Award Abstract # 1514808
EDT: Team Training Mathematical Scientists Through Industrial Collaborations

NSF Org: DMS
Division Of Mathematical Sciences
Recipient: UNIVERSITY OF TEXAS AT DALLAS
Initial Amendment Date: May 6, 2015
Latest Amendment Date: June 9, 2017
Award Number: 1514808
Award Instrument: Continuing Grant
Program Manager: Swatee Naik
snaik@nsf.gov
 (703)292-4876
DMS
 Division Of Mathematical Sciences
MPS
 Directorate for Mathematical and Physical Sciences
Start Date: June 1, 2015
End Date: May 31, 2019 (Estimated)
Total Intended Award Amount: $598,805.00
Total Awarded Amount to Date: $598,805.00
Funds Obligated to Date: FY 2015 = $163,002.00
FY 2016 = $211,031.00

FY 2017 = $224,772.00
History of Investigator:
  • Susan Minkoff (Principal Investigator)
    sminkoff@bnl.gov
  • Yan Cao (Co-Principal Investigator)
  • luis felipe pereira (Co-Principal Investigator)
  • John Zweck (Co-Principal Investigator)
  • Yulia Gel (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Texas at Dallas
800 WEST CAMPBELL RD.
RICHARDSON
TX  US  75080-3021
(972)883-2313
Sponsor Congressional District: 24
Primary Place of Performance: University of Texas at Dallas
800 West Campbell Road
Richardson
TX  US  75080-3021
Primary Place of Performance
Congressional District:
24
Unique Entity Identifier (UEI): EJCVPNN1WFS5
Parent UEI:
NSF Program(s): WORKFORCE IN THE MATHEMAT SCI
Primary Program Source: 01001516DB NSF RESEARCH & RELATED ACTIVIT
01001617DB NSF RESEARCH & RELATED ACTIVIT

01001718DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 8549
Program Element Code(s): 733500
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.049

ABSTRACT

The goal of this project is to transform doctoral training in the mathematical sciences to include activities that provide students with marketable skills and experiences. Prior to the start of formal thesis research, students will develop mathematical and statistical approaches to tackle problems arising in other areas of science and engineering. The 16 doctoral students supported by the project will spend two semesters of their second year working on an interdisciplinary research problem posed by an external partner from industry, a government lab, or a research institution. Students will work in teams consisting of 2-3 students, one mathematics and one statistics faculty mentor, and one of our external partners. These research projects will replace the students' normal teaching assistant duties in their second year and will provide exposure to the entire research process, from starting with an open-ended problem description to obtaining final results. In the summer after this year-long research experience, the students will be well positioned to continue work at the external partners' organizations as interns, thereby gaining experience that could ultimately lead to employment opportunities after graduation. Specific objectives include: (1) Broadening opportunities for students to pursue the solution of real-world application problems through connections with industrial and interdisciplinary researchers; (2) Increasing students' confidence to tackle applied problems with which they are not familiar; (3) Increasing students' ability to communicate within research teams and to wider audiences; (4) Broadening the range of career paths for mathematical sciences Ph.D. graduates, and increasing the number and proportion of students who do internships, take industrial research positions, or take postdoctoral positions at national labs. The teams will be chosen to include some students whose dissertation topics may not involve strong modeling or computational components. We will use the EDT project as a recruiting tool to increase the number of qualified U.S. students who apply to the Ph.D. program, especially women and under-represented minorities. The web site for the project is www.utdallas.edu/EDT.

Six external partners have agreed to collaborate on this project. In consultation with the external partners, we have developed broad research problems that complement the strengths of the faculty mentors at UTD. Proposed projects include (1) uncertainty quantification for microseismic source estimation in unconventional oil and gas recovery; (2) infectious disease forecasting; (3) cone beam computerized tomography to acquire patient anatomy data for cancer radiotherapy treatments; (4) multisensor tracking of multiple moving targets for defense applications; and (5) modeling of plasma processing systems for advanced manufacturing. Following an annual kick-off event, we will hold an in-depth workshop in which students and faculty will learn fundamental theory, examples, and computational skills of direct relevance for the chosen projects that year. The remainder of the year is broken into one- to four-month time periods during which the research goals will be defined, the problem prototyped and solved, and the results disseminated to the external partner and others outside the project.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 13)
Akbarabadi, M., Borges, M., Jan, A., Pereira, F., and Piri, M. "A Bayesian Framework for the Validation of Models for Subsurface Flows: Synthetic Experiments" Computational Geosciences , v.19 , 2015 , p.1231
Ghari, A., Gel, Y.R., Lyubchich, V., Chun, Y., Uribe, D. "On Employing Multi-Resolution Weather Data in Crop Insurance" Proceedings of the SIAM International Conference on Data Mining (SDM17) Workshop on Mining Big Data in Climate and Environment , 2017
H. Akbari, A. P. Engsig-Karup, V. Ginting, F. Pereira "AA multiscale direct solver for the approximation of flows in highcontrast porous media" Journal of Computational and AppliedMathematics , 2019 DOI: 10.1016/j.cam.2019.03.028
M. Abdullah, F. Pereira, A. Rahunanthan "Convergence Analysis of MCMC Methods for Subsurface Flow Problems" The 2018 International Conference on Computational Science and Its Applications, Lecture Notes in Computer Science , v.10961 , 2018 Springer
M. Akbarabadi, M. Borges, A. Jan, F. Pereira, M. Piri "On the Validation of a Compositional Model for the Simulation of CO2 Injection Into Saline Aquifers" Transport in Porous Media , v.119 , 2017 , p.25
Nezafati, K.., Gel, Y.R., Ramirez Ramirez, L.L. "The R Package SIMID (SIMulation ofInfectious Diseases)" Proceedings of the 7th Workshop on Complex Networks (CompleNet) , 2016
Popa, J., Nezafati, K., Gel, Y.R., Zweck, J., Bobashev, G. "Catching Social Butterflies: Identifying Influential Users of an Event-Based Social Networking Service" IEEE Big Data Congress , 2017
R. Guiraldello, R. Ausas, F. Sousa, F. Pereira, G. Buscaglia "The Multiscale Robin Coupled Method for flows in porous media" Journal of Computational Physics , v.355 , 2018 , p.1
R.T. Guiraldello, R.F. Ausas, F.S. Sousa, F. Pereira, G.C.Buscaglia "Interface spaces for the Multiscale Robin Coupled Method inreservoir simulation" Mathematics and Computers in Simulation , 2018 DOI: 10.1016/j.matcom.2018.09.027
R.T. Guiraldello, R.F. Ausas, F.S. Sousa, F. Pereira, G.C.Buscaglia "The multiscale Robin coupled method for flows in porousmedia" Journal of Computational Physics , v.355 , 2018 , p.1
Stuart, G., Yang, W., Minkoff, S.E. and Pereira, F. "A two-stage Markov chain Monte Carlo method for velocity estimation and uncertainty quantification" 86th Annual International Meeting of the Society of Exploration Geophysicists , 2016 , p.3682
(Showing: 1 - 10 of 13)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

