PI4-PREPARE programs

  1. Computational Mathematics Bootcamp, led by Prof. Anil Hirani, and Sean Shahkarami (TA)
    Dates: June 9-20, in 239 Altgeld Hall
    Two week intensive session on computation in a scientific environment.
    See the Bootcamp homepage.
  2. Deterministic and Stochastic Dynamics of a Social Network, led by Prof. Jared Bronski
    Dates: June 2-6 and June 23-July 25, in 159 Altgeld Hall
    Social interaction networks are an important part of the human experience, whether it is relations among subtribes of the Gahuku-Gama people of the highlands of New Guinea or between the 1.1 billion users of Facebook. We will use analysis and numerics to explore several models of deterministic and stochastic dynamics on a network, including a gradient flow model intended to explain social balance theory (“The enemy of my enemy is my friend”) and the Kuramoto model, a model governing synchronization of oscillations, from the flashing of fireflies to the synchronization of electrical generators.
  3. Modeling and Analysis of Mathematical Challenges in Biology led by Prof. Lee DeVille
    Dates: June 2-6 in 173 Altgeld Hall, and June 23-25, July 1-2 in 123 Altgeld Hall computer lab.
    More information coming soon!


PI4-TRAIN programs

Illinois Biomath program – students can be attached to one of the existing summer Biomathematics programs, and can also participate in the Computational Mathematics Bootcamp. Topics in Summer 2014 will include:

  1. disease dynamics and evolution of host-resistance and pathogen-virulence,
  2. the visual system of fish,
  3. ants and networks, in particular how they construct colonies.

Dates depend on host and student schedules. Students can participate also in the Computational Mathematics Bootcamp.


PI4-INTERN topics

Industrial internships: at Personify, John Deere, Caterpillar, and possibly more.

Scientific internships:

  1. Molecular dynamics simulations of biophysical systems (e.g., peptides, lipids) and scientific data analysis and modeling using nonlinear machine learning and/or Bayesian inference model building.
  2. Synchronization and control of small-footprint power systems.
  3. Mechanisms responsible for shaping the patterns of morphological (i.e., form and structure) evolution that characterize the history of life.
  4. This project will seek to understand the three-way relationship among complexity increases in biological systems, information flow between scales in such systems, and thermodynamics. The intern will contribute to this understanding by modeling ecological dynamics over multiple time scales, through analytical work and computer simulation.

Internship dates depend on host and student schedules.