CENG Ph.D. Qualifying Exam Guidelines

Computer Engineering Ph.D. Qualifying Exam Guidelines

The following guidelines are set for the Ph.D. Qualifying Examination in Computer Engineering, in addition to rules and regulations at Middle East Technical University Student Handbook.

General Information

  • Ph.D. Qualifying exam consists of a written part and an oral part. The candidate is considered successful when he/she passes both parts.
  • Ph.D. Qualifying exam is given twice a year each May and November.
  • The candidate should get the approval of his/her advisor and petition the department at least one month before the exam. The student is required to submit the one-page abstract of her/his paper for the oral (area) exam as an attachment to her/his petition for taking the area exam.
  • The candidate failing to pass the Ph.D. Qualifying exam is given a second chance in the subsequent offering of the exam. Failure in the second attempt leads to the dismissal of the student from the Ph.D. program.

Ph.D Qualifying Exam

The exam consists of two parts; Written (Core) exam and Oral (Area) exam.

Written (Core) Exam

This part of the exam covers the following 7 main topics:

  • Data Structures (CENG 213),
  • Algorithms (CENG 315),
  • Discrete Math (CENG 223),
  • Theory of Computation (CENG 280),
  • Programming Languages (CENG 242),
  • Operating Systems (CENG 334),
  • Digital Design and Computer Architecture (CENG 232 & CENG331)

The questions from the Core part will be at the undergraduate level and will cover the content that is listed in the Core subjects table below. In the exam, there will be two questions from each topic, and the student will be asked to attempt only one question from each. Each subject is graded over 20 points and the student is considered successful if her/his total grade is 84 out of 140 (which corresponds to 60% of the maximum grade). In addition, if the student is unsuccessful in the core exam, s/he will be exempt from the subjects on which s/he scored at least 14 out of 20. In the subsequent exam, s/he will be expected to score 60% of the maximum grade from the remaining subjects only. The student reserves the right to “not be exempt” if s/he wishes. The exemption status from a course is valid only for the next exam session.

Oral (Area) Exam

The Purpose of the Exam is to evaluate the student’s ability and potential to conduct research at the doctoral level and to encourage the student to have an earlier involvement in research.

Expectations from the Student before the Exam

  • To choose a topic within the student’s research field.
  • To make a contribution to this topic as explained below.
  • To prepare a paper in the “IEEE conference proceedings format” in at least 6 at most 8 pages.
  • An Originality Report for the paper should be prepared and signed by the thesis supervisor. Similarity with the work of others shouldn’t exceed 20%. Originality Report can be prepared using Turnitin (http://www.turnitin.com) or Ithenticate (http://www.ithenticate.com) plagirism check software. It is enough to print the first page of the similarity index for the originality report.
  • To submit one page abstract of this paper as an attachment to her/his petition for taking the area exam.
  • To submit this paper along with the Originality Report to the examination committee (jury) at least 10 days before the first day of the oral (area) exam period.

Expectations from the Student during the Exam

  • To present the student’s study ( at most 20 minutes)
  • Answer questions about the study (10 minutes)
  • Answer general questions in the Ph.D. field of the student (20 minutes, if the jury finds it necessary the question answer part can be extended).

Expected Contribution The student can choose to make one or more of the following types of contributions:

  • Literature evaluation: A literature survey is required in every study, but those students who select this category will be expected to conduct a more detailed literature survey by identifying the advantages and disadvantages of the previous studies, compare them with each other, and provide an analysis-synthesis of the literature in the selected topic.
  • Implementation: The student will implement a paper in the selected topic which should be chosen together with the student’s advisor and produce results by changing various parameter values if applicable.
  • Novel approach: The student will propose a novel approach to a selected problem and implement this approach to produce results. This category includes improving an existing algorithm using a different approach.
  • Comparison: The student will compare two or more algorithms that are selected with the student’s advisor, and discuss the results obtained as a result of this comparison.
  • Theoretical contribution: The student will propose a novel theoretical approach such as a formula, theory, proof, etc. and show the correctness, utility, and reasoning behind this approach.
  • Case study: The student will apply an existing method or process to a realistic problem and discuss the results that are obtained.

Grading The performance of the student is assessed separately by each jury member according to the table below. A grade of 60 or above is  considered as a "pass" vote by that jury member. If at least three  jury members vote "pass" the student is considered successful, otherwise the student fails the exam.


