Doctor of Philosophy in Scientific Computing
- Master’s degree, normally in an engineering or science related field with an overall graduate GPA of at least 3.25 (on a 4.0 scale), or a Bachelor’s degree, normally in an engineering or science related field with an overall undergraduate GPA of at least 3.00 (on a 4.0 scale) and the Graduate Record Examination General Test.
- Expertise in a high level language and a basic knowledge of data structures.
- Basic knowledge of formal languages, automata, and computability.
- Basic knowledge of computer architecture or operating systems.
- Basic knowledge of calculus, statistics, and linear algebra.
- Satisfy the School of Graduate Studies’ English Language Proficiency requirements as listed in the Graduate Academic Information section.
The department recognizes that the prerequisite expertise identified above may be acquired in several ways. Students who do not meet all of the requirements may be admitted to qualified status with the obligation of meeting the remaining requirements early in their graduate study.
Students seeking the Doctor of Philosophy in Scientific Computing degree must satisfy all general requirements set forth by the School of Graduate Studies. In addition, they must meet the following requirements set by the Computer Science Department:
1. All students are required to obtain interdisciplinary graduate training. This requirement may be met by:
a. Either taking two course clusters from the computational category and one course cluster from an applications category
b. Or taking three course clusters from the computational category and conducting dissertation research in an applications category in the applicable department.
2. Course clusters must be approved by the student's Faculty Advisory Committee.
3. Students may, with approval of the Computer Science Department's Graduate Committee, design their own applications category cluster.
4. The student's Faculty Advisory Committee must include one member from the applicable applications cluster or dissertation research.
5. The Computer Science Department's Graduate Committee must approve the Faculty Advisory Committee membership.
6. Students who have a degree in a field other than Computer Science are not required to obtain interdisciplinary graduate training. These students are required to take three computational category course clusters. In addition, the student's Faculty Advisory Committee will comprise only from Computer Science faculty.
7. Students with approved Bachelor degree:
a. Must complete 51-66 credit hours of coursework;
b. Must complete eight of the core courses.
8. Students with approved Master degree:
a. Must complete 27-39 credit hours of coursework;
b. Must complete four of the core courses.
9. Elective courses: CSci 500 and CSci 566 may not be used as electives. Only 3 credits of CSci 591 may be used as an elective.
10. Successful completion of written Graduate Qualifying Examination (GQE). The GQE's passing cut off point will be higher than the GQE's passing for Master Students (MS) taking the same exam. The GQE will consist of questions on each of the four areas. Moreover, the PhD students are required to complete GQE's requirement within the first 4 semesters, but are strongly encouraged to complete the GQE earlier in their studies.
11. Successful completion of Graduate Comprehensive Exam (GCE).
12. Completion of CSci 599 Dissertation research (9-21 credits).
13. Completion of CSci 999 Dissertation (12 credits maximum).
14. Final oral examination, which includes a defense of the dissertation.
|CSCI 513||Advanced Database Systems||3|
|CSCI 522||Theoretical Foundations of Computer Science||3|
|CSCI 532||High Performance Computing and Paradigms||3|
|CSCI 543||Advanced Artificial Intelligence||3|
|CSCI 551||Security for Cloud Computing||3|
|CSCI 555||Computer Networks||3|
|CSCI 565||Advanced Software Engineering||3|
|CSCI 575||Analysis of Algorithms||3|
The computing clusters contain related courses that provide depth of knowledge in specialized computing systems or methods.
- Software Engineering Cluster: Software engineering combines the ideas from engineering, management, and math disciplines in order to improve our ability to build complex software systems on time and within the budget. Requires any three of the following courses:
CSCI 463 Software Engineering 3 CSCI 562 Formal Specification Methods 3 CSCI 565 Advanced Software Engineering 3 CSCI 582 Software Architecture 3
- Data Management Cluster: The cluster enhances a student’s knowledge in data engineering and management. It includes the study of database systems, data management, data mining and data warehousing, digital libraries and information retrieval and systems.
