Welcome to the OpAL Lab

The Optimization, Analytics and Learning (OpAL) Lab is a research group at the Cheriton School of Computer Science. Our work is focused on machine learning on graphs, which is about making predictions using multi-modal datasets that combine features and relational information among entities. Our work involves stronger control on optimization formulations and algorithms, as well as their parallel and communication efficient implementations. We are equally interested in theory and in practice.

We are located at the University of Waterloo, a vibrant technological hub with Concept, Velocity and Google around the block. We are also part of the Scientific Computation Group.

We are looking for passionate new PhD students, Postdocs, and Master students to join the team (more info) !

We are grateful for funding from the University of Waterloo, NSERC, and DARPA.


23 February 2021

Our paper Statistical guarantees for local graph clustering was accepted at JMLR.

17 February 2021

New paper: Local Hyper-flow Diffusion.

16 February 2021

New paper: Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization.

9 December 2020

Our paper Parallel and Communication Avoiding Least Angle Regression was accepted at SISC.

14 October 2020

Shenghao Yang is one of the three nominees of the CS department at UWaterloo for the IBM PhD Fellowship Award Program.

18. September 2020

Shenghao Yang is presenting his work on Targeted Pandemic Containment Through Identifying Local Contact Network Bottlenecks at NetSci 2020 on Wed 23rd of September at 15:55pm CEST (or 09:55am EDT). Here is the schedule. Here is the paper on arXiv.

18. September 2020

The revised version of our paper on parallel and communication avoiding least angle regression is on arXiv. Joint work with S. Das, J. Demmel, L. Grigori, M. W. Mahoney and S. Yang.

18. September 2020

Welcome — our lab is officially starting!

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