmission function, Green’s functions, tunneling and Non-equilibrium Green’s function (NEGF) formalism.
      This course intends to provide and discuss advanced knowledge of nanophotonics. It covers a few current topics related to nanophotonics. The topics include: surface-plasmon polariton, plasmonic waveguides, plasmonic waveguide devices, nanophotonic devices like photonic crystals
      This course introduces advanced research topics in the field of Communication, Control, and Signal Processing I.
      This course introduces advanced research topics in the field of Communication, Control, and Signal Processing II.
      This course introduces advanced research topics in the field of Communication, Control, and Signal Processing III.
      This course introduces advanced research topics in the field of Communication, Control, and Signal Processing IV.
      This course introduces advanced research topics in the field of Electrical Engineering V.
      This course introduces advanced research topics in the field of Electronic Design and Applications I.
      This course introduces advanced research topics in the field of Electronic Design and Applications II.
      This course introduces advanced research topics in the field of Electronic Design and Applications III.
      This course introduces advanced research topics in the field of Electronic Design and Applications V.
      This course introduces advanced research topics in the field of Electronic Design and Applications IV.
      This course introduces advanced research topics in the field of Device Physics I.
      This course introduces advanced research topics in the field of Device Physics II.
      In this course we provide the student with the basic knowledge of electrodynamics, which are necessary to understand the advanced electrodynamics. The electrostatics, magnetostatics, boundary value problems, Maxwell equations, and wave propagations are covered.
      This course is intended to improve our understanding of the basic principles and theoretical schemes of quantum mechanics by revisiting the topics covered in undergraduate quantum mechanics with more systematic and advanced mathematical formalism. The basic assumptions, Dirac notation, Hilbert space, Schrodinger equation, harmonic oscillator, angular momentum, spin and identical particles will be discussed.
      In this intermediate level course of plasma physics, basic frameworks are discussed for understanding of waves in plasmas, diffusion, collisions and energy absorption, MHD model, nonlinear theories of plasma sheath and shock waves etc. The prerequisite is the undergraduate plasma and beam physics or similar topics.
      The interfaces between different materials in an electronic device take crucial roles in determining the functionality and efficiency of the device. This course introduces the basic physics of various interface phenomena occurring in electronic devices, and also the experimental methods characterizing them as well. Particularly, it discusses the electronic band structure and charge/spin transport (lateral, vertical) at interfaces, and their relations to the operational mechanisms of various actual electronic devices.
      This course covers basic principles of vacuum electron devices. The electron beam formation, beam-wave interaction, and application of vacuum electron devices are the main topics of this course. The modern vacuum electron devices such as micro-vacuum electronics, and THz frequency sources will be discussed. Students are required to take pre-requisites for this course.
      This course is composed of two parts. Before the midterm, diverse subjects of laser-plasma interactions including the scattering, energy absorption by Bremsstrahlung, particle acceleration, nuclear fusion, terahertz generation, wakefield, and other nonlinear interactions are briefly introduced. After the midterm, specialized lectures are given on the laser-plasma-based particle acceleration and its numerical simulation.
      This course intends to cover basic principles of nuclear fusion and broad knowledge of the current technology in the world. Physics of fusion plasmas and beam-wave interaction are the main themes of the course. Students are required to take pre-requisites for this course.
      This course will cover the basic concepts, mechanisms, and special issues in organic electronics. Based on understanding of the basic properties of inorganic semiconductors, this course will focus on the applications using organic semiconductors such as organic light-emitting diodes, organic solar cells, and organic field-effect transistors.
      The purpose of this course is to extend knowledge to the state-of-the-art R&D level by invited talks of the experts in various related scientific or engineering fields, and also possibly by presentations of the students in the course to exchange their own ideas and updated information for creative and fine-tuned achievements.
      This course is related to the student’s graduate thesis and dissertation. As such, students should be actively working in a laboratory setting and gaining experience through hands-on experimentation.
      This course is related to the student’s graduate thesis and dissertation. As such, students should be actively working in a laboratory setting and gaining experience through hands-on experimentation

    • This course studies a class of graphical models that represent joint probability distributions of random variables. The topics include conditional dependence of random variables, statistical inference, message passing algorithm, Nash equilibrium, (Non-)cooperative game, Bayesian network, Conditional Random Fields.
      Planning is a fundamental ability for intelligent agents to act successfully in an environment.
      Automated planning has been an active area of research in artificial intelligence for over three decades. Planning techniques have been applied in a number of domains including robotics, process planning, web-based information gathering, and spacecraft mission control. This course aims to introduce the basic algorithms and techniques in AI planning research, with an overview of a wide variety of planning paradigms and applications.