Point [0-100]


 Written work


 Presentation and questions related to the presentation


 General questions in the selected field

 Weighted Total:


  General Principles about the Administration of the Exam

  • For every student who will take this exam, a jury comprised of 5 people with Ph.D. degree and experts in the selected field will be formed by the qualifying exam committee. This jury will include the student’s advisor. It is crucial that the jury members read the study before the exam and prepare questions to be asked during the exam.
  • Qualification Exam Committee designates one of the jury members as the chair.  The chair is responsible for conducting the examination process.
  • The written work prepared by the student must be original. It should not be put together by copying and pasting from the previous work.
  • Jury members could check the document submitted by the student against plagiarism using http://www.ithenticate.com/ to which METU has a subscription.
  • A study prepared for the master’s thesis cannot be directly used for his exam. A contribution is expected to be made during the Ph.D. studies even if the topic remains the same.
  • An article (published or unpublished) written by the student as the primary author can be used for this exam. However, the same article cannot be used by more than one student, and if it was published it should not be published more than 12 months before the exam.
  • In case of failure for the first time, a new topic can be chosen or the jury must indicate the expectations from the student for the second exam if the same topic will be used.
  • In case the student is taking this exam for the second time from the same topic, a supporting document explaining the changes from the previous version must be provided by the student to the jury along with the latest version of the study.

Written (Core) Exam Syllabus




Data Structures

Algorithm analysis for data structures

* Mark Allen Weiss, Data Structures and Algorithm Analysis in C++ (3rd ed.), Addison Wesley, 2006

Lists, stacks, queues


Priority queues



Analysis of Algorithms

* Introduction to Algorithms, T. H. Cormen, C. E. Lieserson, R. L. Rivest, C. Stein, Mc Graw-Gill

Sorting, Searching

String Processing

Graph Algorithms

Greedy Approach

Divide and Conquer Algorithms

Dynamic Programming

Exhaustive Search

Complexity Classes, NP-completeness

Discrete Mathematics

Propositional Logic: Logic, Equivalences

* K.H. Rosen, Discrete Mathematics and its Applications, (Sixth Edition) McGraw-Hill, 2007. 
* W.K. Grassmann and J.P. Tremblay, Logic and Discrete Mathematics: A Computer Science Perspective, Prentice Hall, 1996

Predicate Logic: Predicates and Quantifiers, Nested Quantifiers, Methods of Proof

Sets and Functions: Sets, Set Operations, Functions, Growth of Functions, Complexity of Algorithms

Integers: Integers and Division, Integers and Algorithms

Induction and Recursion: Sequences and Summations, Mathematical Induction, Recursive Definitions and Structural Induction, Recursive Algorithms

Counting: Permutations and Combinations, Recurrence Relations, Solving Recurrence Relations, Generating Functions, Inclusion and Exclusion

Relations: Relations and Their Properties, Representing Relations, Closure of Relations, Equivalence Relations, Partial Orderings

Graphs: Int to Graphs, Graph Terminology, Representing Graphs, Connectivity, Euler and Hamiltonian Paths, Shortest Path Problem, Graph Coloring

Trees: Int to Trees, Applications of Trees, Spanning Trees, Min Spanning Trees

Theory of Computation

Finite Automata and Regular Expressions: Alphabets and languages, Finite representations of languages,Deterministic finite automata, Nondeterministic finite automata, Equivalence of DFA and NFA, Finite automata versus regular languages, Pumping lemma and its applications, State minimization

* Elements of the Theory of Computation, H.R.Lewis, C.H.Papadimitriou, (2nd ed.), Prentice-Hall, 1998. 
* Introduction to the Theory of Computation, M.Sipser, Course Technology, 2005.

Push-down Automata and Context Free Grammars: Parse trees and derivations,Pushdown automata, Pushdown automata versus context-free grammars, Closure properties,Pumping theorem and its applications, Deterministic PDAs

Regularity and context-freeness of languages

Turing Machines and unrestricted grammars: Turing machines – definition and examples, Computing with TMs, Recursive and recursively enumerable languages, Extensions of TMs, Nondeterministic TMs, Unrestricted grammars

Church-Turing thesis, universal Turing machines

Halting problem

Programming Languages

Storage structures, control structures, scope and binding

* Programming Language Concepts and Paradigms, D.A. Watt, Prentice-Hall, 1990. 
* Programming Languages: Concepts and Constructs, R. Sethi, Addison Wesley, 1996.

