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.
We are actively looking for smart and passionate students like you!
Join the team and be at the forefront of machine learning, network science, and systems design.