Research

Research Interests

I am broadly interested in the development and application of machine learning methods. My past research has been on multiclass classification – typically involving SVMs – as well as meta-learning and hierarchical classifier design. I’ve also worked on regularized regression methods, where I’m mainly interested in optimization algorithms for non-convex problems. On the more practical side I have developed software packages for most of my research projects, as well as a command-line tool to automate benchmarking of machine learning methods on distributed architectures.

Publications

Journal articles:

Preprints:

Dissertation:

Software

  • SmartSVM. Implements the SmartSVM classifier from this paper. PyPi - GitHub.
  • SparseStep. Implements the SparseStep method from this paper. CRAN - GitHub.
  • GenSVM. Implements the GenSVM method from this paper (in C and Python). PyPi - GitHub.
  • Abed. Tool for benchmarking ML methods on compute clusters. PyPi - GitHub.
  • SyncRNG. The same random numbers in R and Python. CRAN - PyPi - GitHub.

Teaching

Lecturer:

  • Programming – part-time lecturer, set up and pioneered the use of Autolab for this course (2015, 2016)

Thesis Supervision:

Teaching assistant: