21 accepted papers
Anomaly detection in spatiotemporal data is a challenging problem encountered in a variety of applications including urban traffic monitoring. Existing anomaly detection methods mostly focus on point anomalies and cannot deal with temporal and spatial dependencies that arise in spatiotemporal data.…
Subgroup identification is a fundamental step in precision medicine. Recent research applying data-driven methods such as independent component/vector analysis to multi-subject functional magnetic resonance imaging (fMRI) data has effectively revealed meaningful subgroups. These methods typically fo…
An important problem in graph signal processing (GSP) is to infer the topology of an unknown graph from a set of observations on the nodes of the graph, i.e. graph signals. Recently, graph learning (GL) approaches have been extended to learn dynamic graphs from temporal graph signals. However, exist…
Many real-world data are represented through the relations between data samples, i.e., a graph structure. Although many datasets come with a pre-existing graph, there is still a large number of applications where the graph structure is not readily available. An essential task for such cases is graph…
In recent work , we developed a fast, memory-efficient, and sample-efficient solution to the Low Rank column-wise Compressive Sensing (LRcCS) problem: recover an n × q LR matrix from m under-sampled linear projections of each of its columns. Here, undersampled means m ≪ n,q . The matrix LR model and…
Networks provide a powerful tool to model complex systems. Recently, there has been a growing interest in multiplex networks as they can represent the interactions between a pair of nodes through multiple types of links, each reflecting a distinct type of interaction. One of the important tools in u…
Graph learning (GL) aims to infer the topology of an unknown graph from a set of observations on its nodes, i.e., graph signals. While most of the existing GL approaches focus on homogeneous datasets, in many real world applications, data is heterogeneous, where graph signals are clustered and each…
Phase-amplitude coupling (PAC), which quantifies the coupling between the amplitude of a fast oscillation and the phase of a slow oscillation, is reported as a possible mechanism that controls the flow of information in the brain. Although there is ample evidence suggesting that neural interactions…
Anomaly detection in spatiotemporal data is a challenging problem encountered in a variety of applications including hyperspectral imaging, video surveillance, and urban traffic monitoring. Existing anomaly detection methods are most suited for point anomalies in sequence data and cannot deal with t…
Networks are useful representations of many systems with interacting entities, such as social, biological and physical systems. Characterizing the meso-scale organization, i.e. the community structure, is an important problem in network science. Community detection aims to partition the network into…
Long-distance neuronal communication in the brain is enabled by the interactions across various oscillatory frequencies. One interaction that is gaining importance during cognitive brain functions is phase amplitude coupling (PAC), where the phase of a slow oscillation modulates the amplitude of a f…
Interaction of neuronal oscillations across different frequency bands plays an important role in perception, attention, and memory. One particular form of interaction is the modulation of the amplitude of high-frequency oscillations by the phase of low-frequency oscillations, known as phase-amplitud…
Many real-world systems can be represented by networks. One common approach to characterizing the organization of networks is community detection. A lot of work has been conducted in community detection of static networks. However, most real systems are time-dependent and modeled by temporal network…
The rapid development of information technology is making it possible to collect massive amounts of multidimensional, multimodal data with high dimensionality in a diverse set of science and engineering disciplines. Although there has been a lot of recent work in the area of unsupervised tensor lear…
Recent work on resting-state functional magnetic resonance imaging (rs-fMRI) suggests that functional connectivity (FC) is dynamic. A variety of machine learning and signal processing tools have been applied to the study of dynamic functional connectivity networks (dFCNs) of the brain, by identifyin…
Many systems from human brain to the networks on social media, can be modeled as graphs. The network structure helps us understand, predict and optimize the behavior of dynamical systems. One of the important tools in understanding network topology is community detection. Even though community detec…
Graph signal processing extends the notion of frequency from signals in the time domain to signals defined on graphs. Graph signals arise in many applications including brain signals defined on functional connectivity networks. Most of the current work on graph signal processing focuses on static gr…
Advances in information technology are making it possible to collect increasingly massive amounts of multidimensional, multi-modal neuroimaging data such as functional magnetic resonance imaging (fMRI). Current fMRI datasets involve multiple variables including multiple subjects, as well as both tem…
Event-related potentials (ERP)s are electrophysiological responses that are commonly used for detecting the brain response to external stimuli. In this paper, we propose to use the sparse common component and innovations model (SCCI) to extract ERPs from multiple EEG signals recorded across closely…
Functional connectivity brain networks have been shown to demonstrate interesting complex network behavior such as small-worldness. Transforming networks to time series has provided an alternative way of characterizing the structure of complex networks. However, previously proposed deterministic met…
Recent developments In Information technology have enabled us to collect and analyze high dimensional and higher order data such as tensors. High dimensional data usually lies in a lower dimensional subspace and identifying this low-dimensional structure is important in many signal and information p…