The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …

Mobile sensing systems have been widely used as a practical approach to collect
behavioral and health-related information from individuals and to provide timely intervention …

Self-supervised representation learning methods have achieved significant success in
computer vision and natural language processing (NLP), where data samples exhibit explicit …

Mental health disorders, such as generalized anxiety disorder and depression, are prevalent
in modern society. Early detection of mental illness is essential to minimize the negative …

As mobile and wearable devices continue to grow in popularity, there is strong yet
unrealized potential to harness people's mobile sensing data to improve our understanding …

GPS-based navigation systems have played crucial roles to improve transportation system
performances. A limitation of such route guidance systems is that their route …

End-Stage Kidney Disease (ESKD) patients on hemodialysis suf-fer from kidney failure, with
the inability to remove excess fluid causing fluid overload. This can cause many morbidities …

Mobile sensing appears as a promising solution for health inference problem (eg, influenza-
like symptom recognition) by leveraging diverse smart sensors to capture fine-grained …

In most countries around the world, various public policies and guidelines, such as social
distancing and stay-at-home orders, have been put in place to slow down the spreading of …

The ubiquity of executing machine learning tasks on embedded systems with constrained
resources has made efficient execution of neural networks on these systems under the CPU …