Research Interests

I am broadly interested in the development and application of machine learning methods. Currently I focus on developing AI-based tools for data wrangling in an effort to automate the tedious tasks of data preparation and data cleaning that often precede a machine learning analysis. 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.

Click here for my full CV.


Journal articles:




  • 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. PyPi - CRAN - GitHub.
  • Abed. Tool for benchmarking ML methods on compute clusters. PyPi - GitHub.
  • SyncRNG. The same random numbers in R and Python. CRAN - PyPi - GitHub.



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

Thesis Supervision:

Teaching assistant: