VIGRE at Maryland
Research Interaction Teams for Fall 2009

Research Interaction Teams for Fall 2009

(Many Research Interaction Teams evolved into Student Seminars.)

  • Student Dynamics Seminar
    • Organizers: Kevin McGoff (mcgoff@math.umd.edu), Joseph Galante (joepi@math.umd.edu)
    • 3:30 Tuesdays, Room to be determined.
  • Student Algebra/Number Theory Seminar
    • Organizers: Dave Karpuk (karpuk@math.umd.edu) and Sean Rostami (srostami@math.umd.edu)
    • Thursdays 2-3:15 in MTH 1311
    • First meeting: Thursday, September 10, 2009.
  • Advanced Complexity Theory
    • Dr. William Gasarch
    • First meeting: Wednesday, September 9, 2009
    • Wednesdays, 2-3.
    • A. V. Williams Building 3258
    • Requirements: Participants (for credit) must attend all the lectures (can miss with Dr's note) and give at least one lecture.
    • Goals: There has been alot of work in concrete complexity theory. In this theory the models are simple (e.g., Communication Complexity, Decision Trees, Circuits, Branching Programs) hence lower bounds are quite possible (unlike P vs NP). We will read papers that obtain real upper and lower bounds on natural problems in these models (note- ``natural'' is subjective). The results we obtain are ends in themselves in that we will learn the complexity of several problems.
  • Genetic Algorithms and Combinatorial Optimization
    • Professor Bruce L. Golden
      • France-Merrick Chair in Management Science
      • Editor-in-Chief, Networks
      • Decision & Information Technologies Robert H. Smith School of Business
        4339 Van Munching Hall
        University of Maryland
        College Park, MD 20742-1815
        301-405-2232 TEL
        301-405-8655 FAX
        bgolden@rhsmith.umd.edu
        http://www.rhsmith.umd.edu
    • First meeting: Tuesday, September 1, 5:00pm
      4339 Van Munching Hall
  • Ecology and Population Biology: Mathematics of movement
  • Pricing, Optimization and Data Mining
    • Wedad Elmaghraby, Wolfgang Jank, Itır Karaesmen Aydın
    • offices:VMH 4352, 4322, 4357
    • Time & location: Van Munching Hall, TBD
    • Office Hours: by appointment
    • Please get in touch with one of the instructors if you are interested in this course!
    • Course homepage: The course homepage will be setup on the Blackboard. Everyone registered for the course should be able to log in to Blackboard and view the course pages. URL for Blackboard is http://bb.rhsmith.umd.edu/.
    • Course notes: As needed, handouts and notes will be made available in class. All handouts and notes will be posted on Blackboard. Course prerequisites: STAT 700-01, STAT 740-41, BMGT 881. Strong computing skills; mastering of a statistical programming package (e.g. SAS, R); desire and willingness to work in a team and perform scholarly research NOTE: If you are interested in this course but are not sure whether you meet all the pre-requisites, please contact the instructors.
    • Course overview and objectives: Online merchants are discovering that by leveraging the Internet, real-time data can be collected and used to make accurate decisions regarding inventory control and supply chains. However, one component of the retail model has remained largely untapped by most e-retailers - price. What most e-retailers have yet to uncover is that by adjusting price levels based on real-time market data, dramatic increases in gross margins can occur. In fact, according to Arthur Andersen, a 1% increase in price can lead to operating profit improvements of 11% or greater. To achieve the goal of 'charging the right price', retailers must first understand the nature of the demand that they face (is demand highly inelastic, is it very time-sensitive, is it subject to dramatic shifts due to exogenous forces, etc.). Equipped with this knowledge, they then must understand how to optimize prices in the presence of their supply chain constraints. On the other hand, they have to understand how pricing decisions affect their bottomline and what demand- or pricing-related parameters are crucial for optimal decision-making. While the academic literature in operations management and management science for optimal decision-making in pricing and revenue management has been growing, the demand models that are proposed/used in that literature rely on several restrictive assumptions and rarely rely on "field data." In fact, demand-modeling is considered to be one of the "greatest unsolved problems" in revenue management and pricing. On the other hand, empirical and computational work - as in marketing or data mining- to characterize demand through field data rarely consider the role or affect of optimal decision-making. Research on use of statistics and data mining to characterize demand/pricing-related information in order to optimize pricing decisions will not only fill a void in the academic literature, but will have significant practical value. The objective of this course is to explore and develop methodology in price testing in order to optimize prices. In this research seminar, we will investigate both statistical and optimization models and methods to
