PhD Engineering, Computer Science

PhD in Computer Science degree program caters to the development of strong computer and information technology skills that can be applied in different professional pursuits. The course curriculum of this degree program allows the students to become experts in information system analysis, programming and hardware installation, server management and networking, system analysis, computer architecture organization, data analysis and software technology that is used in leading global organizations. After completing this degree programs, students become eligible to acquire employment at not only big multi nationals but also government and state agencies as well.

In order to apply for the Phd in Engineering – computer science degree program you must have a graduate degree in computer science or engineering.

WHAT YOU'LL LEARN

As a result of completing this program, students should be able to

  • Learn to think independently (unless you're in an intense lab structure where your adviser or post-docs dictate your research, but that seems very uncommon).
  • Spend a few years thinking deeply about a problem that they are interested in
  • Learn how to break down problems, and set about seeking to solve them.
General studies
subjects
semester
units
course
description

Information Research Strategies

2

Introduction to information research including electronic resources. This course is designed to help researchers locate, evaluate, and use information. It includes exploration of the research process, search strategies, locating resources, source documentation, and organization of research.

General studies
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semester
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Statistical Modeling and Analysis for Complex Data Problems

2

Reviews some of today's more complex problems, and reflects some of the important research directions in the field. Twenty-nine authors – largely from Montreal's GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and processes – present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains.

Optimal Experimental Design

2

Introduces the philosophy of experimental design, provides an easy process for constructing experimental designs, calculating necessary sample size using R programs and teaches by example using a custom made R program package: OPDOE introduces experimenters to the philosophy of experimentation, experimental design, and data collection. It gives researchers and statisticians guidance in the construction of optimum experimental designs using R programs, including sample size calculations, hypothesis testing, and confidence estimation. A final chapter of in-depth theoretical details is included for interested mathematical statisticians.

Mathematical Modeling

2

Complete range of basic modeling techniques: it provides a consistent transition from simple algebraic analysis methods to simulation methods used for research.

Research Methods and Design

2

Learners gain a thorough understanding of statistical tests appropriate to their dissertation topic and design, how to interpret the results of the tests and how to conduct follow-up analyses, as appropriate. This course includes guidelines and "best practices" for collecting data.

Dissertation Planning, Writing, and Defending

2

step-by-step through the dissertation process, with checklists, illustrations, sample forms, and updated coverage of ethics, technology, and the literature review.

General studies
subjects
semester
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Information Theory

2

Mathematical models for channels and sources; entropy, information, data compression, channel capacity, Shannon's theorems, and rate-distortion theory.

Coding Theory

2

General discussion on coding theory with emphasis on the algebraic theory of cyclic codes using finite field arithmetic, decoding of BCH and RS codes, convolution codes and trellis decoding algorithms.

Digital Image Processing

2

Image formation, enhancement, and reconstruction. Applications in medical imaging, computer vision, and pattern recognition.

Computational Intelligence - Theory and application

2

This course covers the four main paradigms of Computational Intelligence, viz., fuzzy systems, artificial neural networks, evolutionary computing, and swarm intelligence, and their integration to develop hybrid systems. Applications of Computational Intelligence include classification, regression, clustering, controls, robotics, etc.

Compiler Design, Theory, and Optimization

2

Design and theory of programming language translators and the theory and implementation of optimizers. Topics include: intermediate representations, advanced code generation, control- and data-flow analysis, advanced compiler optimization, dynamic compilation, global register allocation and instruction scheduling.

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Theory of Computation

3

Topics covered include Turing machines and their variants, the halting problem and decidability, computability, reducibility, NP-completeness, time and space complexity, and topics from recursive function theory. The course starts with a brief review of the computation models from CS3311.

Advanced Algorithms

3

Design and analysis of advanced algorithms. Topics include algorithms for complex data structures, probabilistic analysis, amortized analysis, approximation algorithms, and NP-completeness. Design and analysis of algorithms for string-matching and computational geometry are also covered.

Parallel Algorithms

3

Advanced topics in the design, analysis, and performance evaluation of parallel algorithms. Topics include advanced techniques for algorithm analysis, memory models, run time systems, parallel architectures, and program design, particularly emphasizing the interactions of these factors.

Advanced Computer Architecture

3

An in-depth study of various aspects of parallel processing, with an emphasis on parallel architectures. The course has an analytical focus and investigates models of various aspects of the design and analysis of parallel systems. Topics include simple uniprocessor/multiprocessor performance models, pipelining, instruction-level parallelism, and multiprocessor design issues.

Distributed Systems

3

Covers time and order in distributed systems; mutual exclusion, agreement, elections, and atomic transactions; Distributed File Systems, Distributed Shared Memory, Distributed System Security; and issues in programming distributed systems. Uses selected case studies.

Systems Performance Analysis

3

Analysis of the performance of computer systems. Topics include: measurement techniques and tools, probability theory and statistics, experiment design and analysis, simulation, queuing models. Course includes a significant experimental component.

Software/Hardware Design of Multimedia Systems

3

A comprehensive overview of the design and implementation of the hardware and software of a platform for multimedia applications. Topics include system level design methodology, single-instruction-multiple data processor (SIMD), virtual platform implementation, development of an SIMD parallel compiler, and real-time operating systems (RTOS).

GPU and Multicore Programming

3

Introduction to Graphics Processing units (GPU) and multi-core systems, their architectural features and programming models, stream programming and compute unified driver architecture (CUDA), caching architectures, linear and non-linear programming, scientific computing on GPUs, sorting and search, stream mining, cryptography, and fixed and floating point operations.

Data Visualization

3

Introduction to scientific and information visualization. Topics include methods for visualizing three-dimensional scalar and vector fields, visual data representations, tree and graph visualization, large-scale data analysis and visualization, and interface design and interaction techniques.

Advanced Artificial Intelligence

3

Course topics include current topics in artificial intelligence including agent-based systems, learning, planning, use of uncertainty in problem solving, reasoning, and belief systems.

General studies
subjects
semester
units
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description

Concept Paper

2

Ethical issues in research are studied and the Learner evaluates the research plan developed in modules RSH8951-RSH8953 against accepted ethical principles and practices in the field. The material developed in the modules is integrated into a summarizing document called the Dissertation Research Proposal. The proposal is comprised of Chapter I (Introduction), Chapter II (Literature Review), and Chapter III (Methodology).

Doctoral Comprehensive Examination

2

Assures that the Learner has mastered knowledge of his or her discipline, specialization, and can demonstrate applications of that knowledge before formal candidacy status is granted and research in support of the dissertation is initiated. Satisfactory/Unsatisfactory grade only.

Doctoral Dissertation Research l

2

Continuation of RSH8954-P. The draft of the Dissertation Research Proposal is finalized and approved by the Learner's Dissertation Committee and the University's Ethics Committee. All steps necessary to begin data collection, including any necessary pilot testing, are completed. Candidates for the Ph.D. must maintain continuous enrollment. Satisfactory/Unsatisfactory grade only.

Doctoral Dissertation Research ll

2

Dissertation data are collected and analyzed. Candidates for the Ph.D. must maintain continuous enrollment. Satisfactory/Unsatisfactory grade only.

Doctoral Dissertation Research lll

2

the dissertation process is completed. The manuscript is prepared, accepted by the Learner's Dissertation Committee, and the oral defense is conducted. Candidates for the Ph.D. must maintain continuous enrollment. Candidates must have satisfied all financial obligations to the University and be enrolled at the time the oral defense is conducted. Satisfactory/Unsatisfactory grade only.