As stated in the National Academy of Sciences reports, America faces a demographic challenge with regard to its science and engineering workforce. Unlike students in some other parts of the world, American students do not always see the value in graduate study in STEM fields.  To improve recruitment of a diverse set of students into the mathematical sciences, students need to see the relevance of mathematical sciences to the world in which we currently live. Mathematical sciences are part of almost every aspect of everyday life including internet searches, medical imaging, computer animation, numerical weather predictions, business and the military operations, etc.  We all benefit from the mathematical science advances that underpin these capabilities. And yet many mathematical sciences students remain unaware of how ubiquitous mathematics and statistics are to the workings of our every day lives. 

Today, the number of academic jobs available for graduating PhD students in mathematical sciences is far below the number of PhD students seeking such employment. Students need to be aware of and to have the skills to consider other employment opportunities besides academia. However in mathematical sciences, doctoral students often tend to work on a narrowly-focused research problem, and traditionally they only interact on this problem with their thesis advisor. This model is very different from what one would encounter working in business, industry or government.

The goal of this EDT project is to transform the training of mathematical sciences doctoral students at the University of Texas at Dallas (UTD) so that prior to the start of formal thesis research, the students gain important skills and experience using mathematics and statistics to improve understanding of problems in science and engineering, thus serving as a national model for such programs. The 16 supported doctoral students in UTD's EDT program spent two semesters of their first or second year working in teams with one other student, two faculty (when possible one mathematics and one statistics faculty person) at UTD, and one of our external partners from industry, a government lab, or a research institution.

This research endeavor replaced the students' normal teaching assistant duties in their second year and provided them with exposure to the entire research cycle from formation of an open-ended problem description through to obtaining final research results.

Specific objectives for the project included:
(1) Broadening opportunities for students to pursue solution of real-world application problems through connections with industrial and interdisciplinary researchers.
(2) Increasing students' confidence to tackle applied problems outside their comfort zone.
(3) Increasing students' ability to communicate within research teams and to wider audiences.
(4) Broadening the range of career paths for UTD Mathematical Sciences Ph.D. graduates and increasing
the number and proportion of students who do internships, take industrial research positions and postdoctoral positions at national labs and universities.

Over the course of the three year project, 8 different research subproject teams worked on applied problems with partners that included but were not limited to UT Southwestern Medical Center, Johns Hopkins Applied Physics Lab and Pioneer Natural Resources. The projects undertaken each year were the following.

In year 1: 

  • Using social media data to try to predict the spead of infectious diseases
  • Estimation of mechanical properties below the earth's subsurface used for oil and gas exploration, and specifically estimating the uncertainty in these parameter estimates

In year 2:

  • Evaluating the impact of climate change on insurance risks using  modern deep machine learning algorithms
  • Accounting for breathing artifacts in computerized tomography of the thorax used for radiation treament of patients with lung cancer.
  • Multisensor tracking of multiple  moving targets for defense applications

In year 3:

  • Automatic extraction of cell nuclei from pathological images to separate cancerous cells from non-cancerous cells in patient images
  • Tracking of moving targets such as missiles by a pair of biased radars
  • Using machine learning to recover seismic attributes constrained to match well log data

During the 3 year project mathematical sciences PhD students learned to communicate and collaborate with non-mathematicians (application domain scientists), to work together in teams, and to present their results in a professional setting both to the external partner and to a wider audience at professional meetings (both orally and in writing). The UTD EDT program resulted in over 25 publications and presentations. The program supported 16 students, 4 of whom were women and two of whom were under-represented minorities. The students gained valuable advanced computing skills and broadened their knowledge base beyond mathematics. Following their year as EDT students, four student trainees did summer internships at partner companies and labs. While internships are common in some academic disciplines, there was very little precedent for such experiences in Mathematical Sciences at UTD.  Finally, the EDT experience strengthened the Mathematical Sciences Departments' connections to local industrial partners in the Dallas/Fort Worth Metroplex, and the project facilitated new collaborations in the department between mathematics and statistics faculty.


Last Modified: 07/08/2019
Modified by: Susan E Minkoff

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