I defended my Ph.D in AI from the Cheriton School of Computer Science at the University of Waterloo.

I am interested in the theory and practice of machine learning. My thesis was focussed on developing and improving the theoretical foundations of clustering.

I am fortunate to be advised by Prof. Shai Ben-David. A consequence of this is that my research statement closely matches his.


Research motivation

When we discuss with some practioners as to why they chose a particular clustering algorithm, we often get the following responses. "Everyone uses it". OR "It has a nice python/matlab/(pick your favorite language) implementation". OR "Runs fast".

My research aims to address these issues by developing a principled way of choosing clustering algorithms. When is clustering easy? On what datasets should a particular be used? How should we incorporate human expertise into the clustering problem? We provide answers to such questions.

Selected publications

Data de-duplication using same-cluster queries.[AISTATS'19]
Joint work with Shai Ben-David and Ihab Ilyas.
Clustering using same-cluster queries. [NIPS'16 oral]
Joint work with Shai Ben-David and Hassan Ashtiani
Provably noise-robust kmeans clustering. [ARXIV ]
Joint work with Shai Ben-David and Yaoling Yu

Finding meaningful cluster structure amidst background noise.[ALT' 16]
Joint work with Shai Ben-David and Samira Samadi.

Multi-Pivot Quicksort: Theory and Experiments.[ALENEX' 14]
Joint work with Ian Munro, Alex Ortiz and Aurick Qiao.