Phd (Computer Science)

PhD is the highest degree awarded in an academic discipline and is the most satisfying and rewarding educational experience. Students work closely with a faculty member, performing original research, and tackling challenging and unsolved problems.

The department offers PhD degree with the research emphasis on the following research groups

Human Information Interaction

This research group investigates all aspects of information usage by humans. The research focus varies for information seeking behavior, Information Interaction Techniques, Storage and Retrieval models/frameworks for structured and unstructured information, and information services for human information needs.

Contact Person: Dr. Shuaib Karim

email: skarim@qau.edu.pk

Knowledge Engineering

The focus of this research group is on the analysis of data, metadata and knowledge using supervised and unsupervised mining algorithms. The target areas will be software architecture, web services and overlay networks. The main goal is to process related and uncorrelated facts and extract meaningful contextual knowledge for quality decision making.

Contact Person: Dr. Onaiza Maqbool

email: onaiza@qau.edu.pk

Networking and Communication

This group investigates the applied aspects in the domains of networking, communication, security and privacy. The research group focuses on a number of areas including but not limited to computer networks, distributed systems, mobile Agent-based distributed systems, routing protocols, peer-to-peer computing, security and privacy, intrusion and anomaly detection systems, data leakage prevention, computer criminology, key management schemes, trust management, internet of Things, smart homes and smart cities, pervasive computing, wireless sensor networks, cloud computing, architectural, efficiency, security and privacy issues, distributed web services, security & privacy issues in web 2.0, performance issues in web services, trust in learning management systems, formal modeling and analysis, statistical modeling, social network analysis, and structural and behavior modeling and analysis of distributed protocols and systems.

Contact Person: Dr. Muazzam Khattak

email: muazzam.khattak@qau.edu.pk

Fee structure

PhD Courses

• CS-821: Design for Usability

  Principles for designing the product; Development process for user interaction; Usability specifications; Design representation techniques; Prototyping; Formative user-based evaluation; Usability methods; User interaction development process.

• CS-822: Modeling of Web Information Systems

  Web modeling concepts; Modeling the Web applications for requirements engineering; Content modelling; Navigation modeling (Hypertext, Access structure); Modeling the presentation for the end user; Model driven development and model driven architecture; Evolution of the Web, Web 1.0 (visual Web), Web 2.0 (Social Web), and Semantic Web (the Web of metadata); Hypertext patterns; Persistence of HT patterns; O&M of Web applications.

• CS-823: Data Warehousing

  Overview of the course and a brief history; Data Warehouse Architecture; Extract Transform Load; Data Cleansing Algorithms; Hot and Cold Data; Data Warehouse support for OLAP and Data Mining; Active Data warehousing; Semantic Data warehousing; Oracle solution
  TeraData solution; Case Studies.

• CS-824: Peer-To-Peer Systems

  Overview of P2P Systems and brief history; Taxonomy of P2P Networks/Systems and Analysis of popular P2P Systems; Analysis of unstructured P2P Systems; Analysis of structured P2P Systems; Search Efficiency; P2P-based content delivery; Security and Reliability; Replication in peer-to-peer systems; Anonymity in peer-to-peer systems; Social, Legal and Privacy aspects of P2P Systems.

• CS-825: Machine Learning

  Introduction: Overview of machine learning, Machine learning applications and examples, inductive learning, inductive bias; Decision tree learning: Representation, algorithm, hypothesis space, rule extraction, pruning; Neural networks: Representation, perceptrons, multilayer networks, backpropagation algorithm, Training procedures; Bayesian learning: Bayes theorem, maximum likelihood hypothesis, Bayes classifiers, Baysian belief networks; Instance-based learning: K-nearest neighbours, lazy and eager learning; Evaluating Hypothesis: Hypothesis accuracy, sampling theory, hypothesis testing; Computational learning theory: Probably learning an approximately correct hypothesis, sample complexity for finite and infinite hypothesis, VC dimension; Unsupervised learning: Clustering, types, steps

• CS-826: Pattern Recognition

  Introduction: Overview of pattern recognition, review of matrix algebra, probability distributions and probability; Bayesian Decision Theory: Classifiers, discriminant functions and decision surfaces, normal density and discriminant functions for normal density, error bounds for normal densities; Bayesian Parameter Estimation: Univariate and Multivariate cases for the Gaussian distribution, bias, class-conditional densities; Maximum Likelihood Estimation: The general principle, Unknown parameter cases for Gaussian distribution; Problems of dimensionality: Accuracy and training sample size, computational complexity, overfitting; Component Analysis: Principle Component Analysis, Fisher Linear discriminant; Non-Parametric Techniques: Parzen windows, K-Nearest neighbour rule and estimation.

