M.S. and Ph.D. in Computational Mathematical and Statistical Sciences

Program Description

The Department of Mathematical and Statistical Sciences offers both M.S. and Ph.D. study in Computational Mathematical and Statistical Sciences. Computational mathematical and statistical sciences is a field of study that emphasizes the discovery, implementation, and use of computational tools to solve problems in mathematics and statistics that are both applied and pure. Commonly, these efforts involve the development of new computational methodologies, computer software, or computer systems to solve large-scale problems.

Our program is designed to equip graduates with a distinctive blend of theoretical and computational skills, for employment in industry, research laboratories and institutions of higher education. While the bulk of their coursework will be undertaken in this Department, their research topics may range across the computational aspects of a broad spectrum of disciplines.

A relatively small number of programs with the same overall goals exist in this country. Most have a similar title, although their focus varies, quite naturally, according to the resources with which they are blessed. Nonetheless, each offers students the opportunity to work within an interdisciplinary setting wherein the ultimate goal is the solution of a scientific problem using state-of-the-art computational techniques.

Students that excel in the M.S. program that decide to apply for Ph.D. program and are accepted may seamlessly enter the doctoral studies since both share the same core courses. Our doctoral program is designed for individuals of outstanding ability who show promise as a researcher in an interdisciplinary environment. The diverse research opportunities in our naturally interdisciplinary department are enhanced by the research programs of associated faculty on the Marquette campus in the sciences and engineering and Milwaukee area research laboratories and clinics. For a listing of department research and research laboratories, please consult the Faculty section of this web site.

What is Computational Mathematical and Statistical Sciences?

Computational mathematical and statistical sciences (CMPS) is a field of study that emphasizes the discovery, implementation, and use of computational tools to solve problems in mathematics and statistics that are both applied and pure. For instance, in the NSF Program Description for Computational Mathematics, this program "supports mathematical research in areas of science where computing plays a central and essential role, emphasizing algorithms, numerical methods, and symbolic methods. The prominence of computation in the research is a hallmark of the program."

A brief history

Since the advent of the computer, its potential to advance scientific investigation has been recognized. From the design and application of early computers to aid in the cracking of codes during WWII and the simulations leading to the hydrogen bomb in 1952, the use of computers and the development of algorithms and systems in the service of science, “computational science,” was born. Since that time, the computer has become an indispensable appliance of business and an essential facet of science. From the statistical analysis of the mutations of HIV to the simulations describing the evolution of the universe or the flow of traffic on the freeway; from information extracted in a meaningful way from large databases to the transfer of money when an ATM card is used, the computer, sophisticated algorithms, large networks and databases are central to our life – and in science, essential. In science, as problems become more complicated and datasets larger, the techniques needed to design the experiments and make sense of it all requires both the computer and algorithms for the development of the necessary systems, simulations, and analysis. The activity which enables this accomplishment – using the computer as a research tool – is the field of computational science. It clearly involves mathematics, applied mathematics, computer science, and statistics, along with an understanding of the science of the applications. This activity involves original thought and discovery, the ability to design and use tools and systems, and a facility for communicating with those from other fields.

The need for these programs

The need for these programs, as part of STEM (Science, Technology, Engineering and Mathematics) education has been recognized by several influential groups. The most powerful statement is the report from the President’s Information Technology Advisory Committee (PITAC) in 2005 entitled “Computational Science: Ensuring America’s Competitiveness.” In this report, the Principal Finding was:

  • Computational science is now indispensable to the solution of complex problems in every sector, from traditional science and engineering domains to such key areas as national security, public health, and economic innovation. Advances in computing and connectivity make it possible to develop computational models and capture and analyze unprecedented amounts of experimental and observational data to address problems previously deemed intractable or beyond imagination. Yet, despite the great opportunities and needs, universities and the Federal government have not effectively recognized the strategic significance of computational science in either their organizational structures or their research and educational planning. These inadequacies compromise U.S. scientific leadership, economic competitiveness, and national security.

The principal recommendation of special interest to this proposal, is:

  • Universities and the Federal government’s R&D agencies must make coordinated, fundamental, structural changes that affirm the integral role of computational science in addressing the 21st century’s most important problems, which are predominantly multidisciplinary, multi-agency, multi sector, and collaborative. To initiate the required transformation, the Federal government, in partnership with academia and industry, must also create and execute a multi-decade roadmap directing coordinated advances in computational science and its applications in science and engineering disciplines.

