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 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. Umer Rashid
Email: umerrashid at qau dot edu dot 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 Data Management, Social Computing, Image Processing and Computer Vision, Natural Language Processing, Information Retrieval, Software Testing, and Software Re-engineering. 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 at qau dot edu dot pk
Networking and Communication
This group investigates the applied aspects in the domains of Networking, Communication, Internet of Things, Next generation Networks, Network/Information Security and Privacy, and Software Defined Networks. The research group focuses on 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, IoT, smart homes and smart cities, pervasive computing, WSN, cloud computing, and distributed web services.
Contact Person: Dr. Muazzam Khattak
Email: muazzam.khattak at qau dot edu dot pk
Scheme of Study — Fall 2024 onwards
Elective Courses by Focus Area
Elective / Optional Courses
(51 courses)
CSC-815: Neural Information Retrieval (DE) (3 cr.h)
Information retrieval fundamentals, information retrieval evaluation, word representational learning, word embeddings, language modeling, Word2Vec, FastText, word embeddings in information retrieval, query expansion with word embeddings, application to patent retrieval, neural networks, neural network methods in NLP, Sequence Modeling with CNNs (Convolutional Neural Networks) and RNNs (Recurrent Neural Networks): modeling word n-grams with CNN, hierarchical CNNs, recurrent neural networks, simple RNN, RNN as Encoder, LSTM, LSTM gating mechanism, encoder-decoder architecture, attention mechanism, encoder-decoder, and attention, Transformer and BERT: transformer architecture, contextualization via self- attention, transformer – positional encoding, masked language modeling, BERT, and extractive question answering, Neural Re-ranking: text-based neural models, properties of neural IR models, neural re-ranking models, re-ranking evaluation, KNRM (Kernel based Neural Ranking Model), and convolutional KNRM, Transformer Contextualized Re-ranking: web search with BERT, Re-ranking with BERT, splitting BERT - PreTTR and ColBERT, transformer-kernel ranking, TK (Transformer-Kernel) ranking, TKL (Transformer- Kernel for Long documents)and a hybrid approach IDCM (Intra-Document Cascade Model), domain specific information retrieval applications, Dense Retrieval Models – Knowledge Distillation: neural methods for IR beyond re-ranking, dense retrieval, dense retrieval and re-ranking, dense retrieval lifecycle, BERTDOT model, nearest neighbour search, nearest neighbour search – GPU brute-force, approximation of nearest neighbour search, knowledge distillation, DistilBERT, distillation in IR, deep learning based recommender systems.
CSC-816: Medical Image Analysis (DE) (3 cr.h)
Introduction to medical image analysis, medical imaging modalities, image acquisition techniques: MRI, CT, ultrasound, X-ray, challenges and characteristics of medical image data, role of medical image analysis in healthcare and medical research, medical image preprocessing, image denoising and artifact correction techniques, image enhancement methods, image segmentation in medical images, feature extraction and representation, image registration and fusion, classification and diagnosis, machine learning and deep learning approaches for medical image classification and diagnosis, computer-aided diagnosis (CAD) systems in medical imaging and performance measures.
CSC-817: Transfer Learning and Applications (DE) (3 cr.h)
Overview of machine learning and deep learning, neural networks and optimization algorithms, Introduction to Transfer Learning and pretrained models, evaluation and model selection, Feature extraction using pretrained models, Techniques for fine-tuning pretrained models, Unsupervised domain adaptation and self- supervised learning, multi-task learning and applications, Big Transforms, Vision Transforms, Transfer learning in computer vision, Challenges and considerations in transfer learning for computer vision tasks, Transfer Learning in Natural Language Processing (NLP), Fine-tuning pretrained language models for various NLP tasks, Applications of transfer learning in sentiment analysis, text generation, and question answering, Meta-learning and few-shot learning, Lifelong learning.
