The ScholarshipsCreating Opportunities for Applying Mathematics (SCOAM) project at IUP, while increasing the overall number of students pursuing degrees in mathematics, is unique in its goal of establishing a supportive connection of master’s students with undergraduates through scholarship cohort activities.
Each year, needbased scholarship funds support five or six new graduate students from the M.S. in Applied Mathematics program and seven to ten new sophomore, junior, or senior undergraduate students seeking a major or minor in a mathematical area.
Deadline for Spring 2016: We will accept applications only from graduate students for Spring 2016. Deadline Dec. 4, 2015.
If you received an award for the fall 2015 semester, you must submit the
midyear progress report by December 4, 2015.
Application Form for Undergraduate Students
Application Form for Graduate Students
Form for Recommendation Letter
Fall 2010 Scholarship Recipients
Spring 2011 Scholarship Recipients
Fall 2011 Scholarship Recipients
Spring 2012 Scholarship Recipients
Fall 2012 Scholarship Recipients
Spring 2013 Scholarship Recipients
Fall 2013 Scholarship Recipients
Spring 2014 Scholarship Recipients
Fall 2014 Scholarship Recipients
Spring 2015 Scholarships Recipients
Fall 2015 Scholarships Recipients
Benefit
In addition to financial support, SCOAM scholars will also participate in the Mathematics Enrichment Activities Network.
Activities Include
 Departmental or collegewide colloquia
 IUP Math Club, Actuary Club, or Preservice Teachers of Mathematics
 Local, regional, or national conferences
 Connections with working professionals in science and engineering fields
 Workshops in software training, job/internship search, and graduate school preparation
 Presentations by professionals from industry and academia
 Social gathering with other SCOAM scholars
2015–2016 Monthly Meetings
 August 23: 3:00–5:00 p.m. (Stright 240)
 September 29: 4:30–6:00 p.m. (Stright 240)
 October 28: 6:30–8:00 p.m. (Stright 240)
 December 4: 3:30–5:00 p.m. (Stright 240)

January 24, 3:005:00pm (Stright 240)
 Feb. 29, 3:305:00pm
 Mar. 31, 3:305:00pm
 Apr. 29, 3:305:00pm
Workshops: (Open to Public)
 Workshop: Introduction to R  Part I
3:30–5:00 p.m., September 22, in Stright 220
Presenter: Dr. Russ Stocker
 Workshop: Introduction to R  Part II
3:30–5:00 p.m., September 24, in Stright 220
Presenter: Dr. Russ Stocker
Abstract:
R is a free statistical software package that is used extensively in both academia and industry. It is an open source platform with currently more than 7,000 contributed packages that include the latest statistical tools for data analysis.
In this twoday workshop, we introduce the major components of R. These include importing and exporting data, data manipulation, descriptive statistics, graphics, inferential statistics, and statistical modeling. Participants in the workshop will be provided handouts that contain R examples and they will use R to complete a variety of exercises designed to help them learn the package.
 LINGO Workshop
3:305:00pm, Feb. 3, in Stright 220
Presenter: Dr. John Chrispell  SAGE Workshop
3:305:00pm, Mar. 2, 3:305:00pm, in Stright 220
Presenter: Dr. John Chrispell  3D printer workshop
3:005:00pm Mar. 30, in Stright 220
Presenter: Dr. Brian Sharp and Dr. Ed Donley
Invited Speakers: (Open to Public)
 October 12, 2015
1:252:15 p.m. Pratt Auditorium
Presenter: Jeremy Yagle, Data Analyst, NASA Langley Research Center
Title: Temporal Pattern Mining for the Prediction of Flutter from Aeroelasticity Data
Abstract: Aeroelastic Flutter is a highly nonlinear phenomenon caused by a combination of aerodynamics, inertial forces, and the elastic properties of the wing of an aircraft. Researchers at NASA are working to advance fundamental knowledge of aeroelastic phenomena through the development and application of prediction methods to aerospace vehicles. In support of their work, we are investigating a datadriven approach to understanding flutter by applying temporal pattern mining algorithms to the experimental data collected during wind tunnel testing. When testing an aircraft model in a wind tunnel, scientists and engineers currently rely on expert observation, monitoring of instrumentation, and the use of Fourier Transforms to convert data from the time domain to the frequency domain. This approach may not always be effective in identifying subtle dynamics that are precursors to flutter.