• CS-827: Social Network Analysis

  Introduction to social networks; random network models; identifying connected components; giant component; average shortest path; diameter; preferential attachment; network centrality; betweenness; closeness; clustering; community structure; modularity; overlapping communities; small world network models; contagion; opinion formation; applications of social network analysis; social media networks.

• CS-828: Computational Modeling

  Introduction to Computational Modeling, History, Motivation and Prospects; Modeling process, input modeling, Random Numbers and Distributions, Monte Carlo Methods; Discrete Event Modeling , Introduction to Markov Process, Queuing Theory; Continues Time Modeling, System Dynamic Modeling; Game Theoretic Modeling, Prisoner’s Dilemma, Nash Equation; Verification and Validation of Models; Verification and Validation Techniques; Data Driven Modeling ;Concept Learning and Decision, Classification and Clustering; Agent Based Modeling, Belief, Desire and Intension (BDI) , Multi-Agent System; Social Simulation , Social Dynamics, Complex Systems, Emergence.

• CS-831: Multimedia Retrieval Techniques

  Multimedia content and motivations for multimedia retrieval; Issues of multimedia Retrieval. Multimedia retrieval models; Content-based image retrieval; Content-based video retrieval; Content-based audio retrieval: audio representations, audio feature extraction; Query modalities and similarity measures; Analysis of existing multimedia retrieval systems, retrieval evaluation criteria, relevance feedback; current trends in Multimedia Retrieval.

• CS-832: Metadata for Information Resources

  Overview of the course and Metadata; History of schemes and metadata communities; Functions and Types of metadata; Metadata Structure and Characteristics: Semantics, syntax, and structure; Metadata creation process models; Interoperability; Metadata Integration and Architecture: Warwick Framework; Resource Description Framework; Open Archives Initiative; Encoding Standards (Markup Languages): Introduction and history of markup; Metadata use of markup languages; Document Type Definitions (DTD); Structural metadata Data Control Standards: Resource Identifiers; Data Registries; Controlled vocabularies; Name authority control (ISAAR and FRANAR); A-Core; Encoded Archival Description (EAD), Text Encoding Initiative (TEI); Metadata Evaluation: User needs; Quality control issues; Evaluation methods; Educational Metadata: Instructional Management Systems (IMS); Learning Object Metadata (LOM); Gateway to Educational Materials (GEM); Government Information Locator Service (GILS); Visual Resources Metadata: Categories for the Description of Works of Art (CDWA); Visual Resources Association (VRA) Core; Computer Interchange of Museum Information (CIMI)

• CS-833: Information Privacy and Access Control

  Privacy, Privacy policies; Privacy enforcement; Adaptive privacy management; Access control mechanisms; Different access control models such as Mandatory, Discretionary, Role-Based and Activity-Based; Access control matrix model; Harrison-Russo-Ullman model and undecidability of security; Confidentiality models such as Bell-LaPadula; Integrity models such as Biba and Clark-Wilson; Conflict of interest models such as the Chinese Wall

• CS-834: Multimedia Communications

  Introduction and Recent history of multimedia technologies; Digital Image, Video and Audio Compression in Multimedia Communications; Networking Technology for Multimedia; Multimedia and the Internet; IETF Standard Protocols for Multimedia Transport; Multimedia Synchronization; Quality of Service (QoS) and Resource Management for Multimedia; Research Challenges in Real-time Multimedia transport; Multimedia conferencing and collaboration tools; Multimedia Security.

• CS-835: Ubiquitous Information Interaction

  information Interaction; Seminal ideas of ubiquitous computing;
  Tangibility and Embodiment; Social computing; Privacy; Critical and cultural perspectives;
  Mobility and Spatiality; Mobile Technology in the Messy Now; Infrastructure;
  Seams, seamlessness, seamfulness; Evaluating Interaction of Ubicomp systems

• CS-851: Human Information Interaction

  Overview of the course and a brief history; Types and structures of information resources; Types and structures of vocabularies; Information retrieval & Interaction in information retrieval
  Search engines, Digital libraries; Search techniques and effectiveness; Advanced searching
  Web search and the invisible web; Information seeking behaviour; User modelling; Mediation between search intermediaries and users; Evaluation of search sources and results; Result Presentation to users; Keeping up: sources for life-time learning

• CS-852: Information Architecture

  Introduction and Overview of the course. Process of Web development; Information behaviour & the web. Content design and organization systems; Copyright issues. Labeling systems; Writing for the Web. Navigation design; Search systems. Page design; Multimedia. Web usability evaluation & testing. Accessibility for users with disabilities. Global audiences; Web standards & policies. Weblogs, Intranets, Websites for mobile devices; Web design software; Web Content Management Systems. Metadata; Search engines
  

• CS-853: Collaborative Data Mining

  Overview of the course and a brief history; Overview of Distributed Database systems; Importance and usage of collaboration; Web Data Resources; A brief introduction to overlay networks; Remote Collaboration; Collaborative Data Mining Guidelines; Parallel Data Mining; Grid-based Data Mining; Collaborative mining over social networks; Collaborative mining in P2P Networks; Collaborative data mining case studies.
  

