Research Interests

I am broadly interested in the development and application of machine learning methods. In my current position as postdoctoral researcher at the Alan Turing Institute I focus on developing AI-based tools for data wrangling, in an effort to automate the tedious manual tasks of data preparation and data cleaning that often precede a machine learning analysis. I've worked on change point detection, data parsing, matrix factorization, multiclass SVMs, and sparse regression, among other things. Because my research is often focused on developing methods that work well in the real world, I have also created easy-to-use software packages for most of my research projects.

For more about me, check out my industry resume or academic CV.


Journal articles:

  • Wrangling Messy CSV Files by Detecting Row and Type Patterns (HTMLPDF)
    and and
    Data Mining and Knowledge Discovery, .
    ▸ Show abstract
  • GenSVM: A Generalized Multiclass Support Vector Machine (PDF)
    Journal of Machine Learning Research, 17(224):142, .
    Code: CRPython
    ▸ Show abstract

Conference proceedings:

  • Probabilistic Sequential Matrix Factorization (PDF)
    and and and
    Accepted for publication at AISTATS, .
    ▸ Show abstract


  • On Memorization in Probabilistic Deep Generative Models (PDF)
    arXiv preprint 2106.03216, .
    ▸ Show abstract
  • An Evaluation of Change Point Detection Algorithms (PDF)
    arXiv preprint 2003.06222, .
    ▸ Show abstract
  • Fast Meta-Learning for Adaptive Hierarchical Classifier Design (PDF)
    arXiv preprint 1711.03512, .
    Code: Python
    ▸ Show abstract
  • SparseStep: Approximating the Counting Norm for Sparse Regularization (PDF)
    and and
    arXiv preprint 1701.06967, .
    Code: R
    ▸ Show abstract


  • Algorithms for Multiclass Classification and Regularized Regression (PDF)
    Erasmus University Rotterdam, .
    ▸ Show abstract


I aim to make my research accessible by providing software packages for the methods I develop.

  • CleverCSV. Implements the method from this paper. PyPI - GitHub.
  • 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: