Year 2012-13

2012-2013 Monthly Meetings

  1. August Meeting: 4:00-6:00 p.m. on August 26 (Stright 226/229)
  2. October Meeting: 2:30-4:00 p.m. on October 1 (Northern Suite Room 116)
  3. November Meeting: 2:00-3:15 p.m. on November 6 (Stright 333)
  4. December Meeting: 2:30-4:00 p.m. on December 7 (Northern Suite Room 116)
  5. January Meeting: 4:00-6:00 p.m. on January 27 (Stright 226/229)
  6. March Meeting: 6:30-8:00 p.m. on March 4 (Stright 226/229)
  7. April Meeting: 6:30-8:00 p.m. on April 17 (Stright 226/229)
  8. May Meeting: 2:30-4:30 p.m. on May 10 (Northern Suite Room 116)

Workshops: (Open to Public)

  1. R: 3:30-5:00 p.m. on September 5, in Stright 220
    Presenter: Dr. Russ Stocker

    Abstract: R is a free software package commonly used for data analysis and research. It is an open source platform that has over 4000 contributed packages that allow users to implement the latest statistical methodology. In this workshop, we will introduce the major components of R. This will include the importing and exporting of data, descriptive statistics, graphics, inferential statistics, and statistical modeling. Participants of the workshop will utilize the R package on a computer to perform a variety of analyses.
  2. LINGO: 3:30-5:00 p.m. on October 10, in Stright 220
    Presenter: Dr. John Chrispell

    Abstract: LINDO and LINGO are comprehensive software tools designed by Lindo Systems Inc. to help you build and solve a wide variety of optimization models efficiently. Free trial and student versions of the software make it an ideal choice to start solving optimization models. In this workshop we will take a hands on approach to learning some of the basics of the Lindo and Lingo software by solving several example problems.
  3. Sage: 6:30-8:00 p.m. on February 13, in Stright 220
    Presenter: Dr. John Chrispell

    Abstract:Sage is a free mathematics software system licensed under the GPL. Using a Python-based interface Sage's goal is to be an open source alternative to software packages like Maple, Mathematica and Matlab. In the work shop an introduction to the sage software, and its different interfaces will be given using the IUP sage server and hands on sage notebook activities.

Invited Speakers: (Open to Public)

  • September 26, 2012SIAM Visiting Lecturer Program
    Dr. Sara Del Valle
    : Scientist/Project Leader, Los Alamos National Laboratory, Energy and Infrastructure Analysis, D-4

    Dr. Sara Del Valle earned a Ph.D. in Applied Mathematics and Computational Sciences in 2005 from the University of Iowa, and a B.S. and M.S. in Applied Mathematics in 2000 and 2001, respectively, from the New Jersey Institute of Technology. Sara has worked on developing and analyzing mathematical models for the spread of infectious diseases including smallpox, anthrax, malaria, HIV, and influenza on a pandemic scale. She has also worked on social network analyses and modeling and simulation of large scale, agent-based simulations. She's currently a scientist and project leader at Los Alamos National Laboratory.
    1. Great Careers in Mathematical Sciences: 1:25-2:15 p.m. in Pratt Auditorium
      Abstract: Science, technology, engineering, and mathematics (STEM) are playing a key role in the development of new discoveries and meeting the challenges of this century. In this talk, I will present some examples of scientific advances made possible by the interaction between science and mathematics. In particular, I will describe the role of mathematics in epidemiology, climatology, biology, sports, and entertainment.
    2. How to Succeed in Science: My Story: 3:35-4:35 p.m. in Stright Hall, Room 327-329
      Abstract: Pursuing a career in science has never been more timely, but also difficult due to the current economic uncertainty. In this talk, I describe my journey and provide some advice on the difficulties that lie ahead and the skills that you will need to succeed. Although my advice is geared toward women and minorities, many aspects of my talk are universal.
    3. Mathematical Modeling for the Spread of Infectious Diseases: 5:30-6:30 p.m. in Stright Hall, Room 327/329
      Abstract: Emerging and re-emerging infectious diseases are the leading cause of morbidity and mortality across the globe. Modeling efforts can help improve the effectiveness of public health interventions and minimize the population and economic impacts of an epidemic. In this talk, I will describe different mathematical and computational models used to simulate the spread of infectious diseases, including smallpox, influenza, and HIV and show the impact of intervention strategies on their spread.
  • November 30, 3:30-4:30 p.m., Alumni Career Panel in Stright Hall, Room 327/329

    Dane Alabran
    (B.S. in Computer Science and Mathematics and M.S. in Applied Mathematics) is a report analyst in Information Services Department in Indiana Regional Medical Center.

