@inproceedings {42847,
	title = {Simplicial Vector Autoregressive Model for Streaming Edge Flows},
	journal = {2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023)},
	year = {2023},
	note = {Matlab Codes:\&nbsp;https://github.com/Joshinpk/Simplicial_VAR_Model},
	month = {05/2023},
	publisher = {IEEE},
	abstract = {Vector autoregressive (VAR) model is widely used to model time-varying processes, but it suffers from prohibitive growth of the parameters when the number of time series exceeds a few hundreds. We propose a simplicial VAR model to mitigate the curse of dimensionality of the VAR models when the time series are defined over higher-order network structures such as edges, triangles, etc. The proposed model shares parameters across the simplicial signals by leveraging the simplicial convolutional filter and captures structure-aware spatio-temporal dependencies of the time-varying processes. Targetting the streaming signals from the real-world nonstationary networks, we develop a group-lasso-based online strategy to learn the proposed model. Using traffic and water distribution networks, we demonstrate that the proposed model achieves competitive signal prediction accuracy with a significantly less number of parameters than the VAR models.ICASSP 2023 has identified this paper as part of the exclusive group, ranking it within the top 3\% of all accepted papers.},
	isbn = {978-1-7281-6328-4},
	doi = {10.1109/ICASSP49357.2023.10096095},
	url = {https://ieeexplore.ieee.org/document/10096095},
	author = {Krishnan, Joshin and Money, Rohan and Beferull-Lozano, Baltasar and Isufi, Elvin}
}
@article {28392,
	title = {Sparse Online Learning with Kernels using Random Features for Estimating Nonlinear Dynamic Graphs},
	journal = {IEEE Transactions on Signal Processing},
	volume = {71},
	year = {2023},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.\&nbsp;},
	month = {06/2023},
	pages = {2027-2042},
	publisher = {IEEE},
	abstract = {Online topology estimation of graph-connected time series is challenging in practice, particularly because the dependencies between the time series in many real-world scenarios are nonlinear. To address this challenge, we introduce a novel kernel-based algorithm for online graph topology estimation. Our proposed algorithm also performs a Fourier-based random feature approximation to tackle the curse of dimensionality associated with kernel representations. Exploiting the fact that real-world networks often exhibit sparse topologies, we propose a group-Lasso based optimization framework, which is solved using an iterative composite objective mirror descent method, yielding an online algorithm with fixed computational complexity per iteration. We provide theoretical guarantees for our algorithm and prove that it can achieve sublinear dynamic regret under certain reasonable assumptions. In experiments conducted on both real and synthetic data, our method outperforms existing state-of-the-art competitors.},
	issn = {1941-0476},
	doi = {10.1109/TSP.2023.3282068},
	url = {https://ieeexplore.ieee.org/document/10141675},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar}
}
@article {42575,
	title = {Online Edge Flow Imputation on Networks},
	journal = {IEEE Signal Processing Letters},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	publisher = {IEEE},
	abstract = {A novel online algorithm for missing data imputation for networks with signals defined on the edges is presented in this paper. Leveraging the prior knowledge intrinsic to most real-world networks, we propose a bi-level optimization scheme that includes (i) a sparse line graph identification strategy by solving a group-Lasso-based optimization framework via composite objective mirror descent to exploit the causal dependencies among the signals and (ii) a Kalman filtering-based signal reconstruction strategy developed using simplicial complex (SC) formulation to exploit the flow conservation. To the best of our knowledge, this is the first SC-based attempt for time-varying signal imputation, whose advantages have been demonstrated through numerical experiments conducted using EPANET models of both synthetic and real water distribution networks.},
	doi = {10.1109/LSP.2022.3221846},
	url = {https://ieeexplore.ieee.org/document/9947283},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar and Isufi, Elvin}
}
@inproceedings {42572,
	title = {Online Joint Nonlinear Topology Identification and Missing Data Imputation over Dynamic Graphs},
	journal = {30th European Signal Processing Conference (EUSIPCO)},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	publisher = { IEEE},
	abstract = {Extracting causal graph structures from multivariate time series, termed topology identification, is a fundamental problem in network science with several important applications. Topology identification is a challenging problem in real-world sensor networks, especially when the available time series are partially observed due to faulty communication links or sensor failures. The problem becomes even more challenging when the sensor dependencies are nonlinear and nonstationary. This paper proposes a kernel-based online framework using random feature approximation to jointly estimate nonlinear causal dependencies and missing data from partial observations of streaming graph-connected time series. Exploiting the fact that real-world networks often exhibit sparse topologies, we propose a group lasso-based optimization framework for topology identification, which is solved online using alternating minimization techniques. The ability of the algorithm is illustrated using several numerical experiments conducted using both synthetic and real data.},
	doi = {10.23919/EUSIPCO55093.2022.9909681},
	url = {https://ieeexplore.ieee.org/document/9909681},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar}
}
@article {42845,
	title = {Scalable and Privacy-aware Online Learning of Nonlinear Structural Equation Models},
	journal = {IEEE Open Journal of Signal Processing},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70\&nbsp; from the Research Council of Norway.},
	publisher = {IEEE},