• CS-854: Enterprise Intelligence

  Overview of the course and a brief history; Introduction to Real-time Business Intelligence; Enterprise level Online Analytical Processing (OLAP); Open Source OLAP tools; Introduction to Online Analytical Mining (OLAM); OLAM Tool Design for relational data source; OLAM Tool Design for mining data streams; Need for OLAM Query Language; Data Mashups for decision support; Data Mashup case studies; Oracle Data Mining.
  

• CS-855: Communication Networks

  Overview of the course & research activities in computer networks; Communication Networks & Services; Overview of network simulations; Layered architecture; Congestion Control and Traffic Management; Wireless, Mobility and Cross layer concepts; Switching & Routing; Quality of Service ( QoS); Multicast; Peer-to-Peer (P2P) and Overlay Networks; Content Distribution in P2P Networks; Multimedia Information & Networking; Network Measurement.
  

• CS-857: Software Evolution and Reengineering

  Introduction: Challenges of evolution, Legacy systems; Evolution Process: Laws of software evolution, Evolution models, Testing in the context of evolution, Metrics for evolution; Evolution Activities: Concepts of, and techniques for activities e.g. Reverse engineering, Re-factoring, Program Transformation, Visualization; Re-engineering techniques: Code restructuring, Source code analysis, Architecture Recovery; Topics in reengineering research
  

• CS-858: Program Comprehension and Reverse Engineering

  Static Analysis: Parsing, lexical analysis, issues in parsing languages; Program analysis: Control flow analysis, Data flow analysis, flow graphs, program dependence graphs, call graphs; Dynamic analysis: Profiling, dynamic testing; Reverse engineering: Design recovery and re-documentation, challenges in reverse engineering, reverse engineering approaches; Reverse engineering techniques: Techniques for reverse engineering at the program level, Techniques for reverse engineering at the architectural level
  Reverse Engineering tools
  

• CS-861: Information Visualization and Presentation

  Overview of the course and Information Visualization; Types of Graphs and Visualizations
  Data Types and Graph Types; Design Choices in Building Basic Graphs; Multidimensional Graphing; Graphing and Basic Statistics; Perceptual Properties; How to Critique Visual Designs
  Interactive Visualization; Multidimensional Interactive Visualization; Animation; Visualization Networks; Visualization for Search Interfaces and related Fields; Visualization for Text Analysis; 3D in Visualization; Research trends in Information Visualization
  

• CS-862: Accessibility of Interactive Systems

  Vision of Web Accessibility (Web Accessibility Initiative); Accessibility guidelines; Different accessibility components (content developers, authoring tools, evaluation tools, contents, user agents, assistive technologies, user's characteristics); WCAG, ATAG, UAAG, EARL; Accessibility in various projects (Java Accessibility, Microsoft Enable, .); User's disabilities, human disease ontology; User interface characteristics; Modelling of user's disabilities, UI characteristics in Web Technologies; WAI-ARIA, the Accessible Rich Internet Applications.
  

• CS-863: Metadata Model Management

  Overview of the course and a brief history; Autonomous Data Sources; Introduction to MetaData Models; Structured and Semi-structured data models; Data exchange and data integration applications; Requirements for transforming metadata model; Guidelines for transforming metadata model; MetaData Model matching; MetaData driven data exchange; MetaData driven data integration; Evolving schemas and their effect on corresponding evolution of mappings.
  

• CS-865: Advances in Next Generation Networks

  Next Generation Internet/Networks: "Convergence to IP"; Network Technologies and Architectures; Quality of Service; Multimedia protocols; Policy routing; Future Internet; Network traffic optimization; Next Generation Internet and broadband deployment; Advances in wireless mobile networks; Advances in sensor networks; Management of Next Generation Networks
  

• CS-866: P2P-based Information retrieval

  Overview of the Information Retrieval Systems; Multimedia & its characteristics; P2P Systems & its characteristics; Content searching/locating in P2P systems; Emerging coding standards for information; Architecture of P2P-based information retrieval; Privacy & security issues in P2P-based information retrieval; Current research trends in P2P-based information retrieval.
  