In this talk, I will discuss the use of pattern mining algorithms to identify repetitive subsequences  or "signatures"  in time series data collected from accelerometers. The family of algorithms being investigated has the capability to mine unprocessed, timedomain data in a fraction of the time required by other methods. Once identified, these signatures can then be mapped to events that occurred during wind tunnel testing. Detection and identification of significant signatures could lead to new insights about precursors to flutter and other aeroelastic phenomena. An overview of the algorithms and test methodology will be presented, along with a discussion of the potential challenges and benefits to this important area of aerospace research.
About the presenter:
At NASA Langley Research Center, I am involved in the Comprehensive Digital Transformation, which is an initiative focused on developing and implementing innovative data analytics and machine intelligence solutions for complex problems in Langley’s aerospace domain. Our work is centered on two key areas:
Data
Intensive Scientific Discovery: Automated mining of images, experimental data, and computational data that will help NASA scientists derive new insights and make new discoveries. The tools we develop in this area incorporate the underlying physics into the machine learning algorithms.
Deep
Content Analytics: Knowledge mining of scientific literature, web content, and multimedia sources that will allow NASA researchers to quickly analyze vast collections of information in order to answer specific questions. The tools in this area rely
on natural language processing and IBM Watson technologies.
The overall goal of our work is to provide NASA scientists with new tools that will lead to greater scientific discoveries and system design optimizations. While I am involved with several different project across the two key areas, the primary focus of my research is on the
detection and prediction of aeroelastic flutter from wind tunnel test data.
b. Career Panel: 3:30pm5:00pm in Stright Hall Room 226/229
Panel:
Nathan Adelgren, Ph.D. candidate, Clemson University
Ryan Grove, Ph.D. Program at Clemson University & Technical Staff at The
Aerospace Corporation
Matthew Sulkosky, Software Developer, Technology Management Associates
Jeremy Yagle, Data Analyst, NASA Langley Research Center
Biography:
Nathan Adelgren: I'm originally from Jamestown, NY. I attended IUP from 2009 to 2011 and earned a Master's degree in Applied Math. I am now beginning my fifth, and hopefully final, year working on a PhD at Clemson University in Clemson, SC. While at IUP I particularly enjoyed the courses I took in Operations Research with Dr. Kuo, and Computational Math with Drs. Kuo, Adkins and Donley. I chose to attend Clemson because it was one of the few Math programs that I found which offered classes in these areas in
additional to the more common pure math areas, algebra and analysis.
I have completed all of my degree requirements at Clemson except the completion of my dissertation, which will cover the work I've done in two distinct areas of Operations Research: Multiobjective mixedinteger programming and multiparametric linear complementarity problems. I will now briefly explain my
research, one topic at a time.
Multiobjective mixedinteger programming: Multiobjective optimization is a relatively new field in which optimization problems are studied that have more than one objective function. For example, you could consider maximizing profit and customer satisfaction simultaneously. As these
objectives are often conflicting, one can no longer expect to find a single “optimal solution,” but rather must look for a set of “Paretooptimal solutions,” which can be thought of as the solutions that offer an acceptable compromise between the objectives. Researchers have developed many tools for
solving these problems when all the variables in a model are continuous, but relatively little work has been done for problems containing integer variables. I am using the C programming language alongside IBM's optimization package, CPLEX, in order to develop a solver for problems containing both integer and
continuous variables and two objectives.