    Jennifer Casanova (B.S. in Chemistry and Natural Science with minor in Dance and Mathematics) is pursuing a Master of Science in Public Health degree in the Department of Environmental Sciences and Engineering at the Gillings School of Global Public Health at University of North Carolina at Chapel Hill.

    SaraJane Parsons (B.S. in Mathematics with minor in Economics and Business Administration) is in the Economics Ph.D. Program at Michigan State University in East Lansing, Michigan.

  • April 25, 2013-- INFORMS Speakers Program
    Dr. Adam Rosenberg, Clear Demand, Chief Science Officer

    Adam Rosenberg has three decades of mathematical decision-support expertise in industries including retail science, airline optimization, financial-market analysis, hotel yield management, railroad line simulation, and telephony. He started his career at Bell Telephone Laboratories developing the emerging cellular telephone systems. He also worked in printed-circuit-board design and manufacture and wrote a book on CDMA, the most-recent mobile telephone technology. Many of his software solutions have remained in
    production for over a decade and they have earned or saved hundreds of millions of dollars for his employers and clients. Dr. Rosenberg earned his M.S. and Ph.D. degrees from Stanford University in Operations Research and his A.B. from Princeton University in Mathematics (cum laude). He holds several patents.

    1. Title: Least squares with one constraint
    2:00-3:00pm STRGT 327/329

    Abstract: We have a collection of prices that reflect brand preferences from a retail client. Think of a grocery store chain with various name brands like Kellogg's, General Mills, or Quaker Oats and private-label store brands, think Cheerios versus "Tasty-O's." We are trying to quantify the price relationships among N brands based on existing prices.

    We're going to use a least-squares fit to find N-1 brand values (if we fix one of them) based on the N*(N-1)/2 observed price ratios. We avoid the trivial solution of all the values equal to zero by imposing a single constraint on the magnitude of the values.

    Starting with the derivation of the basic formula for minimizing the least sum of squared error with linear coefficients, we'll add the single constraint and derive the formula we're using in our own brand-value calculation.

    2. Career Talk: 3:30-4:20pm STRGT 327/329

    3. Title: Optimal Choices contour for sums
    5:00-6:00pm STRGT 327/329

    Abstract: For each of N products p we have a selection of M(p) prices. Each of these prices has revenue R(P) and profit pi(P) [that's
    supposed to be the Greek letter pi for profit] associated with that price. The revenue and profit value are derived from retail demand models. We want to know the best price for each product. The concept of "best" depends on our relative weight of revenue and profit. We consider a pure-revenue objective to be lambda=zero and pure profit to be lambda=one with a continuum of revenue-profit lambda optimization weights in between. The goal of this work is to present all the optimal price possibilities for all N products over the zero-to-one range of lambda. We'll start by finding the optimal price for each product as a function of lambda. That involves finding the frontier, part of the convex hull of the revenue-profit values for all the prices. Rather than enumerate M-to-the-N price-choice possibilities, we'll find their convex hull in revenue-profit space with nothing more computationally vigorous than sorting the individual-product solutions once. While this two-objective frontier is complete and exact, extension this method to three objectives (for example, revenue, profit, and units sold) is diluted to a good approximation.

Affiliated Events:

  1. 2013 Undergraduate Scholars Forum
    Best Computational Science Poster: Teresa Direks, "Resonance-Assisted Hydrogen Bonding (RAHB) in Carboxyphosphate"
  2. 2013 Women in Mathematics, Science, and Technology Program
    Best Computational Science Poster (S-COAM Grant): Breeanna Mintmier (Chemistry) - Dr. John Ford, faculty supervisor
  3. Allegheny Mountain Section,MAA, April 5& 6, 2013, Indiana, PA Graduate School Panel on Saturday, April 6, 2013
  4. Mathematics Department Scholarship Banque t, May10, 2013