• CS-867: Advanced Software Architecture

  Re-use in architectures: Software product lines, evaluation and validation of product lines, product line testing, re-use in product lines; Service oriented architectures (SOAs): SOA concepts, risks and challenges, quality attributes and SOAs, evaluating and testing SOAs; Architectural evaluation: Methods for architectural analysis, Comparison of methods; Architectural evolution and reconstruction: Models of software evolution, analysis and metrics for evolution, Techniques and tools for architecture reconstruction; Architectures in dynamic environments: Modeling and analyzing dynamic software architectures; Self healing architectures: The need for self-healing, approaches for self healing
  

• CS-868: Software Refactoring

  Refactoring principles: Reasons for refactoring, what to refactor, Challenges in refactoring ; Refactoring categories: Refactoring in the small and large; Refactoring techniques: Recognizing bad smells in code, refactoring for organizing code, higher abstraction, improvement and others; Refactoring of UML models
  Tool support for refactoring: Strengths and limitations
  

• CS-869: Advanced topics in Machine Learning

  Introduction: Overview of machine learning, Machine learning applications and examples; Reinforcement learning: Elements of reinforcement learning, Model based learning, Temporal difference learning, Generalization; Genetic Algorithms: Genetic operators, fitness function, Hypothesis space search, Genetic programming; Support Vector Machines: Optimal separating hyperplane, softmargin hyperplane, kernel functions, SVMs for regression; Combining learners: Voting, Bagging, Boosting; Assessing and Comparing Classification Algorithms: Cross-validation and resampling, Measuring error, Assessing performance, Comparing multiple classification algorithms.

• CS-871: Digital Preservation

  Introduction to Digital Preservation. Digital objects, and their preservation; Key issues such as obsolescence of storage media, software and data formats, hardware, and Digital Curation in Digital Libraries; their solution; Benefits of digital preservation such as Legal, Accountability & protection from litigation, Protecting the long term view, Protecting investment, Reuse; Reference Models for digital preservations; Role of Metadata and Registries; Preservation Methods, approaches, and their evaluation; Selection and appraisal methodologies; Digital Curation in Digital Libraries; Audit and Certification of Preservation Processes and Repositories.

• CS-872: Mining Massive Datasets

  Introduction to map-reduce; large scale file systems, hadoop distributed file system (HDFS); similarity search; minhashing; locality sensitive hashing; fast data-stream processing; PageRank; link-spam detection; frequent itemset mining; clustering very large and high-dimensional datasets; dimensionality reduction: PCA, SVD; CUR decomposition.

• CS-873: Ontology Engineering

  Ontology building methodologies; Ontology design patterns; Different ontology layers such as upper, domain and task ontologies; Ontology languages; Knowledge discovery for ontology construction; Knowledge Interchange Format (KIF); Role of annotations and Web in ontology development; Ontology reuse methodologies; Ontology engineering cost models; Ontology maintenance methodologies; Ontology mediation, merging, alignment, integration; Ontology evolution (data driven and usage driven approaches).

• CS-874: Advanced Topics in Networking

• CS-875: Online Social Networking Systems: Technological Perspective

  Introduction and history of Online Social Networks; Analysis & Design of Online Social Networks; Characteristics of Online Communities; Web 2.0; Application development of Online Social Networks; Clustering & Community Detection; Applications of Online Social Networks; Privacy and other Online Social Issues; Social impact of the social web.

• CS-876: Social Media Content Analysis

  Overview of social media; folksonomies; formal structure of folksonomies; crawling data using APIs; metadata associated with social media; context in social media; contents in social media: tags, photos, videos, status updates, bookmarks; analyzing tagging data; analyzing contents in social media: low-level image features (MPEG7), textual contents, micro blogs; concept identification; recommendation systems; event detection and prediction.

• CS-877: Software Repositories Mining

  Development models: Team formation and collaboration, task assignment in software projects, determining expertise; Prediction: Quality and defect prediction; Evolution models of software: Predicting changes and size, impact analysis; Visualization: Modeling aspects of software repositories; Meta-models and exchange format for mining tools; Tools for software repository mining

• CS-878: Grammatical Inference for Software Engineering

• CS-879: Software and System Specification

• CS-881: Visual analytics

  Introduction and overview of the course. Analytical Reasoning and Critical Thinking. Mental and Visualization Models. Data: Representations, Transformations, and Statistics. Visual Representations. Interaction. Communication: Production, Presentation, Dissemination. Sense Making. Collaborative Visual Analytics. Evaluation of VAST systems.