current and prospective Graduate Students

 

In general I seek graduate student with background knowledge, interests, or strengths in at least some of the following:

  1. .Computational Methods

  2. .Smoothing or Nonlinear Regression

  3. .Mathematical Statistics

  4. .Differential Equation Models

  5. .Machine Learning


Please feel free to contact me for more details or visit the department FAQ website for information about applying for graduate studies at Carleton.

Prospective Graduate Students

Images clockwise from top left
  1. 1.Posterior distributions degenerating to a distribution along a manifold with increasing data points.

  2. 2.A posterior with a plateau region and a sharp peak. Was this MCMC mixing properly? Is the plateau an important feature of the interval estimate? Or is the plateau just a trap for Metropolis-Hastings?

  3. 3.Disjoint confidence regions for an SIR model of the black plague data set based on biweekly recordings of deaths by the plague in the quarantined village of Eyam. Daily data also exists and has been kindly posted by Wilfrid Kendall.

  4. 4.Posterior mean samples from a Gaussian Mixture Model for the Velocities of Galaxies.

 
  1. 1.Signing onto email lists with conferences and job ads long before you are ready to look for a job or present your work will keep you up to date on the nature and diversity of the job market for statisticians. There are a few lists around such as ssc, the allstat and others. Look for the digest option so you get at most a single message per day.


  1. 2. Attend meetups, network and meet the tech industry folks.


  1. 3.Canadian residents can enrol in a cotutelle (joint PhD) program between Carleton and a university in Belgium, Brazil, China, France, Germany, Italy, or Spain. This joint degree program involves spending approximately half or a quarter of your PhD on exchange.

Things worth considering

© Dave Campbell 2007-