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The Scholarships Creating Opportunities for Applying Mathematics (S-COAM) project at Indiana University of Pennsylvania is seeking applicants for our unique scholarship program. In addition to providing monetary scholarship support, S-COAM creates a network of students that engage in professional development activities to improve your career prospects and success in STEM fields.

While increasing numbers of students pursuing mathematics degrees, S-COAM is unique in its goal of establishing a supportive connection of graduate students with undergraduates through scholarship cohort activities.

Need-based scholarships annually support seven to nine students from the MS in Applied Mathematics and about 40 undergraduates seeking a mathematics major or minor in mathematics with a STEM major. Annually, 10 to 20 new freshmen are recruited with at least 87 distinct students supported over five years. We are seeking high-achieving high school students with financial needs from across the region.

The selection process considers need, diversity, academic performance, activities, and a personal statement.

We Want You to Apply!

Apply according to your student group:

Monthly Meetings for 2022-23

Spring 2023

  • Friday February 24, 11:15-12:15 in Stright 226
  • Friday March 24, 11:15-12:15 in Stright 226
  • Friday April 28 TBA

Fall 2022

  • September 12, 11:20-12:20, STRGT 226/229
  • October 6, 3:45-5:15pm, STRGT 226/229   
  • Nov. 2, 11:20-12:20, STRGT 226/229  
  • Dec. 2, 11:20am-12:20pm, STRGT 226/229  

Benefit

In addition to financial support, S-COAM scholars will also participate in the Mathematics Enrichment Activities Network.

Our Students Engage in Many Activities, Including:

  • Local, regional, or national conferences
  • Peer-led, team-learning sessions for freshmen
  • Research opportunities
  • Departmental or college-wide colloquia
  • Workshops in software training, job/internship search, and graduate school preparation
  • Connections with working professionals in science and engineering fields
  • Social gatherings with other S-COAM scholars
  • IUP clubs, including Math Club, Actuary Club, or Preservice Teachers of Mathematics
  • Presentations by professionals from industry and academia

Scholarship Renewal Requirements

  • Participate in all required events in the program
  • Submit the FAFSA form, and continue to meet the federal financial aid requirements every semester
  • Continue as a full-time student at IUP making satisfactory progress toward a qualifying sciences and mathematics degree
  • Submit a summary of activities every semester with a short essay on the program and his/her academic progress
  • Complete the program outcomes and assessment survey
  • Undergraduates must maintain a cumulative GPA of at least 3.0 on a 4.0 scale, and graduates must maintain a cumulative GPA of at least 3.2 on a 4.0 scale.

This web page changes often. Please check back frequently for additional information.

Graduate Students

Check out our Survival Guide for Graduate Students in Applied Mathematics and Data Science.

Peer-Led Team Learning Materials

Workshops

3D Printer Workshop I: 3D Printing Basics

  • October 13, 6:30-8:00 p.m.
  • Stright 220
  • Dr. Rick Adkins and Dr. John Chrispell

This workshop introduces the basics of using 3D printers. Participants learn about the parts of a 3D printer, differences in materials, basics of moving from model design to appropriate in-fill and support, and the tools and standards used in creating and exchanging computer files storing 3D information. Demonstrations of both 3D model creation and printing will be shown using standard software tools. Workshop participants will have the opportunity to create 3D models that could be printed using standard 3D printers.

3D Printer Workshop II: Beyond the Basics

  • October 27, 6:30-8:00 p.m.
  • STRGT 220
  • Dr. Rick Adkins and Dr. John Chrispell

This workshop engages participants with a hands-on approach to creating models for 3D printing. Participants will use notebooks to create 3D geometries that can be converted to file types that are acceptable for 3D printing. The focus of this workshop will be more on the mathematics needed for 3D printing, rather than the creation of complicated 3D printable models.

MATLAB/Octave Workshop I: The Basics of MATLAB/Octave

  • January 31, 3:10-4:40 p.m.
  • STRGT 220
  • Dr. Yu-Ju Kuo

This workshop covers the basics of using MATLAB/Octave, including working with vectors and matrices, defining mathematical functions, plotting graphs of functions, and creating animations using three basic commands.

MATLAB/Octave Workshop II: Simulation and Publishing in MATLAB

  • February 7, 3:10-4:40 p.m.
  • STRGT 220
  • Dr. Yu-Ju Kuo

This workshop covers basic statistical functions and random number generators and how to do simulations of stochastic processes. The second part of this workshop discusses how to create a GUI and interactive notebooks.

Major Events

Career Paths for Mathematicians at the USDA, Sean Rhodes, December 2, 2022

Alumnus Sean Rhodes from the USDA's National Agricultural Statistics Service (NASS) will be discussing career paths for mathematicians within the US Department of Agriculture.

 

SIAM Lecture Series, Dr. Sumanth Swaminathan, October 22, 2021

Dr. Sumanth Swaminathan is a digital health entrepreneur, interdisciplinary scientist, teacher, and performing musician with a professional career that spans academia, government labs, large/small corporations, and startups. He has over a decade of product and business development experience in past roles as a technology consultant, chief data scientist, CEO, and startup Founder. During these roles, he has successfully built and led simulation/mathematical modeling focused teams, he has won government Small Business Innovation Research grants (over $1M in capital), he has raised millions of dollars in private capital, he has led and co-authored numerous peer-reviewed publications and talks, and he has hired and mentored over a dozen PhD and Masters-level students.