      This course provides the fundamental concepts and algorithms in mobile networks involving cellular networks, mobile ad-hoc networks, and opportunistic networks. The topics covered in this course include naming, routing and transport layer protocols designed and optimized for mobile networks.
      This course introduces fundamental principles behind diverse system software such as linker, loader, debugger, performance profiler and virtualization hypervisor.
      This course provides the in-depth understanding of the design issues of processors, memory hierarchy, data bus architectures, and storage technologies.
      This course studies the theories of graphs that are useful in solving problems in computer science/engineering especially in networking, communication, and database. This course also focuses on how to apply the theories of graphs to practical problems and how to implement the solution techniques using computer languages. The major topics to be covered include matchings, factors, connectivity, coloring, and cycles of various types of graphs.
      This course introduces the theory of formal languages and automata. Finite automata, regular expression, context-free grammar, pushdown automata, turing machine and computability will be covered in this course.
      This course is to introduce the core concepts in operating systems and distributed systems, and study recent research topics on computer systems. This course will cover topics including classic systems, large scale systems, multicore systems, and fault tolerance.
      This course provides the practical design and analysis techniques of algorithms. Parallel programming, linear programming, dynamic programming, approximation programming, randomization, amortized analysis, probabilistic analysis, and other advanced algorithm concepts will be dealt with in this course.
      Through this course, students study basic rules and implementation considerations in implementing a programming language. More details on grammar checks for program syntax, implementation optimization, relations between programming languages and compilers, the role of interpreters, run-time systems, and semantically accurate expressions are also covered.
      This course studies the key design principles of distributed systems, which are collections of independent networked computers that function as single coherent systems. Covered topics include communication protocols, processes and threads, naming, synchronization, consistency and replication, and fault tolerance. This course also examines some specific real-world distributed systems case studies, ranging from the Internet to file systems. Class discussion is based on readings from the textbooks and research papers.
      This is an introductory course on cryptography, covering fundamental cryptographic notions including pseudorandom generators, symmetric-key encryption, message authentication codes, public-key encryption, and digital signatures. Special emphasis is given to rigorous definition and provable security.
      This course introduces state-of-the-art programming techniques for massively parallel computing systems, such as graphics processing units (GPU). The course covers basic parallel programming theories and several programming APIs such as NVIDIA CUDA, OpenCL, and MPI.
      Computational geometry studies efficient algorithms and data structures for solving large scale geometry problems. The topics to be covered include computational complexity, convex hull, line segment intersection, Delaunay triangulation, Voronoi diagram, Euclidean shortest path, mesh generation, and so on. The main goal of the course is to make students familiar with the fundamental data structures for geometric objects and train them to develop the efficient data structures. The knowledge and insight about algorithms and data structures gained from this course can be applied to various computer science research – database management systems, distributed systems, geographic information systems, computer graphics, etc.
      An intelligent agent is an Artificial Intelligence program that situates in a simulated or physical environment and operates on behalf of a user to achieve certain goals or maximize a performance measure. This course provides a board introduction to the design of intelligent agents, with emphasis on agents in electronic markets. We will also cover computational and game-theoretic topics related to the foundations of electronic marketplaces. Topics include agent architectures and modeling, game theoretic analysis of multiagent systems, automated mechanism design, auction and exchange design, computational social choice, incentive-compatibility, privacy in mechanism design, negotiation and bargaining, reputation systems, prediction markets, advertising markets, and electricity markets.
      In this class, we will learn introductory visualization algorithms and data structures frequently used in scientific and information visualization research. The class will cover basic data representation, scalar and vector visualization, image and volume visualization, and information visualization. We will also cover widely used image processing and visualization libraries, such as ITK and VTK.
      This course introduces the concepts of Human-Computer Interaction (HCI) that enables computer scientists to design systems that consider human factors. In this course, students will learn what are the good and bad design from the perspective of users, and analytic and empirical evaluation methods.
      Software engineering is a sub field of computer science that studies how to analyze and understand software requirements, how to build cost-effective designs and solutions to the problems, and how to manage project teams. In this course, students will learn foundational skills for high-quality graphical user interface prototyping and development based on the underlying software architectures and modern software prototyping toolkits.
      Parallel computing enables many computations to be carried out concurrently on parallel platforms ranging from multi-core architectures to high-performance clusters. This course introduces parallel architectures, parallel algorithms, parallel programming models and libraries (Pthreads, MPI, PVM, OpenMP), scalability, locking protocols, data localization, and the theoretical models for parallel computation.
      This course introduces concepts of the design of high-level programming languages. It includes various programming language features, structural operational semantics, denotational semantics, logic semantics, algebraic implementation of data types, attribute grammar formalism, and axiomatic semantics.
