Background subtraction, although being a very well-established field, has required
significant research efforts to tackle unsolved challenges and to accelerate the progress …

For many modern applications in science and engineering, data are collected in a streaming
fashion carrying time-varying information, and practitioners need to process them with a …

This paper introduces a generic and scalable framework for automated anomaly detection
on large scale time-series data. Early detection of anomalies plays a key role in maintaining …

Online detection of changes in stochastic systems, referred to as sequential change
detection or quickest change detection, is an important research topic in statistics, signal …

We introduce a new estimator, the simultaneous multiscale change point estimator SMUCE,
for the change point problem in exponential family regression. An unknown step function is …

This paper presents a new framework of recovering missing synchrophasor measurements
(erasures). Leveraging the approximate low-rank property of phasor measurement unit …

Defines the notion of an activity model learned from sensor data and presents key
algorithms that form the core of the field Activity Learning: Discovering, Recognizing and …

High-dimensional data monitoring and diagnosis has recently attracted increasing attention
among researchers as well as practitioners. However, existing process monitoring methods …

Temporally and spatially dense data-rich environments provide unprecedented
opportunities and challenges for effective process control. In this article, we propose a …

Timely detection of abrupt anomalies is crucial for real-time monitoring and security of
modern systems producing high-dimensional data. With this goal, we propose effective and …