Requires the following three courses:
CSCI 455 Database Management Systems 3 CSCI 513 Advanced Database Systems 3 CSCI 515 Data Engineering and Management 3
- Artificial/Computational Intelligence Cluster: The goal of this track is to provide the student with both classical and advanced topics in artificial and computational intelligence. It includes the study of problem solving methods, approximate reasoning, machine learning, decision making, data mining and other application techniques. Requires the following three courses:
CSCI 543 Advanced Artificial Intelligence 3 CSCI 544 Soft Computing: Computational Intelligence I 3 CSCI 554 Applications in AI/Computational Intelligence 3
- Distributed Systems Cluster: The goal for this track is to provide the student with an understanding of the hardware technologies (hardware, network, and storage devices) required to develop a machine suitable for high performance computing. Requires the following three courses:
CSCI 427 Cloud Computing 3 CSCI 551 Security for Cloud Computing 3 CSCI 555 Computer Networks 3
- High Performance Computing Cluster: The cluster provides an understanding of the system architecture (hardware, network, and storage devices) and the software technologies (MPI, PVM, and Java) required to create a system capable of high performance computing. Requires the following three courses:
CSCI 451 Operating Systems I 3 CSCI 532 High Performance Computing and Paradigms 3 CSCI 575 Analysis of Algorithms 3
- Graphics and Visualization Cluster: The goal of this track is for the student to master the OpenGL graphics library, to develop a working understanding of signal and image processing techniques, and to be able to apply those skills to the visualization of results generated by complex computer simulations. Requires the following three courses:
CSCI 446 Computer Graphics I 3 CSCI 448 Computer Graphics II 3 CSCI 547 Scientific Visualization 3
- Modeling and Simulation Cluster: In this cluster the student will study the various techniques for developing mathematical models and software simulations to predict the behavior of complex physical phenomena. Requires the following three courses:
MATH 460 Mathematical Modeling 3 CSCI 445 Mathematical Modeling and Simulation 3 CSCI 545 Discrete Dynamical Systems Modeling and Simulation 3
The application clusters provide exposure to specific scientific disciplines that commonly make use of scientific computing methods. In addition to the clusters listed here, other clusters may be defined by the Faculty Advisory Committee with approval of the Computer Science Department’s Graduate Committee.
- Computational Mathematics Cluster: This cluster provides an understanding of the computational methods used to solve complex mathematical problems on a digital computer. Requires three graduate level mathematics courses. Possible courses are:
MATH 461 Numerical Analysis 3
- Computational Chemistry Cluster: This cluster provides an understanding of the mathematical tools used to solve several major classes of problems in modern theoretical chemistry on a digital computer. Requires three graduate level chemistry courses. Possible courses include:
CHEM 470 3 CHEM 471 Quantum Mechanics & Spectroscopy 3 CHEM 530 Chemical Thermodynamics 3 CHEM 534 Quantum and Computational Chemistry 3
- Computational Physics Cluster: This cluster provides an understanding of the mathematical tools used to solve current problems in modern physics on a digital computer. Requires the following courses:
PHYS 402 Computers in Physics 3 PHYS 509 Methods of Theoretical Physics 3 Select one of the following: 3 Introduction to Astrophysics Introduction to Astrophysics II Methods of Theoretical Physics Solid State Physics Solid State Physics II Quantum Mechanics Quantum Mechanics Theory Electricity Magnetism Theory of Electricity and Magnetism Statistical Physics Analytical Mechanics
- Atmospheric Sciences Cluster: This cluster provides an understanding of the mathematical tools used to solve several major classes of problems in modern atmospheric sciences on a digital computer. Requires the following courses:
ATSC 505 Advanced Atmospheric Dynamics 3 ATSC 530 Numerical Weather Prediction 3 Select one of the following: 3 Atmospheric Data Analysis Measurement Systems Statistical Methods in Atmospheric Science Advanced Surface Transportation Weather Current/Special Topics in Meteorology