Data and procedural abstraction

Type systems

Lexical and syntactic description of languages

Object-oriented programming languages

Functional programming languages

Logic programming languages

Operating Systems

Operating Systems Structures

* Modern Operating Systems, A.S. Tanenbaum, Prentice-Hall, ISBN 0-13-595752-4, 1992. 
* Operating System Concepts, A. Silberschatz, P.B. Galvin, (4th ed.), Addison-Wesley, ISBN 0-201-50480-4, 1994. 
* Design and Implementation of the 4.3BSD Operating System, S.J. Leffler, M.K. McKusick, M.J. Karels, J.S. Quarterman, Addison-Wesley, ISBN 0-201-06196-1, 1989.

Processes, Threads and Their Management

Process and Processor Scheduling

Process Synchronization

Interprocess Communication


Memory Management

Storage Management (I/O Processing, File Systems)

Protection and Security

Digital Design and Computer Architecture

Combinational Circuits

* Digital Design, M. Mano, Prentice-Hall, ISBN 0-13-212994-9, 1991. 
* Computer Organization, C. Hamacher, Z.G. Vranesic, S. Zaky, (4th ed.) McGraw-Hill, ISBN 0-07-114323-8, 1996. 
* Computer Organization and Design, D.A. Patterson, J.L. Hennessy, (2nd ed.), Morgan-Kaufmann, ISBN l-55860-491-X, 1998. 
* Computer Systems: A Programmer’s Perspective by Randal E. Bryant and David R. O’Hallaron Prentice Hall, 2003

Combinational Circuit Minimization: Algebraic and Karnaugh-map minimization

Synchronous Sequential Circuits

Registers, Counters


Arithmetic Logic Unit, Multiplication and Division, Floating Point operations

Pipelining: Hazards, Forwarding, Branch Prediction

Memory Hierarchy: Interleaving, Cache Memory, Virtual Memory

I/O Systems: Buses, I/O Interfaces, Interrupts, DMA

Oral (Area) Exam Syllabus




Artificial Intelligence

Uninformed and Heuristic Search

* Artificial Intelligence: A Modern Approach, S.Russell, P.Norvig, Prentice Hall, 1995. 
* Logical Foundations of Artificial Intelligence, M.R.Genesereth, N.Nilsson, Morgan Kaufmann, 1988.

Game Playing

Constraint Satisfaction and Propagation

Knowledge and Reasoning

Theorem Proving


Reasoning with Uncertainty

Machine Learning: Learning from examples (supervised learning, decision trees, Regression and classification, ANN, SVM), Learning probabilistic models (Bayesian learning, Naive Bayes classifiers, EM algorithm), Reinforcement Learning (passive RL, active RL)

Computer Graphics

Rendering Pipeline: Major stages of the rendering pipeline

* Computer Graphics: Principles and Practice, Foley, Van Dam, Feiner, Hughes, (2nd ed.), Addison Wesley, 1995. 
* Computer Graphics, Hearn, Baker,(2nd ed.), Prentice Hall, 1994. 
* Fundamentals of Computer Graphics, Shirley and Marschner, (3rd ed.), AK Peters, 2009. 
* Realistic Ray Tracing, Shirley and Morley, (2nd ed.), AK Peters, 2003.

Geometric Transformations: Homogeneous coordinates, Vectors, points, normals, Translation, scaling, rotation, sheer transformations (2D and 3D)

Raster Algorithms: Line rasterization, Triangle rasterization, Antialiasing

Viewing: Parallel projections, Perspective projections, Clipping, Viewport transformation

Visible Surface Detection: Back-face elimination, Z-buffer algorithm

Phong Shading Model: Ambient Light, Diffuse Reflection, Specular Reflection

Polygonal Surface Shading: Flat shading, Goraud shading, Phong shading

Texturing: Generating of uv coordinates (for both 2D and 3D texture mapping), Mipmapping, Bilinear interpolation, Bump mapping

Volume Rendering: Marching cubes algorithm, Direct volume rendering

Three Dimensional Object Representations: Hermite curve, Natural cubic splines, Bezier curves and surfaces, Geometric continuities, Joining curves and surfaces

Ray tracing: Parametric lines, Parametric and implicit surfaces, Ray-object intersections (triangle, sphere, plane), Basic ray tracing algorithm, Generating simple shadows with ray tracing, Accelleration structres (bounding boxes, oct-tree, kd-tree)

Radiosity: Basic radiosity algorithm, Radiosity equation, Hemicube method for form factor calculations, Jacobi iteration and Gauss Seidel for solving Ax=b

Natural Language Processing

Linguistic knowledge representation and propagation

* Speech and Language Processing, Jurafsky and Martin, Prentice-Hall, 2000. 
* Natural Language Understanding, J.Allen,2.ed,Benjamin-Cummings, 1995. 
* Prolog and Natural Language Analysis, F.C.N. Pereira, S.M. Shieber, CSLI, 1987.

