Research Areas

Twenty years ago, Jim Gray wrote: “Put everything in future disk controllers (it’s not ‘if’, it’s ‘when’).” His argument was that running application code on disk controllers would be (a) possible, because disks would be equipped with powerful processors and connected to networks via high-level protocols, and (b) necessary, to minimize data movement. He concluded that disk controllers would need a programming environment. Computational storage makes it possible to define a new storage interface directly on the storage controller or on a processing unit (FPGA or SoC) placed on the data path.
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In the cloud today, the quantity of information that needs to be stored and processed is growing faster than the performance of general purpose processors. For decades, Moore’s Law has ensured that CPUs could keep up, but today it is unlikely that this trend can be maintained. To overcome this challenge, we investigate how software interacts with the underlying server hardware and explore ways in which we could tailor the latter to the application’s needs.
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IoT systems deployed for predictive maintenance, asset management, or virtual power plants collect and process large amounts of data locally. This data is used by “prescriptive” models implemented in micro data centers at the edge of the network. These models adapt to changing local conditions, are maintained with as little interference from operators as possible, and are optimized for clear objectives agreed upon per region or per sector. We research methods and tools for developing AI-based solutions on edge-based software platforms.
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Multimedia analytics is a new and exciting research area that combines techniques from multimedia analysis, visual analytics, and data management, with a focus on creating systems for analyzing large-scale multimedia collections. The size and complexity of media collections are ever increasing, as is the desire to harvest useful information from these collections, with expected impacts ranging from the advancement of science to increased company profits. Indeed, multimedia analytics has potential applications in diverse fields, including data journalism, urban computing, lifelogging, digital heritage, healthcare, digital forensics, the natural sciences, and social media.
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The variety and complexity of data-intensive applications and systems have increased drastically over the past decade. A SQL-based big data analytics request running on Apache Spark can be very different from a deep learning training job using TensorFlow. Nevertheless, these data-intensive applications increasingly run on shared hardware resources in data centers or on high-performance computing (HPC) platforms. These hardware resources are also diverse today, ranging from general-purpose CPUs and GPUs to programmable FPGAs and specialized machine learning hardware such as TPUs.
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Federated learning has been gaining adoption in mobile and cloud computing as a mechanism for decentralizing both computation and trust when building machine learning models from user data. Proposals for using federated learning on IoT devices have been emerging, but even though federated learning could provide valuable insights from IoT devices faster and more efficiently, its adoption has been lackluster. We argue that this slow adoption is due to two challenges.
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