Dr. Swaminathan is an adjunct professor of mathematics at the University of Delaware and a specialist in numerical methods including machine-learning predictions and stochastic simulation. He did his PhD in applied mathematics from Northwestern University and Postdoctoral research fellowship at Oxford University & Northwestern University. Outside of his professional life, he’s a passionate saxophone performer/teacher who loves to travel.

October 22, 11:30 a.m.–12:30 p.m., HSS/Leonard Hall, Room 225

"Maximizing Value through Collaboration: Lessons from Academia, Corporations, Government Labs, Small Companies, and Startups"

Sponsored by Mathematical and Computer Sciences Department, S-COAM (DUE 1742304), Science Inspires Series

Abstract:  Collaboration across semi-autonomous organizational functions is a regular and necessary activity in industry.   In this talk, Dr. Swaminathan, himself a product of a highly interdisciplinary education, discusses several examples of successful collaborations in academia, government labs, large/small industrial organizations, and startups. Specific aspects of each example to be discussed include: 1) the process of forming a collaboration, 2) the value add of team units, 3) the diverse set of value propositions that could emerge from collaboration, 4) the unexpected and sometimes serendipitous learning opportunities inherent in collaboration, and 5) the long-term career benefits of collaborating often and early.   

October 22, 1:50–2:50 p.m., HSS/Leonard Hall, Room B10

"Machine-learning Methods and Patient Data Generation Schemes for Remote Detection and Monitoring of Health Deterioration due to Lung and Heart Illness"

Sponsored by Mathematical and Computer Sciences Department, S-COAM (DUE 1742304), SIAM IUP Student Chapter

Abstract:

The COVID-19 pandemic heavily accelerated adoption of telemedicine and remote care services to reduce infection spread and protect essential workers. This has created a new climate of remote-care opportunities to tackle long-standing public health problems endemic to respiratory illness. Acute health deterioration events (exacerbations) due to chronic lung disease account for ~ 200,000 annual deaths and $70 billion of an astounding $130 billion in annual US direct costs. Hospitalizations from heart failure exacerbations account for 6.5 million hospital days, the leading cause of hospitalization in the USA and Europe. The lack of accurate, automated, and personalized approaches for self-identification and early care of these illnesses has led to unnecessary healthcare utilization, increased morbidity, and missed opportunities for timely therapeutic intervention. Moreover, at present there are no scalable technologies for identifying early onset illness at home. 

Here, we describe a machine-learning approach to classifying and triaging health deterioration events due to chronic and infectious illness. Algorithms are trained to consume patient health data inclusive of symptoms, consumer-available biometric data, and patient profile/demographic info. Novel simulation schemes are used to generate clinically diverse, statistically comprehensive patient scenarios that form the train and test datasets for machine-learning classifiers. Classifiers take an input of patient health data and return an assessment of both the existence and severity of health downturns. Performance studies on the machine-learning classifiers indicate superior accuracy, sensitivity, and specificity in making predictions when compared to standard-of-care. 

Dr. Jonathon Leverenz, Operations Research Analyst, Systems Planning and Analysis, Inc., February 18

Dr. Leverenz is an operations research analyst for Systems Planning and Analysis, Inc., a company that provides knowledge-based solutions integrating technical, operational, programmatic, policy, and business solutions in support of important national security objectives. Leverenz specializes in operations research and received his PhD from Clemson University in 2015.

"O.R. - What's That?," February 18, 2022, 11:35 a.m.–12:25 p.m., HSS/Leonard Hall, room 225

Abstract: Operations Research (O.R.) emerged during World War II from Allied efforts to hunt German U-Boats, move cargo safely across the Atlantic, and operate radar more effectively. It is a model-centric, scientific approach to studying systems and processes. Methods in O.R. focus on understanding how these systems operate and then applies ideas from math and science to find ways of improving them.

This talk discusses the role of O.R. in the decision sciences and some of its military and security applications. An example is presented that illustrates how operational knowledge and requirements are
translated into a model of a system. Such a model can be used to determine the implications of different use cases and provide insight to decision makers.

This is a joint event of the S-COAM program and Kopchick College of Natural Sciences and Mathematics Science Inspires Series.

"Career Path Q&A," February 18, 2022, 1:00 p.m.–1:45 p.m., Stright Hall, room 226

Dr. Leverenz will talk about his career path and answer student questions about academic preparation and careers.


If you have questions about this program, please contact:

Project Directors

Yu-Ju Kuo, yjkuo@iup.edu
Rick Adkins, fadkins@iup.edu
210 South Tenth Street
Mathematical and Computer Sciences Department
Indiana University of Pennsylvania
Phone: 724-357-2608

This project is funded by the National Science Foundation Scholarships in Science, Technology, Engineering, and Mathematics (S STEM) program under Award No. DUE 1742304.

Past S-COAM Scholarship Recipients

John J. and Char Kopchick College of Natural Sciences and Mathematics
  • Mathematical and Computer Sciences Department
  • Stright Hall, Room 233
    210 South Tenth Street
    Indiana, PA 15705
  • Phone: 724-357-2608
  • Fax: 724-357-7908
Office hours
Monday through Friday
1:00 p.m.–4:30 p.m.