Computational aspects of Morphology

Syntactic representation in NLP (phrase structure, dependency, unification)

Parsing strategies for natural languages (bottom-up,top-down, mixed)

Parsing decisions and improvements (determinism, non-determinism, charts)

Grammar formalisms (dependency grammars, categorical grammars, phrase-structure grammars) and hierarchy for natural languages

Handling non-local dependencies

Compositional semantics: Lambda-calculus and logical form

Basics of data-intensive linguistics (n-grams, language models, classifiers)

Database Systems

Physical data organization

* Database Management Systems, Raghu Ramakrishnan, McGraw-Hill. 
* Principles of Database and Knowledge-base Systems, volume 1, Ullman, Computer Science Press. 
* Database system concepts, Silberschatz & Korth, McGraw-Hill.

Data models

Relational database design theory (normalization)

Relational query languages

Integrity and security

Transaction management

Concurrency control

Recovery techniques

Query optimization

Numerical Computation

Numerical stability of algorithms and conditioning of problems

* Numerical Methods, G.Dahlquist, A.Björck, Prentice-Hall. 
* Matrix Computations, G. Golub, C.F. Van Loan, THe Johns Hopkins University Press.
* Yousef Saad, Iterative Methods for Sparse Linear Systems, SIAM.

Linear systems: Norms, matrix norms, Gaussian elimination, forward and backward substitution, pivoting, Householder’s reflection, Given’s rotations, Gram-Schmidt method, QR, Singular Value Decomposition, Linear Least Squares problems and curve fitting, Relaxation methods (Jacobi, Gauss-Seidel)

Matrix eigenvalue Problems: Power method, inverse iteration, Rayleigh Quotient, and QR iterations, Jacobi method, Arnoldi and Lanczos processes, Krylov subspace methods for solution of linear systems (GMRES, CG, BiCGStab), preconditioning

Finding roots of nonlinear equations: Bisection, Secand, Newton’s methods, fixed point iteration

Interpolation: Lagrange interpolation, Newton’s interpolation and divided differences, Runge’s phenomenon, Splines, Orthogonal polynomials

Numerical integration: Interpolatory quadrature, Composite quadrature rules

Software Engineering

Lifecycles and process models

* Software Engineering: a Practitioners Approach, R.S. Pressman, (4th ed.), McGraw-Hill. 
* Software Engineering, Sommerville, (4th ed.), Addison-Wesley

Software project management

Specification and modeling techniques

Traditional, object oriented and component based approaches

Software metrics

Software quality

Testing and integration methods


Pattern Recognition and Image Analysis

Image Transform: Discrete Fourier transform (FFT excluded), Discrete Haar Wavelet transform

* Digital Image Processing, R. C. Gonzales and R. E. Woods, Prentice-Hall, 3rd edition, 2008.
* Pattern Classification, R.O. Duda, P. E. Hart and D. G. Stork, Wiley-Interscience, 2nd edition, 2000. 
* Computer Vision, L. G. Shapiro and G. C. Stockman, Prentice Hall, 2001.

Image Enhancement Techniques: Point Processing (basic intensity transformations), Histogram processing, Image negation, power law, log transformations, Spatial Filtering, Convolution (smoothing, sharpening)

Image Compression: Redundancy and measuring image information, Huffman coding

Morphological Operations: Erosion, dilation, opening, closing

Image Segmentation: Edge detection (Canny, Hough transform), Thresholding

Image Representation and Description: Chain codes, Polygons, Regional descriptors

Texture: Texel-based Texture Descriptions, Quantitative Texture Measures

Content-based image retrieval: Image Distance Measures (Color, Texture and Shape Similarity Measures), Precision, Recall and F-score Performance Analysis

Motion from 2D images: Image Subtraction

Stereo Vision: Matching: Cross Correlation, Symbolic Matching, The Epipolar and The Ordering Constraints

Bayesian Decision Theory: Gaussian Density Estimation, Classifier Discriminant Functions

Maximum Likelihood Method: Gaussian Density Estimation

Non-parametric techniques: Parzen Window, K-Nearest Neighbor

Unsupervised learning: Mixture Resolving, Unsupervised Bayes Method, Maximum Likelihood Method

Clustering: K-means Clustering, Hierarchical Clustering, Component Analysis


Learning and generalization

* Neural Computing: Theory and Practice, P.D. Wasserman. 
* Introduction to the Theory of Neural Computation, J. Hertz, A. Krogh, and R.G. Palmer, Addison-Wesley, 1991. 
* Neural Networks: A Comprehensive Foundation, S. Haykin, Macmillan, 1994.