      1. analyze pricing data in real time,
      2. develop statistically significant beliefs over the dynamics of demand,
      3. investigate methods suitable to anticipate changes in demand,
      4. optimize prices given the beliefs on demand.
      The course will include reading of subject-specific articles from the pricing and operations management literature; it will also include reading of articles from statistics and data mining. While some parts of the course will focus more on a theoretical understanding of the issues surrounding data-driven pricing, much of it will focus on hand-on research via analysis of pricing data using modern statistical methods and models, and simulation of real-world scenarios. To that end, the student will need good knowledge of statistical software packages (e.g. R, SAS) as well as other programming languages.
    • Required work: The course work will consist of data-driven research projects, reading & presentation of scholarly papers, and presentations of research progress.
  • Recent developments in Thurston theory of surfaces
    • Director: Bill Goldman
    • Organizer: Jeff Frazier
    • Tuesdays, 2pm, in MTH 1311
    • first meeting: Tuesday, September 8 in MTH 1311
    • This seminar will explore the geometry of W. Thurston's space of measured foliations on topological surfaces and the equivalent geometric theory of measured geodesic laminations on hyperbolic surfaces. Our viewpoint will follow Bonahon's theory of geodesic currents, and the ergodic theory of the geodesic flow. Dynamics of Kleinian groups and their limit sets will also be discussed. Particular attention will be given to the Teichmueller flow and the symplectic theory developed by Penner and Papadopoulos. We hope to discuss extensions to higher Fricke-Teichmueller theory as developed by Labourie, Fock-Goncharov and others.
  • Mathematical Finance
    • Organizers: Dilip Madan and Michael Fu
    • Tuesdays, 5pm
    • Room to be announced
  • Current Challenges in Materials Science: Aspects of Interface Motion
    • Ted Einstein (Physics) Dio Margetis (Math & IPST)
    • WEBSITE: http://www.math.umd.edu/~dio/RIT/Materials-Fall09/index.html
    • TIME (subject to change): Wednesdays, 4:30-5:30pm
    • ROOM: Math 1308
    • First meeting: Sept. 9, 2009
    • Abstract: This lecture series will explore current themes in mathematical materials science, with emphasis on the modeling and analysis of interface motion. Topics tentatively include: the statistical mechanics and kinetic theory of interfaces; boundary value problems for germane parabolic-type PDEs; prediction of surface morphology; homogenization; fluctuations and related Stochastic Differential Equations; energy storage and conversion.
  • Intersection Cohomology Methods in Representation Theory
    • Faculty Adviser: Tom Haines
    • Organizer: Sean Rostami
    • 4:00 Mondays in MTH1311
    • Organizational Meeting: 2:00 Wednesday, September 2 in MTH1311.
  • Multi-Level Statistical Models
    • Organizers: Eric Slud (evs@math.umd.edu) and Paul Smith (pjs@math.umd.edu)
    • Wednesdays 1-2pm, location TBA
    • First meeting: Wednesday, September 9, 2009.
    • Requirements: Participants should have had some upper-level course in Mathematical Statistics (at the level of Stat 420 or higher) and some introduction to Statistical Computing (at the level of Stat 430 for SAS or Stat 705 for R, but other Stat computing languages would also be OK). Some familiarity with linear or GLM models would be helpful.
    • Goals: to understand and work with Statistical Computing tools for (Frequentist and Bayesian) data analysis using mixed effect and multilevel and hierarchical linear and generalized-linear models. This is primarily an applied topic, except that there are necessary theoretical preparations for MCMC/Bugs software and for numerical analysis underlying likelihood calculations for multilevel models. We will read papers and portions of books and do data analyses primarily using R and SAS.
  • Topics in Cryptography
    • Organizer: Jonathan Katz
    • Times and location TBD
    • Note: this will be a weekly seminar, focused on reading and presenting current research papers in the field of cryptography. This semester we will focus on lattice-based cryptography. Email the organizer (jkatz@cs.umd.edu) for further details.
  • Applied PDE
    • Team Directors:
      • Stuart Antman
      • Sandra Cerrai
      • Manoussos Grillakis
      • Dave Levermore
      • Doron Levy
      • Matei Machedon
      • Dionisis Margetis
      • Antoine Mellet
      • Eitan Tadmor
      • Athanasios Tsavaras
      • Konstantina Trivisa
  • Cancer Dynamics
    • Organizer: Doron Levy (Math/CSCAMM), dlevy@math.umd.edu
    • Time: Fridays at 12pm
    • Organizational meeting: September 4, 2009
    • Location: CSCAMM Seminar Room (CSIC Building #406)
    • This semester we will focus on Mathematical Models of Cellular Regulation

Last modified: 14 November 2009

For more information contact Dr. William Goldman (wmg@math.umd.edu) .