	abstract = {An online topology estimation algorithm for nonlinear structural equation models (SEM) is proposed in this paper, addressing the nonlinearity and the non-stationarity of real-world systems. The nonlinearity is modeled using kernel formulations, and the curse of dimensionality associated with the kernels is mitigated using random feature approximation. The online learning strategy uses a group-lasso-based optimization framework with a prediction-corrections technique that accounts for the model evolution. \E{The proposed approach has three properties of interest. First, it enjoys node-separable learning, which allows for scalability in large networks. Second, it offers privacy in SEM learning by replacing the actual data with node-specific random features. Third, its performance can be characterized theoretically via a dynamic regret analysis, showing that it is possible to obtain a linear dynamic regret bound under mild assumptions. Numerical results with synthetic and real data corroborate our findings and show competitive performance w.r.t. state-of-the-art alternatives.},
	doi = {10.1109/OJSP.2023.3241580},
	url = {https://ieeexplore.ieee.org/document/10034854},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar and Isufi, Elvin}
}
@article {28390,
	title = {Cyber-Physical Systems for Smart Water Networks: A Review},
	journal = {IEEE Sensors Journal},
	volume = {21},
	year = {2021},
	note = {This research work was carried out at University of Agder with funding from the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway, and completed after the SIGIPRO Department was created.},
	month = {Jan-12-2021},
	pages = {26447 - 26469},
	publisher = { IEEE},
	abstract = {There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.},
	issn = {1530-437X},
	doi = {10.1109/JSEN.2021.3121506},
	url = {https://ieeexplore.ieee.org/document/9580916},
	author = {Bhardwaj, Jyotirmoy and Krishnan, Joshin and Diego F. Larios {Marin} and Beferull-Lozano, Baltasar and Linga Reddy {Cenkeramaddi} and Harman, Christopher}
}
@inproceedings {28389,
	title = {Data-Driven Pump Scheduling for Cost Minimization in Water Networks},
	journal = {2021 IEEE International Conference on Autonomous Systems (ICAS)2021 IEEE International Conference on Autonomous Systems (ICAS)},
	year = {2021},
	note = {This work was carried out at University of Agder with the funding from the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway.},
	month = {08/2021},
	publisher = {IEEE},
	address = {Montreal, QC, Canada},
	abstract = {Pumps consume a significant amount of energy in a water distribution network (WDN). With the emergence of dynamic energy cost, the pump scheduling as per user demand is a computationally challenging task. Computing the decision variables of pump scheduling relies over mixed integer optimization (MIO) formulations. However, MIO formulations are NP-hard in general and solving such problems is inefficient in terms of computation time and memory. Moreover, the computational complexity of solving such MIO formulations increases exponentially with the size of the WDN. As an alternative, we propose a data-driven approach to estimate the decision variables of pump scheduling using deep neural networks (DNN). We evaluate the performance of our trained DNN relative to a state-of-the-art MIO solver, and conclude that our DNN based approach can be used to minimize the pump switching and cost incurred due to dynamic energy in a given WDN with much lower complexity.},
	doi = {10.1109/ICAS49788.2021.9551168},
	url = {https://ieeexplore.ieee.org/document/9551168},
	author = {Bhardwaj, Jyotirmoy and Krishnan, Joshin and Beferull-Lozano, Baltasar}
}
@inproceedings {28388,
	title = {Online Non-linear Topology Identification from Graph-connected Time Series},
	journal = {2021 IEEE Data Science and Learning Workshop (DSLW)2021 IEEE Data Science and Learning Workshop (DSLW)},
	year = {2021},
	note = {This work was carried out at University of Agder with the funding from the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	month = {June/2021},
	publisher = {IEEE},
	address = {Toronto, ON, Canada},
	abstract = {Estimating the unknown causal dependencies among graph-connected time series plays an important role in many applications, such as sensor network analysis, signal processing over cyber-physical systems, and financial engineering. Inference of such causal dependencies, often known as topology identification, is not well studied for non-linear non-stationary systems, and most of the existing methods are batch-based which are not capable of handling streaming sensor signals. In this paper, we propose an online kernel-based algorithm for topology estimation of non-linear vector autoregressive time series by solving a sparse online optimization framework using the composite objective mirror descent method. Experiments conducted on real and synthetic data sets show that the proposed algorithm outperforms the state-of-the-art methods for topology estimation.},
	doi = {10.1109/DSLW51110.2021.9523399},
	url = {https://ieeexplore.ieee.org/document/9523399},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar}
}
@inproceedings {28391,
	title = {Random Feature Approximation for Online Nonlinear Graph Topology Identification},
	journal = {2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP) 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)},
	year = {2021},
	note = {This work was carried out at University of Agder with the funding from the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	month = {10/2021},
	publisher = {IEEE},
	address = {Gold Coast, Australia},
	abstract = {Online topology estimation of graph-connected time series is challenging, especially since the causal dependencies in many real-world networks are nonlinear. In this paper, we propose a kernel-based algorithm for graph topology estimation. The algorithm uses a Fourier-based Random feature approximation to tackle the curse of dimensionality associated with the kernel representations. Exploiting the fact that the real-world networks often exhibit sparse topologies, we propose a group lasso based optimization framework, which is solved using an iterative composite objective mirror descent method, yielding an online algorithm with fixed computational complexity per iteration. The experiments conducted on real and synthetic data show that the proposed method outperforms its competitors.},
	doi = {10.1109/MLSP52302.2021.9596512},
	url = {https://ieeexplore.ieee.org/document/9596512},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar}
}
