Ph.D. of Computer Science: Informatics

This course starts with big data modeling and management systems for real-time and semi structured data. Systems and tools discussed include: AsterixDB, HP Vertica, Impala, Neo4j, Redis, SparkSQL. Machine learning algorithms and scaling up for big data is subsequently presented as well as topics including cluster analysis, association analysis, and graph analytics including connectivity, community, and centrality analytics. Computing platforms for graph analytics for large scale graph processing are also investigated with example including analysis of data acquired Internet of Things (IoT) devices.

This is an advanced course on machine learning, focusing on recent advances in deep learning with neural networks, such as recurrent and Bayesian neural networks. The course will introduce the mathematical definitions of the relevant machine learning models and derive their associated optimization algorithms. Topics to be covered include Bayesian modelling and Gaussian processes, randomized methods, Bayesian neural networks, approximate inference, variational autoencoders, generative models, recurrent neural networks, backpropagation through time, long short-term memory, attention networks, memory networks, and neural Turing machines.

This course covers advanced research topics in computer vision assuming basic knowledge of computer vision. The course will prepare graduate students in both the theoretical foundations of computer vision as well as the practical approaches to building real computer vision systems. Topics covered include multi-view geometry, motion analysis and activity recognition, unsupervised representation learning, image style transfer, deep learning for 3D classification and segmentation, image-to-image translational networks.

This course provides in-depth study of advanced methods and research topics of current interest in image processing and analysis. The course covers nonlinear scale space and anisotropic diffusion, differential invariant structures, image registration including deformable registration (snakes, level sets) and atlas building, shape representations and the theory of shape spaces, level set segmentation, statistical shape analysis, and Markov random fields

This course provides candidates with an understanding of advanced imaging systems and their integration across the fields of diagnostic radiology, nuclear medicine and radiotherapy. This will include study of time-resolved ("4-dimensional") imaging (CT, MRI, and US), image-guided radiotherapy, and hybrid-modality imaging (e.g. PET/CT) together with topics such as image registration and its integration into treatment facilities and protocols. Emerging medical imaging modalities such as Phase Contrast Imaging are also explored during the course.

This course covers various topics which includes video spatio-temporal sampling, motion estimation, parametric motion models, motion-compensated filtering, noise reduction, restoration, super-resolution, deinterlacing, video sampling structure conversion, and 46 compression (frame-based and object-based methods). Also, more advance topics such video segmentation, layered video representations, transform coding, entropy coding, scalable video coding, watermarking, video streaming, compressed-domain video processing, and digital TV will be covered.

This course discusses advanced approaches and tools for big data processing. The course starts with describing popular big data frameworks with focus on Hadoop and Spark, HDFS, YARN, and MapReduce. The use of Pig, Hive, and Impala to work on data stored in HDFS is subsequently presented. Data ingestion with Sqoop and Flume, and real-time parallel processing with functional programming in Spark are investigated along with advanced optimization strategies. Security issues in big data and managing big data streams are also discussed.

This course focuses on the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning or corpus based methods and algorithms. It also covers applications of these methods and models in syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization. Topics covered include Parsing and Syntax, Lexical Similarity, Log-Linear Models, Grammar Induction, Machine Translation, Maximum Entropy, Word Sense Disambiguation, Named Entity Tagging, and Joint Inference and Belief Propagation.

Modern-day biology is increasingly characterized by genome-scale and data-driven approaches. Bioinformatics use mathematics, statistics and computing to manage, analyze and build models from biological data to solve scientific problems. Present-day bioinformaticians are typically either bio-scientists armed with the methods of computer science, statistics and mathematics, or data analysts intimately acquainted with the nature and challenges of molecular biology. Moreover, the course is designed to leverage synergies between the two groups. The students get hands-on experience using some of the relevant tools and databases to apply it on different subfields of bioinformatics, from various facets of DNA sequence analysis to predicting RNA and protein structure. In these practical sessions, the students will apply bioinformatics tools to real-world biological problems. The course aims to instill an appreciation and understanding of a range of computational and statistical applications in biology involving the processing, analysis of and model-building from genomic data and other biological data.

This‬ course‬ is‬ tailored‬ to‬ introduce‬ students‬ to‬ the‬ latest‬ advances‬ in‬ the‬ various‬ fields‬ in‬ Informatics,‬ and/or ‬to ‬focus ‬on‬ a ‬specific‬ area ‬of‬ particular‬ interest ‬to‬ the‬ discipline.‬ The‬ course‬ is‬ a series‬ of‬ lectures‬ covering‬ current‬ research‬ and‬ research ‬trends ‬in ‬the‬ area‬ of ‬informatics.‬ Topics‬ may ‬include ‬advanced‬ aspects‬ of‬ some ‬of‬ the ‬following:‬Cloud ‬Computing,‬ Visualization,‬ Multimedia,‬ Medical ‬Image‬ Understanding,‬ Next‬ Generation‬ Sequencing ‬(NGS)‬, Data‬ Analysis,‬ Metagenomics,‬ Taxonomic‬ Classification,‬ among ‬others.