They continue:

  • Traditional disciplinary boundaries within academia and Federal R&D agencies severely inhibit the development of effective research and education in computational science. The paucity of incentives for longer-term multidisciplinary, multi-agency, or multi-sector efforts stifles structural innovation. To confront these issues, universities must significantly change their organizational structures to promote and reward collaborative research that invigorates and advances multidisciplinary science. They must also implement new multidisciplinary structures and organizations that provide rigorous, multifaceted educational preparation for the growing ranks of computational scientists the Nation will need to remain at the forefront of scientific discovery.

The Society for Industrial and Applied Mathematics (SIAM) asked a distinguished group to study recent developments in CSE (Computational Science and Engineering) education and to give recommendations for SIAM’s role in this important effort. The report was published in 2007. It surveyed graduate programs in computational science, M.S. and Ph.D., in the U.S. and worldwide (list in Appendix 2). Besides examining several programs in detail, they state:

  • One point we would like to emphasize in this document is that CSE is a legitimate and important academic enterprise, even if it has yet to be formally recognized as such at some institutions. Although it includes elements from computer science, applied mathematics, engineering and science, CSE focuses on the integration of knowledge and methodologies from all of these disciplines, and as such is a subject, which is distinct from any of them.

The study group recognized that common to all successful programs was a foundation in mathematics, applied mathematics, statistics and computer science and an application area involving an interdisciplinary team.

apply to the Computational Mathematical and Statistical Sciences program


Program Resources

Student Success Stories

Do you have a success to share with us? We'd love to hear from you. Please fill out this form and tell us about your new job, presentation, publication, or any other award or honor you've recently received. We will post your story here, on the Graduate School website and on the Marquette University Facebook and Twitter pages.

Md Kamrul Hasan - Ph.D., Computational Mathematical and Statistical Sciences

Md. Kamrul Hasan- MSSC Graduate Program Student

Md Kamrul Hasan, is an Arthur J. Schmitt Leadership Fellow at Marquette University 2018-19 and a graduate student of Mathematics, Statistics, and Computer Science (MSSC) Department. As a fourth year PhD student in Computational Sciences program of MSSC department at Marquette, his research focuses on the development of a smartphone-based point of care tool to estimate the hemoglobin level noninvasively. The study considers two different approaches. For more information on Md Kamrul Hasan, please visit his graduate school web page.

Drew Williams - Ph.D., candidate in Computational Mathematical and Statistical Sciences

Drew Williams-Math, Statistics and Computer Science PhD student

Drew presented at the 2018 Rehabilitation Engineering and Assistive Technology Society of North America in Washington D.C. on July 12-15. Her presentation received honorable mention in the student paper competition. 

 During her third year, Drew presented at the Rehabilitation Engineering and Assistive Technology Society of North America's national conference in Washington D.C. Drew presented two posters: Access Ruler: An Accessible Measurement Application for Determining Accessibility in the Built Environment and xFACT: Developing Usable Surveys for Accessibility Purposes. Access Ruler relies on using a laser ruler, in conjunction with an iOS application that obtains measurements taken by the ruler and stores them in a list. Pairing the laser ruler with the application is a single-button process. Access Ruler improves the accessibility of the process of measuring building features for accessibility, in addition to retaining accuracy and ease of use. xFACT is a desktop application designed to assist in deploying accessible, usable assessments.  xFACT can create one survey for multiple types of assessments, and tailor it to the needs of the user with very little effort on their part, thus improving the assessment-taking process for determining accessibility, and other such uses.

Piyush Saxenaena - Ph.D., Computational Mathematical and Statistical Sciences (Alumni)

Piyush Saxena Computing Graduate Student

Coming from a Civil engineering background Marquette’s was one of the very few programs that allowed me to leverage my experience as an engineer to succeed in an analytical field without excessive coursework. During my time I was exposed to some of the best research labs and industry-academia collaborations. One such collaboration with Direct Supply evolved into what became a significant part of my dissertation. That industry connection took professional development to new heights for me. I was able to do novel research while being empowered by leaders in academia and industry. The Computational Sciences program provides a sound support structure while encouraging the flexibility that drives innovation.  I currently work as a Data Scientist at Direct Supply in Milwaukee.  Direct Supply is a Senior-Living industry juggernaut. Using Deep Learning to improve the lives for American seniors, that is what the program empowered me to do!


Contact Us

For more information, please contact Dr. Daniel Rowe, the Director of Graduate Studies.