A distinguishing feature of SLD research is challenging the status quo by bringing scientific and engineering approaches together. In particular, we use machine learning and optimization, network science, system design and optimization, and hardware prototyping to explore new avenues in edge computing and system design which can enable new applications of great societal interest.
Our current research topics of interest include IoT and edge computing, resource management for software-reconfigurable heterogeneous systems on chip, social sensing and epidemics modeling, and other emerging areas. These investigations are carried out using advanced concepts rooted in deep learning, model compression, knowledge distillation, imitation learning, federated learning, model-architecture co-design, as well as hardware prototyping.
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EdgeAI refers to the ability to run various AI applications directly on edge devices, hence minimizing or even eliminating the need to rely on the cloud. Given its huge potential to enable new opportunities for various IoT applications (e.g., image classification, object detection, autonomous driving, language processing, etc.), edge computing/IoT is currently one of the hottest research areas. Our research is primarily focused on developing new energy-aware machine learning techniques and hardware prototypes that leverage the network and the system characteristics to enable edge/IoT computing.
Networks are all around us. As such, network science is crucial for our understanding of many applications of high societal relevance (e.g., social and technological networks, epidemics, biological networks). Our research focuses on developing new machine learning methods to discover complex interactions and collective behaviors that determine how various types of events and behaviors in social networks are generated and propagated. In particular, we are interested in developing new approaches for social sensing that are relevant to the immediate concerns around pandemic detection and mitigation.
Futurists often speak of society’s inevitable technological “singularity,” a point in the near future where computers will become ubiquitous units, seamlessly integrated in everyday objects. This trend is already being foreshadowed by manycore processing via the network-on-chip approach, a novel paradigm which implements on-chip networks that enable platforms with extreme parallel capabilities. Our group seeks to develop new machine learning, optimization, and resource management techniques which can enable such a fundamental shift for energy-efficient, cost-effective, large-scale distributed computational platforms for both embedded and high-performance applications.