Multilayer perceptrons and the backpropagation algorithm

Hopfield model

Recurrent networks

Unsupervised learning and self organizing maps

Adaptive resonance theory

Radial basis function networks

Higher order neural networks


Parallel Computing

Parallelism and classification of parallel computers: Performance bottlenecks, Classification of parallel computers and applications, Programming models for parallel computers

* Introduction to Parallel Computing, by Grama, Gupta, Kumar, and Karypis, Addison Wesley, 2003. 
* Parallel Programming for Multicore and Cluster Systems, Rauber and Runger, Springer Verlag, 2010. 
* Sourcebook of parallel computing, Jack Dongarra, et.al. Kaufmann, 2002.

Pipelining and vector processing: Instruction pipelining, superscalar execution, and instruction scheduling, Pipelining arithmetic operations, Performance analysis of pipelined operations

Interconnection topologies and implementing various communication operations: Metrics for evaluating performance of interconnection networks, Point to point and collective communication operations and their implementation

Task decomposition and design of parallel algorithms: Principles of parallel algorithm design, Task interaction and dependency graphs, Graph partitioning/clustering, Load balancing

Analysis of parallel algorithms: Speed improvement and efficiency, Amdhal’s law, Gustafson’s law, Weak and strong scalability

Parallelism in various applications (e.g. matrix problems in scientific applications, sorting and searching, etc.)

Distributed Systems

Time Synchronization

* Distributed Systems: Principles and Paradigms, 2nd edition, A.S. Tanenbaum, M. Van Steen, Pearson Higher Education, 2007. 
* Distributed Systems: Concepts and Design 4th edition, J. Dollimore, T. Kindberg, G. Coulouris, Addison-Wesley, 2006. 
* Principles of Concurrent and Distributed Programming, 2nd edition, B. Ben-Ari, Addison-Wesley, 2006.


Structuring Distributed Systems

Process Interaction and Group Communication

Distributed File Systems

Concurrency Control

Distributed Shared Memory

Basics of Fault-Tolerance and Real-Time Systems

Programming Languages and Compilers (Advanced)

Typed lambda calculus

* Foundations for Programming Languages, (first six chapters) J.C. Mitchell, MIT Press, 1996. 
* Compilers: Principles, Techniques, and Tools, A.V. Aho, R. Sethi, J.D. Ullman, Addison-Wesley, 1986.

Semantic specification of languages: Operational, denotational and axiomatic approaches

Algebraic specification of data types

Partial correctness proofs with before and after assertions

Lexical and syntactic analysis of languages

Syntax-directed translation, attribute grammars

Abstract machines, intermediate languages

Code generation

Networked Systems

The principles and techniques employed in computer and wireless networks; the seven-layer protocol suite known as ISO model

* Computer Networking: A top down approach, 6th Ed., J.F. Kurose, K.W. Ross, Addison-Wesley, 2012. 
* Computer Networks, 5th Ed., A.S. Tanenbaum, Prentice Hall, 2011. 
* Cryptography and Network Security: Principles and Practice, 5th Ed., W. Stallings, Prentice Hall, 2011.

Data link layer issues (medium access control, reliable data transfer)

Network layer issues (packet- versus circuit-switching, routing algorithms, IP, QoS)

Transport layer issues (error control, flow control, congestion control, end-to-end argument, TCP, UDP)

Network programming (socket interface)

Performance evaluation of computer networks

Security of computer networks (confidentiality, integrity, and authentication)

Wireless networks (Cellular networks, mobility management, WLAN)


Sequence analysis, next generation sequencing: Genome annotation, Computational evolutionary biology, Comparative genomics, Genetics of disease, Analysis of mutations in cancer

* Understanding Bioinformatics, M. Zvelebil and J.O. Baum, Garland Science, 2008. 
* Bioinformatics: the machine learning approach, 2nd Ed., Baldi and S. Brunak, MIT press, 2001. 
* Principles of Computational Cell Biology, V. Helms, Wiley-Blackwell, 2008.

Gene and protein expression, gene regulation

Structural bioinformatics: Protein folding problem, prediction of secondary/tertiary structure, Structural alignment, Multiple structural alignment, Protein docking

Functional classification of proteins, human genome annotation

Statistical modeling of biological data

Biological Text Mining

Bioimage Informatics: High-throughput image analysis

Biological networks and computational systems biology