CSC-818: Advanced Computer Vision (DE) (3 cr.h)
Advanced Image Processing Techniques, Image enhancement: multi-scale processing, contrast enhancement, Edge detection, Image restoration, Feature Extraction and Description, Matching and alignment of image features, Image Segmentation, Semantic Segmentation, Object detection with CNNs, Transfer learning and fine-tuning pre-trained models, 3D object recognition and pose estimation, Video Analysis and Tracking, Generative Adversarial Networks (GANs) for image generation and style transfer, Attention mechanisms in computer vision, Visual understanding in specific domains: robotics, medical imaging, autonomous vehicles
CSC-821: Design for Usability (DE) (3 cr.h)
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.
CSC-822: Modeling of Web Information Systems (DE) (3 cr.h)
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.
CSC-823: Data Warehousing (DE) (3 cr.h)
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.
CSC-824: Peer-To-Peer Systems (DE) (3 cr.h)
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.
CSC-826: Pattern Recognition (DE) (3 cr.h)
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.
CSC-827: Social Network Analysis (DE) (3 cr.h)
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.
CSC-828: Computational Modeling (DE) (3 cr.h)
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.
CSC-831: Multimedia Retrieval Techniques (DE) (3 cr.h)
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.
CSC-832: Metadata for Information Resources (DE) (3 cr.h)
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)
CSC-833: Information Privacy and Access Control (DE) (3 cr.h)
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
CSC-834: Multimedia Communications (DE) (3 cr.h)
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.
CSC-835: Ubiquitous Information Interaction (DE) (3 cr.h)
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
CSC-836: Search Spaces Discovery (DE) (3 cr.h)
Information needs; information seeking behavior; search types: lookup, exploratory, and leisure-oriented search; information search process; search patterns; Multimedia information retrieval types; information retrieval interaction; nonlinear and multimodal data models; search results spaces; information exploration services; taxonomy of discovery; information discovery theories and models; search and discovery services; search results discovery scenarios; user studies; search results discovery evaluation.
CSC-837: Multimodal User Interfaces (DE) (3 cr.h)
Classical interaction models and paradigms; human cognitive models and interactions; multimodal interaction myths; multimodal inputs/outputs and processing; information dimensions and multimodalities; multimedia input/output systems; multimodal interaction patterns; multimodal interfaces classification; user-centered multimodal interfaces and interaction; virtual reality and augmented reality interfaces; multimodal search user interfaces; design guidelines, user studies; multimodal interface evaluation.
CSC-838: Recommender Systems (DE) (3 cr.h)
Introduction to recommender systems; Collaborative recommendation; Content-based recommendation; Knowledge-based recommendation; Hybrid recommendation approaches; Evaluating recommender systems; Recommender systems and the next-generation web; Group Recommender Systems; Trust-Aware Recommender Systems; Social Recommender Systems; Time and Location Sensitive Recommender Systems, Cross-Domain Recommender Systems, Multi-Criteria Recommender Systems; Advanced topics in recommender systems
CSC-839: Information Cryptography (DE) (3 cr.h)
Introduction to Nondeterministic and deterministic cryptography, Computationally secure encryption, Asymptotic ciphers, Computational Indistinguishability, Symmetric multi-party computation, Searchable encryption, Secure multiparty computation, Oblivious data transfer, Shannon perfect security, Linear and Nonlinear feedback shift registers, Block ciphers structures, Design and analysis of block ciphers primitives, Design and analysis of stream cipher primitives, Design and analysis of collision resistant hashing algorithms, Differential and Linear Cryptanalysis, Attacks on block ciphers, Attacks on stream ciphers, Partitioning cryptanalysis, Brute-force attacks, Plaintext attacks, Man in middle attack, Chosen plaintext attack, Known key attack, Ciphertext-only attack, Non-deterministic and deterministic random bit generator, Statistical randomness evaluation, Key generation, Key distribution centre, Factorization of polynomials, Gröbner basis algorithms, Shor's basis algorithm, Digital signature standard, Merkle–Hellman knapsack cryptosystem, Public key exchange, Zero-Knowledge Cave, Certification authorities.
CSC-841: Advanced Social Network Analysis (DE) (3 cr.h)
Overview of social network analysis; dynamic networks – representation, measures, centrality, connectedness, growth, applications of dynamic networks; multiplex networks – formulation of multiplex networks, weighted multiplex networks, unweighted multiplex networks, neighbors, paths, layers, centrality measures in multiplex networks, communities in multiplex networks; network resilience – measuring resilience, failure in networks, targeted attacks in networks; information diffusion – modeling information diffusion, epidemic models, influence models, explanatory models; recent trends in social network analysis.
