This year's award was presented to Sanmi Koyejo, University of Illinois, Urbana-Champaign. This includes scalable, distributed, and robust machine learning, and metric elicitation; selecting more effective machine learning metrics via human interaction, primarily applied to ML fairness. His applied research includes applications to cognitive neuroimaging, healthcare, and biomedical imaging. Some recent work has concentrated on generative models for X-rays and fMRI, and risk-scoring and prediction models. You can see a list of recent publications here.

Edge devices/IoT such as smart phones, wearable devices, sensors, and smart homes are increasingly generating massive, diverse, and private data. In response, there is a trend towards moving computation, including the training of machinelearning models, from cloud/datacenters to edge devices [1,24]. Ideally, since trained on massive representative data, the resulting models exhibit improved generalization. In this paper, we consider distributed on-device machine learning. The distributed system is a server-worker architecture.