Welcome to MSRG_
The Middleware Systems Research Group at the University of Toronto
Traditionally, the “M” in MSRG stood for “middleware”—the Middleware Systems Research Group at the University of Toronto. But don’t worry, “middleware is literally everywhere.” Over the years, that “M” has evolved to mean “mobile,” “microservices,” “machine learning,” and, last but not least, “matter,” as in entangled particles in space-time. What more could you ask for?
Can you imagine a distributed system that doesn’t manage data in any way, shape, or form?
MSRG advances the frontiers of data management and distributed systems research. We develop scalable middleware platforms for efficient large-scale data processing and real-time event streaming. Our research has pioneered novel approaches to publish/subscribe systems, content-based routing, and filtering techniques that enable efficient data dissemination across distributed applications. In the cloud computing space, we optimize resource allocation and system performance for data-intensive workloads. Our work bridges theoretical computer science with practical distributed computing challenges, delivering innovative solutions that shape the future of middleware systems.
How can distributed learning systems unlock the full potential of AI while remaining efficient and compliant?
At MSRG, we tackle core challenges in distributed deep learning systems. Our research focuses on optimizing training and inference across distributed infrastructure, developing novel approaches for resource allocation and scheduling of training jobs, and creating system-level improvements for deep learning and graph learning frameworks. We are advancing federated learning for resource-constrained and embedded devices, optimizing performance and communication efficiency in edge computing environments. Through our work, we aim to make distributed deep learning more efficient and accessible, enabling faster training and better resource utilization.
Can quantum computers deliver reliable and scalable performance to tackle real-world challenges?
MSRG works on foundational challenges of quantum computing including NISQ-era quantum algorithms, quantum error correction, and qubit noise reduction. We develop distributed quantum algorithms to accelerate electronic structure calculations, enabling more accurate modelling of molecular systems. Simultaneously, we use quantum machine learning techniques to efficiently explore chemical compound space, contributing to material discovery research. This interdisciplinary approach bridges quantum computing, machine learning, and computational chemistry to tackle fundamental challenges in molecular simulation and materials discovery.
If you are interested in work on one of our research areas, collaborate with an interdisciplinary team, and leave your mark in research do not hesitate to reach out to Prof. Jacobsen.