198 citations · 450 across the 17 of their papers we have counts for
18 papers
TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series
Yang Jiao, Kai Yang, Shaoyu Dou +3
Multivariate time series (MTS) data are becoming increasingly ubiquitous in diverse domains, e.g., IoT systems, health informatics, and 5G networks. To obtain an effective represen…
Optimal Sensor Collaboration for Parameter Tracking Using Energy Harvesting Sensors
Shan Zhang, Sijia Liu, Vinod Sharma +1
In this paper, we design an optimal sensor collaboration strategy among neighboring nodes while tracking a time-varying parameter using wireless sensor networks in the presence of…
First-order bifurcation detection for dynamic complex networks
Sijia Liu, Pin-Yu Chen, Indika Rajapakse +1
In this paper, we explore how network centrality and network entropy can be used to identify a bifurcation network event. A bifurcation often occurs when a network undergoes a qual…
Algorithm-Hardware Co-Optimization of the Memristor-Based Framework for Solving SOCP and Homogeneous QCQP Problems
Ao Ren, Sijia Liu, Ruizhe Cai +3
A memristor crossbar, which is constructed with memristor devices, has the unique ability to change and memorize the state of each of its memristor elements. It also has other high…
A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks
Farshad Harirchi, Omar A. Khalil, Sijia Liu +3
In this paper, we propose an optimization-based sparse learning approach to identify the set of most influential reactions in a chemical reaction network. This reduced set of react…
A New Data-Driven Sparse-Learning Approach to Study Chemical Reaction Networks
Farshad Harirchi, Doohyun Kim, Omar A. Khalil +4
Chemical kinetic mechanisms can be represented by sets of elementary reactions that are easily translated into mathematical terms using physicochemical relationships. The schematic…
Semiblind subgraph reconstruction in Gaussian graphical models
Tianpei Xie, Sijia Liu, Alfred O. Hero
Consider a social network where only a few nodes (agents) have meaningful interactions in the sense that the conditional dependency graph over node attribute variables (behaviors)…
A Memristor-Based Optimization Framework for AI Applications
Sijia Liu, Yanzhi Wang, Makan Fardad +1
Memristors have recently received significant attention as ubiquitous device-level components for building a novel generation of computing systems. These devices have many promisin…
Bias-Variance Tradeoff of Graph Laplacian Regularizer
Pin-Yu Chen, Sijia Liu
This paper presents a bias-variance tradeoff of graph Laplacian regularizer, which is widely used in graph signal processing and semi-supervised learning tasks. The scaling law of…
Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity
Sijia Liu, Pin-Yu Chen, Alfred O. Hero
We consider the problem of accelerating distributed optimization in multi-agent networks by sequentially adding edges. Specifically, we extend the distributed dual averaging (DDA)…
Learning Sparse Graphs Under Smoothness Prior
Sundeep Prabhakar Chepuri, Sijia Liu, Geert Leus +1
In this paper, we are interested in learning the underlying graph structure behind training data. Solving this basic problem is essential to carry out any graph signal processing o…
Optimized Sensor Collaboration for Estimation of Temporally Correlated Parameters
Sijia Liu, Swarnendu Kar, Makan Fardad +1
In this paper, we aim to design the optimal sensor collaboration strategy for the estimation of time-varying parameters, where collaboration refers to the act of sharing measuremen…
Sensor Selection for Estimation with Correlated Measurement Noise
Sijia Liu, Sundeep Prabhakar Chepuri, Makan Fardad +3
In this paper, we consider the problem of sensor selection for parameter estimation with correlated measurement noise. We seek optimal sensor activations by formulating an optimiza…
Joint Sparsity Pattern Recovery with 1-bit Compressive Sensing in Sensor Networks
Vipul Gupta, Bhavya Kailkhura, Thakshila Wimalajeewa +1
We study the problem of jointly sparse support recovery with 1-bit compressive measurements in a sensor network. Sensors are assumed to observe sparse signals having the same but u…
Measurement Matrix Design for Compressive Detection with Secrecy Guarantees
Bhavya Kailkhura, Sijia Liu, Thakshila Wimalajeewa +1
In this letter, we consider the problem of detecting a high dimensional signal based on compressed measurements with physical layer secrecy guarantees. We assume that the network o…
Sparsity-Aware Sensor Collaboration for Linear Coherent Estimation
Sijia Liu, Swarnendu Kar, Makan Fardad +1
In the context of distributed estimation, we consider the problem of sensor collaboration, which refers to the act of sharing measurements with neighboring sensors prior to transmi…
Optimal Periodic Sensor Scheduling in Networks of Dynamical Systems
Sijia Liu, Makan Fardad, Engin Masazade +1
We consider the problem of finding optimal time-periodic sensor schedules for estimating the state of discrete-time dynamical systems. We assume that {multiple} sensors have been d…
Adaptive Non-myopic Quantizer Design for Target Tracking in Wireless Sensor Networks
Sijia Liu, Engin Masazade, Xiaojing Shen +1
In this paper, we investigate the problem of nonmyopic (multi-step ahead) quantizer design for target tracking using a wireless sensor network. Adopting the alternative conditional…