CSC-842: Research in Social Computing (DE) (3 cr.h)
Introduction to social computing – goals, challenges, and recent research; social computing platforms – blogging, social networking, professional social computing platforms; collecting social computing data – Application Programming Interfaces (APIs), crawling, online surveys, benchmarks; research methods in social computing – descriptive methods, predictive methods, geospatial methods; qualitative research methods for social computing – focused groups, interviews, semiotics, opinion mining; spatial analysis methods – spatial interpolation, spatial regression, spatial visualization; temporal analysis methods – temporal data representation, analyzing time series data, prediction and grouping in temporal data; network-science based methods – graphs, nodes, edges, centrality measures, community detection.
CSC-843: Advanced Information Retrieval Techniques (DE) (3 cr.h)
Information Retrieval, Boolean Retrieval Model and Query Processing, Phrase Queries and Positional Queries, Ranked Retrieval Models: Motivation, Jaccard Coefficient, Log Frequency Weighting, TFIDF, SMART, Cosine Similarity Measures, Evaluating Search Engines, TF Transformation and BM25, BM25F and BM25+, Evaluation Measures: Precision, Recall, F-Measure, Average Precision, MAP and gMAP, Mean Reciprocal Rank, nDCG, Kappa Measure, Index Compression: RCV-1 Statistics, Dictionary Compression, Postings Compression, Text Classification, Naïve Bayesian, Probabilistic Retrieval Model, Probabilistic Language Modelling, N-gram language modelling, Feedback in text retrieval, Feedback in Vector Space Model, Link analysis and Page Rank, Recommender Systems: Content Based Recommendation and Collaborative Filtering
CSC-844: Advanced Digital Image Processing (DE) (3 cr.h)
Review of digital image fundamentals, noise models, image restoration, image de-blurring, feature detection and characterization, shape representations, deformable models, statistical shape analysis, object segmentation, Digital data hiding, Digital Watermarking, Image based forensics, medical imaging, satellite imaging, video modeling and compression, and other related recent research topics of interest.
CSC-845: Deep Learning (DE) (3 cr.h)
Review of Neural Networks, activation functions & back-propagation; multilayer neural nets, deep networks, learning algorithms, Convolutional Neural Networks: History, Convolution, Pooling, CNNs for classification, CNN Architectures; Sequence Modeling: Recurrent and Recursive Nets: Long-Short Term Memory models and variants, Language modeling and image captioning, Unsupervised learning: Restricted Boltzmann Machines and Auto-encoders; Case Studies.
CSC-846: Advance Natural Language Processing (DE) (3 cr.h)
Introduction to NLP; Observations and Target Encoding; Tensors; Sequence Modeling; Constituency Grammars and Parsers; Dependency Parsing; Information Extraction; Machine Translation; Transformers; Natural Language Generation; Coreference Resolution; Integrating Knowledge in Language Models; Pretrained Language Models; Advanced topics in NLP
CSC-851: Human Information Interaction (DE) (3 cr.h)
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
CSC-852: Information Architecture (DE) (3 cr.h)
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
CSC-853: Collaborative Data Mining (DE) (3 cr.h)
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.
CSC-854: Enterprise Intelligence (DE) (3 cr.h)
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.
CSC-855: Communication Networks (DE) (3 cr.h)
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.
CSC-857: Software Evolution and Reengineering (DE) (3 cr.h)
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
CSC-858: Program Comprehension and Reverse Engineering (DE) (3 cr.h)
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
CSC-861: Information Visualization and Presentation (DE) (3 cr.h)
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
CSC-862: Accessibility of Interactive Systems (DE) (3 cr.h)
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.
CSC-863: Metadata Model Management (DE) (3 cr.h)
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.
CSC-865: Advances in Next Generation Networks (DE) (3 cr.h)
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
CSC-866: P2P-based Information retrieval (DE) (3 cr.h)
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.