Multiparametric linear complementarity problems:
Multiparametric optimization is similar to the stochastic optimization that you've likely seen in your OR courses, but in this case some of a problem's defining data is simply assumed to be unknown rather than assumed to follow some probability distribution. The
goal is to find the optimal solution as a function of the parameters. Linear programs (LPs) and quadratic programs (QPs) can both be reformulated into a problem known as the linear complementarity problem (LCP) and thus, studying multiparametric LCP is a way of studying multiparametric LPs and QPs. Much work
has been done in the past to determine methods for solving multiparamtric LPs and QPs with parameters in the coefficients of the linear terms of the objective function and/or the right hand sides of the constraints, but little work has focused on when parameters occur in arbitrary locations. My research consists of developing the theory necessary for solving these problems with parameters in arbitrary locations.
Ryan Grove is currently a parttime Member of the Technical Staff (MTS) at The Aerospace Corporation. He is also a Graduate Teacher of Record, teaching MATH1060 at Clemson University. This is in addition to being a 3rd year PhD student and doing research under his advisor, Timo Heister, where he solves advectiondiffusion equations that appear in the
finite element discretization of a mantle convection simulation. In the summer of 2015, he was a Member of the Technical Staff (MTS) Summer PhD at The Aerospace Corporation, where he was a developer for the Genetic Resources for Innovation and Problem Solving
(GRIPS) program, which is a decisionsupport process that uses evolutionary algorithms, efficient parallel processing on thousands of compute cores, and advanced highdimensional visualization to solve complex problems.
Matthew Sulkosky: I grew up very close to the university in the town of Blairsville. After graduating high school, I joined the Pennsylvania Army National Guard in July of 2009. I completed initial training and returned to Pennsylvania to enroll in college. I started at Penn State University for Electrical Engineering. My first year there I learned that I strongly disliked my choice, and transferred to Indiana University of Pennsylvania for my two favorite subjects: Mathematics and Computer Science. Two years and an internship later, I graduated from IUP with two bachelors degree and moved to the Virginia area to work as a software developer. I have worked on a few different projects since moving to the area, mostly in the field of web development and a few projects in data analytic. This has been over the course of working at three different companies: Booz Allen Hamilton, Information Innovators Inc., Technology Management Associates.  Feb.23, 2016
Dr. Fern Hunt, Research Mathematician, Mathematical Modeling Group, National Institute of Standards and Technology
http://www.maa.org/fernyhunt
https://nces.ed.gov/nceskids/grabbag/Mathquiz/mathresult.asp?coolest=j
a. 11:0011:50am Pratt Auditorium
Title: A Mathematical Look at Paint, Hollywood and
Networks
Abstract: This talk will present several examples of the kinds of
problems a mathematician can encounter at NIST. They illustrate the breadth of
applications mathematical ideas.
b: 3:304:30pm STRGT 326/329
Title: An Algorithm for Identifying Optimal
Spreaders in a Random Walk Model of Network
Communication
Abstract: In a model of network
communication based on a random walk in an undirected graph, what subset of
nodes (of some fixed size), enable the fastest spread of information? The
dynamics of spread is described by a process dual to the movement from informed
to uninformed nodes. In this setting, an optimal set A minimizes the sum of the expected first
hitting times (F(A)), of random walks
that start at nodes outside the set. Identifying such a set is a problem in
combinatorial optimization that is probably NP hard. Fortunately, F has been
shown to be a supermodular and nonincreasing set function.
In this talk, the problem is
reformulated so that the search for solutions to optimization problem is
restricted to a class of optimal and "near" optimal subsets of the
graph. We will discuss our approach to the approximation and solution of this
problem based on properties of the underlying graph.
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 fulltime 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 webpage changes often. Please check back frequently for additional information.
If you have questions about this program, please contact:
Project Directors:
Dr. YuJu Kuo,
yjkuo@iup.edu
Dr. Rick Adkins,
fadkins@iup.edu
210 South Tenth Street
Mathematics Department
Indiana University of Pennsylvania
Phone: 7243572608
This project is funded by the National Science Foundation Scholarships in Science, Technology, Engineering, and Mathematics (S STEM) program under Award No. DUE 0966206 & DUE 1259860.