We live life in the network. We check our e-mails regularly, make mobile phone calls from
almost any location, swipe transit cards to use public transportation, and make purchases …

We live life in the network. When we wake up in the morning, we check our e-mail, make a
quick phone call, walk outside (our movements captured by a high definition video camera) …

With the rollout of smart metering infrastructure at scale, demand-response (DR) programs
may now be tailored based on users' consumption patterns as mined from sensed data. For …

The beginning of the age of artificial intelligence and machine learning has created new
challenges and opportunities for data analysts, statisticians, mathematicians …

This paper considers the problem of clustering a partially observed unweighted graph—ie,
one where for some node pairs we know there is an edge between them, for some others we …

This paper proposes a novel similarity measure between vector sequences. We work in the
framework of model-based approaches, where each sequence is first mapped to a Hidden …

The hidden Markov model (HMM) is a widely-used generative model that copes with
sequential data, assuming that each observation is conditioned on the state of a hidden …

The problem of clustering is considered for the case where every point is a time series. The
time series are either given in one batch (offline setting), or they are allowed to grow with …

We propose a framework, named Aggregated Wasserstein, for computing a dissimilarity
measure or distance between two Hidden Markov Models with state conditional distributions …

Sepsis is not a monolithic disease, but a loose collection of symptoms with diverse
outcomes. Thus, stratification and subtyping of sepsis patients is of great importance. We …