Director:
| Touma, Jihad (Physics, FAS) |
| Executive Committee Members: | Araman, Victor (Decision Science, OSB) Doummar, Joanna (Geology, FAS) Issa, Ibrahim (Electrical and Computer Engineering, MSFEA) Monni, Stefano (Mathematics, FAS) Mouawad, Amer A. (Computer Science, FAS) Najem, Sara A. (Physics, FAS) Taati, Siamak (Mathematics, FAS)
|
Computational Science is a thriving field of study at the interface of computer science, mathematics and statistics, the natural sciences, engineering, and financial engineering. Practitioners of the art develop mathematical models, construct and optimize numerical algorithms, then deploy them on increasingly powerful computers to address real-life problems in
fields where quantitative/compute-intensive modeling and simulation are essential to optimal
design, predictive analytics, and inference.
The Graduate Program in Computational Science (GPCS) at AUB is open to students with a
background in computer science, applied mathematics and statistics, the natural sciences,
engineering, economics, or business who wish to further their undergraduate experience
with computers and computing via intensive, hands-on study, development, and application
of state-of-the-art numerical algorithms.
Having completed core courses in the program, a student will then follow a sequence of
elective courses, then formulate and tackle problems in computationally-intensive fields currently explored at AUB (e.g., data analytics, computational theory, bioinformatics, biostatistics, computational biosciences, physics, astrophysics, hydrogeology, continuum mechanics,
optimization, operations research, and risk analysis).
The program prepares its students for an academic adventure in applied mathematics, computational sciences and related fields, as well as a career in industries or research centers
where numerical modeling, simulation, design, and/or optimization are key
Admission Requirements
Admission to the master’s program in computational science follows basic AUB regulations, and will be ultimately based on interview. To be considered, applicants to the program should either: 1) be holders of a bachelor’s degree in the natural sciences, business, computer science, economics, engineering, or mathematics; have successfully completed the equivalent of CMPS 201, MATH 201, MATH 202, MATH 218 or 219; and have acquired proficiency in discrete mathematics, numerical analysis and statistics, at a level equivalent to MATH/CMPS 211, MATH/CMPS 251, and STAT 230 (233), respectively; or 2) be holders of a bachelor’s degree, and have completed the equivalent of MATH 202, STAT 230 and of the FAS core course requirements for a minor in computational science. Some candidates may be admitted as prospective students until full completion of the required undergraduate courses.
Graduate assistantships (GFAP) are available for some applicants to the program based on qualifications.
Graduation Requirements
- 9 credits consisting of three core courses in advanced numerical methods, optimization and data science, to be selected from the following three baskets respectively:
1. Advanced Numerical Methods (3 credits)
A. General
- MATH/CMPS 350 Discrete Models for Differential Equations
- MATH/STAT 348 Monte Carlo Methods
B. Discipline Specific
- BIOL 370 Bioinformatics
- PHYS 310A Computational Physics
- MECH 663 Computational fluid dynamics
2. Optimization (3 credits)
- MATH/CMPS 351 Optimization and Nonlinear Problems
- ENMG 616 Advanced Optimization Techniques
3. Data Science (3 credits)
- CMPS 364 Advanced Machine Learning
- EECE 633 Data Mining
- EECE 664M Introduction to Machine Learning
- EECE 667 Pattern Recognition
- EECE 693 Neural Networks
- 12 credits of electives which the student would typically select within one of the program’s approved tracks: Data Analytics, Computational Theory, Bioinformatics, Biostatistics, Computational Biosciences, Computational (Astro)-Physics, Hydrogeology, Continuum Mechanics, Optimization, Operations Research, or Risk Analysis. Alternatively, students can, in coordination with their advisor, and with the approval of the GPCS committee, define a 12-credit track that best suits their background and research interests.
- A 9-credit thesis (CMTS 399) in which qualified students demonstrate the abilityof constructing, implementing and/or proficiently using computational tools toaddress problems in their chosen track.
Courses that count towards credit in Computational Science:
Optimization
Data Analytics