Computational Science is a discipline dedicated to the coding of algorithms for the solution of mathematical equations that describe scientific problems over a wide variety of fields. Its aim is to obtain new scientific information and technical practices. The phenomena approached by Computational Science can range from modeling blood flow and the interaction of microorganisms, to geophysical events, biomolecular processes, turbulence in the motion of liquids and gases, the trajectory of spacecraft, statistics, epidemiology, and more.

Investigators with computational science expertise provide ways of approaching important problems that complement and improve experimental and theoretical approaches by performing simulations outside the ranges of what may be viable by other methods. In this way, computational science informs other studies, generating synergy with scientific and engineering disciplines.

MS in Computational Science

• The Masters degree program in Computational Science was created in 2010 as a 4+1 program to provide students in the sciences and engineering with the opportunity to gain practical computational experience in their discipline and participate in a research project, thus making the students more competitive for jobs.  In May of 2012, the program was approved also as a Masters degree for students outside Tulane.
• The MS program is administered by the Center for Computational Science (CCS), which is not an academic department but a research unit that provides an infrastructure for investigators interested in computational science to exchange ideas, produce research and establish new collaborations.

Learning Objectives

1.     Students will gain practical experience solving scientific, engineering, or mathematical problems using computational methods.

2.     Students will produce clear and well-written reports.

3.     Students are prepared for further studies or a career that uses computational science.

Prerequisites: Students are expected to have taken an undergraduate Scientific Computing course (e.g. MATH 3310/6310). With approval by the CCS Director, some students may count a different introductory computation course as a substitute prerequisite.

Eligibility: To enter the MS program the students must have a Bachelor's degree in a scientific discipline.  To enter the 4+1 program the students must be on schedule to complete the requirements of their major by their senior year. The B.S. requirements will guarantee that the student has a substantial background in the application area. Every student in the program must write a thesis.

Advising: Every student in the 4+1 program is assigned two advisors: one from the student’s major department and one from among the faculty involved in CCS projects. This gives the student the opportunity to choose a computational project in his/her major discipline whose significance and progress can be evaluated by one advisor while the second advisor guides the computational aspects of the work.

The thesis: must be approved by the thesis committee consisting of the two advisors. The thesis topics are typically related to the student’s undergraduate major. The student, with input from his/her computational advisor, selects the application discipline advisor.  All MS students are encouraged to join a CCS research team and to attend CCS seminars and computational workshops.

Financial assistance for MS students is in the form of a partial tuition waiver. For current discount rates, please refer to the School of Science and Engineering.

Program Requirements

The Master's degree consists of 30 credit hours: 12 from required courses, 6 from courses from group A, 6 from Group B, and 6 from Masters thesis research.

Required Courses

COSC 6000 C++ Prog For Sci & Engr (3 c.h.)

COSC 6200 Large Scale Computation (3 c.h.)

MATH 7570 Scientific Computation II (3 c.h.)

MATH 7580 Scientific Computing III (3 c.h.)

MATH 9980 Masters Research (3 c.h.)

MATH 9980 Masters Research (3 c.h.)

Two courses from group A (Theory and Applications) and two courses from group B (Computation)

Group A

MATH 6470 Analy Methods Appl Math (3 c.h.)       

 MATH 7310 Applied Mathematics I (3 c.h.)

MATH 7320 Applied Math II (3 c.h.)              

BMEN 6420 Transport in Cells and Organs (3 c.h.)

BMEN 6330 Advanced Biofluid Mech (3 c.h.)

CENG 6770 Advances In Biotechnolog (3 c.h.)

CHEM 7120 Statistical Mechanics (3 c.h.)

NSCI 7110 Graduate Neuroscience I (3 c.h.)

PHYS 7170 Quantum Mechanics I (3 c.h.)

GROUP B

MATH 7740 Topics In Computation (3 c.h.)

CHEM 7140 Computational Quantum Chemistry (3 c.h.)

MATH 7360 Data Analysis (3 c.h.)

CMPS 6130 Intro Comp Geom (3 c.h.)

CMPS 6140 Intro Artificial Intelligence (3 c.h.)

CMPS 6160 Introduction to Data Science (3 c.h.)

CMPS 6210 Algs Comp Struct Bio (3 c.h.)

CMPS 6340 Introduction to Deep Learning (3 c.h.)

CMPS 6360 Data Visualization (3 c.h.)

CMPS 6620 Artificial Intelligence (3 c.h.)

CMPS 6630 Computational Bio & Bioinform (3 c.h.)

CMPS 6640 Advanced Computational Geometry (3 c.h.)

For more information, contact Ricardo Cortez via email or the School of Science and Engineering.