Selected Recent Publications

Mixed-frequency data prediction tasks are pertinent in various application domains, in which one leverages progressively available …

The study considers a vector autoregressive model for high-dimensional mixed fre- quency data, where selective time series are …

Even though many time series are sampled at different frequencies, their joint evolution is usually modeled and analyzed at a common …

Vector autoregression has been widely used for modeling and analysis of multivariate time series data. In high-dimensional settings, …

Estimating a directed acyclic graph (DAG) from observational data represents a canonical learning problem and has generated a lot of …

We consider the problem of constructing asymptotically valid confidence intervals for the change point in a high-dimensional covariance …

We study the problem of detecting and locating change points in high-dimensional Vector Autoregressive (VAR) models, whose transition …

In network analysis, it is common to work with a collection of graphs that exhibit hetero- geneity. For example, neuroimaging data from …

Adaptive optimization methods, such as AdaGrad , RMSProp , and Adam , are widely used in solving large-scale machine learning problems. …

Due to increasing rates of adoption of electric vehicles (EVs), there is a strong need to deploy the necessary charging station …

We introduce a general tensor model suitable for data analytic tasks for heterogeneous datasets, wherein there are joint low-rank …

Tensor regression models are of emerging interest in diverse fields of social and behavioral sciences, including neuroimaging analysis, …

The paper addresses joint sparsity selection in the regression coefficient matrix and the error precision (inverse covariance) matrix …

The joint estimation of multiple graphical models from high-dimensional data has been studied in the statistics and machine learning …

We consider a general electric vehicle (EV) charging system with stochastic demand, demand request locations, and predetermined …

The rapid development of high-throughput technologies has enabled the generation of data from biological or disease processes that span …

There is increasing interest in identifying changes in the underlying states of brain networks. The availability of large scale …

Many real time series datasets exhibit structural changes over time. A popular model for capturing their temporal dependence is that of …

Min-max saddle point games have recently been intensely studied, due to their wide range of applications, including training Generative …

We study the attribution problem in a graphical model, wherein the objective is to quantify how the effect of changes at the source …

Vector autoregressive (VAR) models aim to capture linear temporal interdependencies among multiple time series. They have been widely …

We consider identification of linear dynamical systems comprising of high-dimensional signals, where the output noise components …

Performance of adaptive control policies is assessed through the regret with respect to the optimal regulator, which reflects the …

This paper studies adaptive algorithms for simultaneous regulation (i.e., control) and estimation (i.e., learning) of Multiple Input …

High dimensional piecewise stationary graphical models represent a versatile class for mod- elling time varying networks arising in …

A factor-augmented vector autoregressive (FAVAR) model is defined by a VAR equation that captures lead-lag correlations amongst a set …

The main challenge for adaptive regulation of linear-quadratic systems is the tradeoff between identification and control. An adaptive …

Identifying change/break points in multivariate time series represents a canonical problem in signal processing, due to numerous …

We consider the problem of estimating the location of a single change point in a network generated by a dynamic stochastic block model …

Identification of input configurations so as to meet a prespecified output target under a limited experimental budget has been an …