Welcome to MSRG_
The Middleware Systems Research Group at the University of Toronto
Currently, our work centers around foundational research in quantum computer systems.
We explore NISQ-era quantum algorithms, quantum error correction, and overall qubit noise reduction.
Our overall goal is to increase the robustness of modern quantum computing systems and explore scalability characteristics.
Our distributed learning systems projects focus on high performance distributed deep learning with the objective of optimizing training costs.
We also extensively engage in federated learning research in resource constrained environments (e.g., edge computing).
Our primary objective is to build new systems that can scale, are energy efficient, and legally compliant with emerging AI legislation.
As an application area, we work on systems for graph neural networks.
Within our data management research we focus on 3H (highly scalable, highly reliable, and highly availble) system designs.
Our research efforts span over transactional and analytical database systems, distributed ledger technology, and consensus mechanisms to keep distributed data in sync.