CSC-867: Advanced Software Architecture (DE) (3 cr.h)
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
CSC-868: Software Refactoring (DE) (3 cr.h)
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
CSC-871: Digital Preservation (DE) (3 cr.h)
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.
CSC-872: Mining Massive Datasets (DE) (3 cr.h)
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.
CSC-873: Ontology Engineering (DE) (3 cr.h)
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).
3 cr.h
CSC-875: Online Social Networking Systems: Technological Perspective (DE) (3 cr.h)
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.
CSC-876: Social Media Content Analysis (DE) (3 cr.h)
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.
CSC-877: Software Repositories Mining (DE) (3 cr.h)
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
3 cr.h
CSC-881: Visual analytics (DE) (3 cr.h)
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.
CSC-885: Deep Learning on Graphs (DE) (3 cr.h)
Introduction to Graphs; Introduction to Deep Learning; Algorithms for Node Embeddings (random walk baed methods, deep learning based methods); Algorithms for Graph Embeddings; Graph Neural Networks (message passing framework, aggregation, deep learning); Graph Convolution Networks; Graph Attention Networks; Graph Autoencoders; Graph Transformers; Scaling GNNs; Applications of GNNs (node classification, link prediction, graph classification, community detection, etc.).
Artificial Intelligence AI
(3 courses)
CSC-819: Machine Learning for Software Engineering (DE) (3 cr.h)
Representation of problem and ML/DL models for Software Engineering tasks related to the following: Requirements Engineering (e.g. requirement tracing, requirement prioritization, requirement assessment), Design and Modeling (e.g. design pattern detection, software modeling, architecture evaluation), Implementation (e.g. code smell detection, code summarization, code comment management), Defect Analysis (e.g defect localization, defect categorization, defect cause analysis), Project management (e.g. software effort estimation, software crowdsourcing recommendations), evaluation approaches and measures, embedding techniques and pre-trained models for software, factors in selection of ML/DL, challenges in application of ML for SE, future research directions
CSC-825: Machine Learning (DE) (3 cr.h)
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
CSC-869: Advanced topics in Machine Learning (DE) (3 cr.h)
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.
Data Science DS
(3 courses)
CSC-819: Machine Learning for Software Engineering (DE) (3 cr.h)
Representation of problem and ML/DL models for Software Engineering tasks related to the following: Requirements Engineering (e.g. requirement tracing, requirement prioritization, requirement assessment), Design and Modeling (e.g. design pattern detection, software modeling, architecture evaluation), Implementation (e.g. code smell detection, code summarization, code comment management), Defect Analysis (e.g defect localization, defect categorization, defect cause analysis), Project management (e.g. software effort estimation, software crowdsourcing recommendations), evaluation approaches and measures, embedding techniques and pre-trained models for software, factors in selection of ML/DL, challenges in application of ML for SE, future research directions
CSC-825: Machine Learning (DE) (3 cr.h)
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
CSC-869: Advanced topics in Machine Learning (DE) (3 cr.h)
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.
PhD (Computer Science) Frequently Asked Questions
PhD Admissions are announced twice in a year (Spring and Fall semesters).
PhD admission test is a subjective type test, based on core areas of Computer Science. The test is designed to evaluate the knowledge of the applicant in Computer Science core subject areas. The format is similar to our MPhil test (sample provided here), but is expected to be more rigorous.
Duration of admission PhD admission test is 120 – 180 minutes.
For PhD admission, you can apply online by visiting the university website http://ugadmissions.qau.edu.pk/
No, PhD program is a full-time program.
In order to know more about fee structure of PhD program, please visit http://qau.edu.pk/phd-fee-structure/
Minimum duration of PhD studies is 3-years whereas maximum is 7-years.
Yes, you have to provide NOC from employer at the time of application form submission and study leave notification is required after securing admission.
In order to know more about courses, click here
Students must take course work of 18 credit hours preferably in first year.
There will be comprehensive exam based on Computer Science subject areas.
In order to know more about faculty profile, click here
It is recommended to visit faculty profiles and communicate with the potential supervisor before applying for PhD admission.
Yes, the synopsis writeup helps us to determine your area of interest and assign a supervisor from the relevant area.
PhD supervisor is assigned at the time of admission