      This course will introduce the fundamentals of embedded system design. Students are required to design and implement an application for an embedded systems platform, and to investigate performance tuning.
      This course is to understand key concepts and techniques of cloud computing and virtualization, which is the core technology for cloud computing. This course will cover interesting topics including x86 virtualization, virtual machine management techniques, cloud resource management and optimization, big data analysis on cloud, and high performance computing on cloud.
      Robotics is a topic in artificial intelligence which focuses on the physical aspect of intelligence. A machine that can interact successfully with our physical world is an important incarnation of an intelligent agent. In this course, we will introduce some basic algorithms for robotic research. Topics include, but are not limited to: motion control (PID control), observers and tracking (Kalman filters), localization (particle filters, SLAM), vision (segmentation and object detection), walking (zero-moment point), action and sensor modeling (STRIPS planning, optimization of humanoid walk), path planning (Rapidly-exploring Random Trees), behavior architectures (subsumption architecture), multi-robot coordination (multi-robot patrolling), reinforcement learning (Q-learning, multi-armed bandit), multi-robot interaction (socially intelligent robots), applications (autonomous vehicles), and social implications (Isaac Asimov’s “Three laws of Robotics”). This course gives basic introduction to algorithms and complexity. The topics covered are: review of asymptotic notations, elementary data structures and graph algorithms, dynamic programming, maximum flow, linear programming, Turing machine formalism, the classes P and NP, NP-completeness and reduction, and probabilistic algorithms. This course provides in-depth understanding on the design and implementation of computer and communication networks. It covers a variety of analytical techniques to understand system performance, and advanced networking technologies for performance improvement in wired and wireless environment.
      This course aims at learning how to extract valuable information from visual scenes using computers. Topics may include the basic theories for capturing images by cameras, human visual perception, filtering, edge detection, segmentation, stereo, motion analysis, feature extraction, and object recognition.
      The goal of Machine Learning is to build intelligent system that can adapt behaviors based on their experience. This course will study the theory ad application of machine learning methods in graduate level. The main body of the course will cover computational learning theory and various recently developed machine learning methods. The methods includes supervised/unsupervised learning, on-line learning method, Bayesian inference, Support Vector Machine (SVM), Deep Networks and Conditional Random Fields.
      This course introduces new research topics in the field of Computer Engineering I.
      This course introduces new research topics in the field of Computer Engineering.
      This course introduces new research topics in the field of Computer Engineering.
      This course introduces new research topics in the field of Computer Engineering.
      This course introduces new research topics in the field of Computer Engineering.
      This course introduces the theory and techniques to process natural language with computer systems.
      This course provides diverse techniques for designing intelligent decision-making machines. The topics covered in this course are machine learning, expert systems, neural networks, game theory, operations research, and heuristic algorithms.
      This course is an advanced course on the state-of-the-art 3D computer graphics theories and applications. The course will review recent computer graphics and visualization research articles about 3D modeling, rendering, image processing, and volume graphics.
      This course covers database management system design principles and techniques. Possible topics include internal design of DBMS, indexing, query optimization, parallel databases, distributed databases, geographic information systems, data intensive computing, and big data processing. In the first half of the course, we will review internal design of DBMS. In the second half, we will read milestone papers in DB history as well as the state-of-the-art papers mainly focusing on emerging technologies.
      Computational complexity theory studies how much resource (time or memory, for example) is required to solve a given computational problem. Topics covered in this class includes time complexity, space complexity, randomized computation, quantum computation, and interactive proofs.
      This course introduces theory and design of text-based information retrieval systems. It discusses the models and methodologies used in information retrieval systems, statistical characteristics, representation of information, clustering algorithms, collaborative filtering, automatic text categorization, etc.
      Bioinformatics studies methods for storing, retrieving, and analyzing biological data, such as protein sequence, structure, and genetic interactions. It deals with various computer science fields including algorithms, databases, information systems, artificial intelligence, data mining, image processing, and discrete mathematics.
      The objective of this class is to help students develop the understanding necessary to apply stochastic models to a variety of problems in engineering, science and operations research. The course contains many examples and case studies designed to build insight into the structure of stochastic processes and their impact on real systems, especially in the broad area of communication and networking.
      This course introduces advanced research topics in the field of Computer Engineering I.
      This course introduces advanced research topics in the field of Computer Engineering II.
      This course introduces advanced research topics in the field of Computer Engineering III.
      This course introduces advanced research topics in the field of Computer Engineering Ⅳ.
      This course introduces advanced research topics in the field of Computer Engineering Ⅴ.