Anomaly detection algorithms that intuitive, rigorous and scalable.


Monitoring large volumes of data and finding anomalous behavior in them is a ubiquitous challenge. The data are of typically high-dimensional, heterogeneous (categorical and numerical), and contain irrelevant attributes and noise. Labels are often scarce and/or expensive, hence unsupervised learning methods are called for.

Our goal is to come up with algorithms that
  • Make minimal generative assumptions, and hence apply broadly.
  • Give rigorous guarantees, and whose outcomes are easily interpretable.
  • Can handle heterogeneous datasets.
  • Are highly performant and scale to large volumes and high dimensions.


2019 Interns

2017 Interns

Related Publications


  • Active Research Areas

Research Areas

  • Algorithms
  • Machine Learning
  • Statistics