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Networks

Networks are all around us. As such, network science is crucial for our understanding of many applications of high societal relevance (e.g., social and technological networks, epidemics, biological networks). Our research focuses on developing new machine learning methods to discover complex interactions and collective behaviors that determine how various types of events and behaviors in social networks are generated and propagated. In particular, we are interested in developing new approaches for social sensing that are relevant to the immediate concerns around pandemic detection and mitigation.

networks image

epidemics

Epidemics

Viral outbreaks spread throughout networks of people via transmission events. We aim to combine human mobility data, network science, and machine learning to inform and mitigate the disease dynamics for COVID-19. Furthermore, we aim to build an always-on social sensing system to improve a population’s resilience to a novel virus.

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social networks

Social Networks

Objective social media exhibit rich yet distinct temporal dynamics which cover a wide range of different scales. We are able to identify the compositional structures that can accurately characterize the complex social dynamics from these two social media. We further show that identifying these patterns can enable new applications such as anomaly detection and improved social dynamics forecasting. We aim to uncover new insights on understanding and engineering social media dynamics and their consequences on offline behaviors.

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biological networks

Biological Networks

It is well established that bacteria engage in social behavior and form networked communities via molecular signaling. We analyze the network dynamics and biofilm metrics, showing that our method can effectively reveal the underlying intercellular communication process and community organization within the biofilm. We claim that the application of social and network sciences to understanding bacteria population dynamics can aid in developing better drugs to control the many pathogenic bacteria that use social interactions to cause infections.

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Selected Publications

51 entries « ‹ 1 of 9 › »

Krishnakumar, Anish; Marculescu, Radu; Ogras, Umit Y

INDENT: Incremental Online Decision Tree Training for Domain-Specific Systems-on-Chip Inproceedings

In: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pp. 1-9, 2022.

Links

@inproceedings{nokey,
title = {INDENT: Incremental Online Decision Tree Training for Domain-Specific Systems-on-Chip},
author = {Krishnakumar, Anish and Marculescu, Radu and Ogras, Umit Y},
url = {https://dl.acm.org/doi/abs/10.1145/3508352.3549436},
year = {2022},
date = {2022-10-30},
urldate = {2022-10-30},
booktitle = {Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design (ICCAD)},
pages = {1-9},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

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  • https://dl.acm.org/doi/abs/10.1145/3508352.3549436

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Hurtado, Sofia; Marculescu, Radu; Drake, Justin

Quarantine in Motion: a Graph Learning Framework to Reduce Disease Transmission without Lockdown Inproceedings Forthcoming

In: Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2022), Forthcoming.

@inproceedings{Hurtado2021Q,
title = {Quarantine in Motion: a Graph Learning Framework to Reduce Disease Transmission without Lockdown},
author = {Hurtado, Sofia and Marculescu, Radu and Drake, Justin},
year = {2022},
date = {2022-10-06},
urldate = {2022-10-06},
booktitle = {Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2022)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}

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Hurtado, Sofia; Marculescu, Radu; Drake, Justin; Srinivasan, Ravi

Pruning Digital Contact Networks for Meso-scale Epidemics Surveillance Using Foursquare Data Inproceedings

In: ACM/IEEE Intl. Conf. on Advances in Social Network Analysis and Mining (ASONAM), 2021.

Links

@inproceedings{Hurtado2021b,
title = {Pruning Digital Contact Networks for Meso-scale Epidemics Surveillance Using Foursquare Data},
author = {Hurtado, Sofia and Marculescu, Radu and Drake, Justin and Srinivasan, Ravi },
url = {https://www.medrxiv.org/content/10.1101/2021.09.29.21264175v1.full.pdf},
year = {2021},
date = {2021-11-11},
urldate = {2021-11-11},
booktitle = {ACM/IEEE Intl. Conf. on Advances in Social Network Analysis and Mining (ASONAM)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

Close

  • https://www.medrxiv.org/content/10.1101/2021.09.29.21264175v1.full.pdf

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Davis, Brian; Bhatt, Umang; Bhardwaj, Kartikeya; Marculescu, Radu; Moura, José MF

On network science and mutual information for explaining deep neural networks Inproceedings

In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8399–8403, IEEE 2020.

Links

@inproceedings{davis2020network,
title = {On network science and mutual information for explaining deep neural networks},
author = {Brian Davis and Umang Bhatt and Kartikeya Bhardwaj and Radu Marculescu and Jos\'{e} MF Moura},
url = {https://arxiv.org/abs/1901.08557},
year = {2020},
date = {2020-01-01},
booktitle = {ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {8399--8403},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

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  • https://arxiv.org/abs/1901.08557

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Topirceanu, Alexandru; Udrescu, Mihai; Marculescu, Radu

Centralized and decentralized isolation strategies and their impact on the COVID-19 pandemic dynamics Journal Article

In: arXiv preprint arXiv:2004.04222, 2020.

Links

@article{topirceanu2020centralized,
title = {Centralized and decentralized isolation strategies and their impact on the COVID-19 pandemic dynamics},
author = {Alexandru Topirceanu and Mihai Udrescu and Radu Marculescu},
url = {https://arxiv.org/pdf/2004.04222},
year = {2020},
date = {2020-01-01},
journal = {arXiv preprint arXiv:2004.04222},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

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  • https://arxiv.org/pdf/2004.04222

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Lo, Chieh; Marculescu, Radu

MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks Journal Article

In: Bmc Bioinformatics, vol. 20, no. 12, pp. 314, 2019.

Links

@article{lo2019metann,
title = {MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks},
author = {Chieh Lo and Radu Marculescu},
url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2833-2},
year = {2019},
date = {2019-01-01},
journal = {Bmc Bioinformatics},
volume = {20},
number = {12},
pages = {314},
publisher = {BioMed Central},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

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  • https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2833-2

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51 entries « ‹ 1 of 9 › »

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Contact

Prof. Radu Marculescu
System Level Design Group
Electrical and Computer Engineering
The University of Texas at Austin
radum@utexas.edu

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