Bioinformatics is a growing area which applies the techniques of algorithms, statistics, data science, graphics, etc. to solve problems and verify hypotheses in biology. It plays an essential role in understanding life, health, and diseases at the molecular and cellular level. In the past decade, the DNA sequences of many individuals and various species have been deciphered. Instances of severe diseases are treated with genome sequencing techniques. Terabytes of data are produced daily. It challenges the current data processing and analysis techniques, and brings many opportunities to research and development. Successful addressing of the problems will have a high impact on our lives and society.
Cloud computing promises to provide better agility at lower cost and has the potential to become the next generation model of utility. To fulfil the potential of cloud computing, there are many fundamental research issues to be investigated such as availability, security, privacy and dynamic elasticity. The cloud computing research group is engaged in solutions of cloud security and privacy, congestion control, load balancing, service-level traffic management using SDN, and dynamic resource provisioning.
Data science is a research area which spans many different disciplines such as machine learning, data mining, data warehousing and information retrieval with a common objective of extracting useful information from data. One emerging challenge in this research area is how to handle a large amount of structured and unstructured data through high performance computing technologies. A data science practitioner, i.e., a data scientist, may use their skill sets in the aforementioned disciplines to solve a complex problem in a specific domain, such as genomics, drug design, social networks, security and meteorology.
Embedded systems are computer systems with dedicated functions within mechanical or electrical systems, often with real-time computing constraints. It is embedded as part of a complete device often including hardware and mechanical parts.
Embedded systems range from portable devices, such as smart watches and MP3 players, to large stationary installations like traffic lights, factory controllers, and complex systems like hybrid vehicles, MRI, and avionics. With the recent development of technology, embedded systems are now everywhere and having a significant impact on our daily lives. For example, Google acquired the smart thermostat company Nest Labs for US$3.2 billion recently, which is only a 4-year-old company. Another example is the recently announced Apple iWatch, which is expected to transform how we interact with technology in our daily lives.
Our major research focus is to combine ideas and techniques from evolutionary computation, traditional mathematical programming, and machine learning for designing efficient metaheuristic algorithms for dealing with hard search and optimization problems in fields ranging from engineering design to e-commence and management planning. The metaheuristic optimization group has an excellent research track record. The multi-objective evolutionary optimization algorithms based on decomposition (MOEA/D) developed by us have become one of the most widely used algorithmic frameworks in our area.
Information security covers the general areas of applied cryptography, cloud computing & big data security, and network security. We have a strong group of devoted researchers and competent students working in various fields of cryptography and information security at large. Our mission is to develop sound security and privacy technologies for enhancing society welfare in the era of ubiquitous computing. To achieve this, we investigate both basic and applied research issues, including theoretical cryptographic problems, security protocol design, analysis for secure wireless computing devices, cryptographic algorithm implementation, and risk analysis and security management.
Machine learning is the study of algorithms and systems for computers to learn from data to make predictions. Our current research projects in machine learning focus on probabilistic graphical models, time-series models, deep learning, active learning, kernel methods, clustering, manifold learning and Gaussian processes. Machine learning is also applied to fields such as computer vision, computer graphics, multimedia information retrieval, software engineering, cognitive science and bioinformatics. Another research topic is to hybridize machine learning techniques and various optimization methods for handling expensive and noisy optimization problems in engineering fields, with applications for car-crash simulations and telecommunication system design.
A key requirement of mobile computing and networking is the ability to access critical services and data anytime and anywhere. This means that the mobile network plays an important role in connecting the elements of mobile and pervasive computing systems. In addition, distributed network algorithms are used to carry the information flow across this mobile network. The mobile computing and networking group is engaged in advancing these mobile technologies using rigorous theories and system implementations such as next-generation cellular networks, heterogeneous networks, cognitive radio networks, smartphones, tablets, and cloud computing systems.
Multimedia computing research studies the processing and management of media objects such as image, video, text, and audio. Of particular interest is the effective integration of different media for novel and interactive multimedia applications. The department has established a strong track record in the broad areas of multimedia computing, ranging from motion capture, media storage and coding, and information retrieval to emerging topics in social media, big data analytics, and health care. The developed systems for smart ambience therapy, interactive dancing, smart classroom, scalable video coding, and cross-media retrieval have won numerous awards in international exhibitions and conferences.
Software is increasingly more complex in regard to its infrastructure, execution environments and the characteristics of big data to be processed. Its pace of evolution is also speeding up, while multiple versions of the same piece of software may still be actively used. The Software Engineering Research Group tackles fundamental and real-world research problems raised from the needs of developing and operating high-quality software systems. Our research addresses foundational, contemporary as well as emerging problems from a wide spectrum of software engineering and programming languages topics in cloud computing, big data, software-defined networking, shared-memory, parallel or distributed software systems, global software development, software processes, software quality, and software project management. We are particularly interested in the areas of software analysis, testing and management.
Our research outcomes have not only been disseminated in prestigious international journals and conferences, but also formed a strong basis that informs both teaching and practice, as well as knowledge transfer to the industry.
Theoretical computer science aims to explore the foundations of computer science, especially from the mathematical point of view. There are two major directions within, namely algorithm design and complexity. In algorithm design, researchers aim to design better algorithms. In complexity theory, researchers study the relationship of different complexity classes with the final goal of settling the problem of whether P=NP. Our research groups mainly focus on designing efficient algorithms for real-world problems like energy efficient scheduling and wireless coverage. We are also interested in exploring the optimal structures for some combinatorial problems and truthful mechanism designs in game theory.
The field of computer vision focuses on processing, interpreting, and understanding images and videos. Our current research projects in computer vision range from low-level vision (optical flow, stereo vision, sensor fusion, image denoising, illumination & reflection) to mid-level vision (tracking, 3D reconstruction, dynamic textures, background subtraction, image/video segmentation) to high-level vision (human pose estimation, crowd counting, semantic image/video annotation, object recognition, visual discovery). The field of computer graphics covers the analysis, editing, and synthesis of images, videos, and graphics. Our current research applies computer vision and machine learning techniques to assist image and video synthesis. Representative projects include data-driven comic production, 3D image/video editing, computational photography, data-driven crowd animation and sketch-based object analysis.