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?
We are always looking for interested trainees and students to join our research endeavors. We've summarized some specific points as well as some more general information about engaging with us. For more information, check out our we are hiring in the following areas ...
Can you imagine a distributed system that doesn’t manage data in any way, shape, or form?
Our research is dedicated to designing 3H systems—highly scalable, highly reliable, and highly available. We push the boundaries of transactional and analytical database systems, distributed ledger technologies, and consensus mechanisms to ensure seamless synchronization of distributed data.
We’re equally passionate about advancing cloud databases and resource efficiency in modern cloud environments, with a particular focus on containerized clouds driving serverless computing and microservices architectures.
How can distributed learning systems unlock the full potential of AI while remaining efficient and compliant?
Our research focuses on designing high-performance distributed machine learning systems that optimize training costs while ensuring scalability and energy efficiency. We explore federated learning as a key approach to distributed learning, enabling collaboration in resource-constrained environments like edge computing while tackling privacy and legal compliance challenges.
Central to our work is graph neural networks, advancing distributed learning through graph processing to derive AI-supported insights.
Can quantum computers deliver reliable and scalable performance to tackle real-world challenges?
Our research focuses on foundational advancements in quantum computer systems, including NISQ-era quantum algorithms, quantum error correction, qubit noise reduction, and distributed quantum computing. We aim to enhance the robustness and scalability of modern quantum systems, pushing the boundaries of quantum technology.
We explore applications such as quantum machine learning, molecular property predictions, and innovations in advanced, distributed data management, a core area of our expertise.
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The SustainSys project addresses the growing environmental impact of large-scale ML tasks, which demand complex compute and software infrastructure. To meet these challenges, future data engineers must adopt green, holistic approaches to design energy-efficient data processing platforms.
SustainSys is an NSERC CREATE training network where we partner with Concordia, McGill, and Waterloo to train a new generation of highly skilled computer scientists focused on sustainability.
The PANDORA project advances AI and data engineering to drive industrial innovation and economic growth. It develops AI-driven frameworks for trustworthy datasets, improving AI model accuracy, sustainability, and efficiency in managing IoT data for smarter, more responsive devices in smart ecosystems..
PANDORA is an EU-funded project in which we are participating as an associated partner.
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