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T. Mitchell Roddenberry - IEEE Xplore Author Profile
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T. Mitchell Roddenberry

Publications
16
Citations
120
Publications by Year
20192023
Co-Authors:
Show All Co-Authors (17)
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T. Mitchell Roddenberry

Affiliation

Department of Electrical and Computer Engineering
Rice University
Houston, TX, USA

Publication Topics

Biography

T. Mitchell Roddenberry (Student Member, IEEE) received the B.Sc. degree (with highest honor) in computer engineering from Florida State University, Tallahassee, FL, USA, in 2018. He is currently working toward the Ph.D. degree in electrical and computer engineering at Rice University, Houston, TX, USA. His research interests include signal processing, network science, and optimization.(Based on document published on 30 June 2021).
Publications
16
Citations
120
Publications by Year
20192023
Co-Authors:
Show All Co-Authors (17)
Author's Published Works

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Enhanced Graph-Learning Schemes Driven by Similar Distributions of Motifs

Samuel Rey;T. Mitchell Roddenberry;Santiago Segarra;Antonio G. Marques

IEEE Transactions on Signal Processing
Year: 2023 | Volume: 71 | Journal Article |
Cited by: Papers (8)
This paper looks at the task of network topology inference, where the goal is to learn an unknown graph from nodal observations. One of the novelties of the approach put forth is the consideration of prior information about the density of motifs of the unknown graph to enhance the inference of classical Gaussian graphical models. Directly dealing with the density of motifs constitutes a challengin...Show More

Enhanced Graph-Learning Schemes Driven by Similar Distributions of Motifs

Samuel Rey;T. Mitchell Roddenberry;Santiago Segarra;Antonio G. Marques

Year: 2023 | Volume: 71 | Journal Article |

Windowed Fourier Analysis for Signal Processing on Graph Bundles

T. Mitchell Roddenberry;Santiago Segarra

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
We consider the task of representing signals supported on graph bundles, which are generalizations of product graphs that allow for "twists" in the product structure. Leveraging the localized product structure of a graph bundle, we demonstrate how a suitable partition of unity over the base graph can be used to lift the signal on the graph into a space where a product factorization can be readily ...Show More

Windowed Fourier Analysis for Signal Processing on Graph Bundles

T. Mitchell Roddenberry;Santiago Segarra

Year: 2023 | Conference Paper |

Signal Processing On Product Spaces

T. Mitchell Roddenberry;Vincent P. Grande;Florian Frantzen;Michael T. Schaub;Santiago Segarra

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
We establish a framework for signal processing on product spaces of simplicial and cellular complexes. For simplicity, we focus on the product of two complexes representing time and space, although our results generalize naturally to products of simplicial complexes of arbitrary dimension. Our framework leverages the structure of the eigenmodes of the Hodge Laplacian of the product space to jointl...Show More

Signal Processing On Product Spaces

T. Mitchell Roddenberry;Vincent P. Grande;Florian Frantzen;Michael T. Schaub;Santiago Segarra

Year: 2023 | Conference Paper |

On Local Distributions in Graph Signal Processing

T. Mitchell Roddenberry;Fernando Gama;Richard G. Baraniuk;Santiago Segarra

IEEE Transactions on Signal Processing
Year: 2022 | Volume: 70 | Journal Article |
Cited by: Papers (8)
Graph filtering is the cornerstone operation in graph signal processing (GSP). Thus, understanding it is key in developing potent GSP methods. Graph filters are local and distributed linear operations, whose output depends only on the local neighborhood of each node. Moreover, a graph filter's output can be computed separately at each node by carrying out repeated exchanges with immediate neighbor...Show More

On Local Distributions in Graph Signal Processing

T. Mitchell Roddenberry;Fernando Gama;Richard G. Baraniuk;Santiago Segarra

Year: 2022 | Volume: 70 | Journal Article |

Hodgelets: Localized Spectral Representations of Flows On Simplicial Complexes

T. Mitchell Roddenberry;Florian Frantzen;Michael T. Schaub;Santiago Segarra

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2022 | Conference Paper |
Cited by: Papers (10)
We develop wavelet representations for edge-flows on simplicial complexes, using ideas rooted in combinatorial Hodge theory and spectral graph wavelets. We first show that the Hodge Laplacian can be used in lieu of the graph Laplacian to construct a family of wavelets for higher-order signals on simplicial complexes. Then, we refine this idea to construct wavelets that respect the Hodge-Helmholtz ...Show More

Hodgelets: Localized Spectral Representations of Flows On Simplicial Complexes

T. Mitchell Roddenberry;Florian Frantzen;Michael T. Schaub;Santiago Segarra

Year: 2022 | Conference Paper |

Signal Processing On Cell Complexes

T. Mitchell Roddenberry;Michael T. Schaub;Mustafa Hajij

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2022 | Conference Paper |
Cited by: Papers (18)
The processing of signals supported on non-Euclidean domains has attracted large interest recently. Thus far, such non-Euclidean domains have been abstracted primarily as graphs with signals supported on the nodes, though the processing of signals on more general structures such as simplicial complexes has also been considered. In this paper, we give an introduction to signal processing on (abstra...Show More

Signal Processing On Cell Complexes

T. Mitchell Roddenberry;Michael T. Schaub;Mustafa Hajij

Year: 2022 | Conference Paper |

Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering

Michael Weylandt;T. Mitchell Roddenberry;Genevera I. Allen

2021 IEEE Data Science and Learning Workshop (DSLW)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
Clustering is a ubiquitous problem in data science and signal processing. In many applications where we observe noisy signals, it is common practice to first denoise the data, perhaps using wavelet denoising, and then to apply a clustering algorithm. In this paper, we develop a sparse convex wavelet clustering approach that simultaneously denoises and discovers groups. Our approach utilizes convex...Show More

Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering

Michael Weylandt;T. Mitchell Roddenberry;Genevera I. Allen

Year: 2021 | Conference Paper |

Sparse Partial Least Squares for Coarse Noisy Graph Alignment

Michael Weylandt;George Michailidis;T. Mitchell Roddenberry

2021 IEEE Statistical Signal Processing Workshop (SSP)
Year: 2021 | Conference Paper |
Graph signal processing (GSP) provides a powerful framework for analyzing signals arising in a variety of domains. In many applications of GSP, multiple network structures are available, each of which captures different aspects of the same underlying phenomenon. To integrate these different data sources, graph alignment techniques attempt to find the best correspondence between vertices of two gra...Show More

Sparse Partial Least Squares for Coarse Noisy Graph Alignment

Michael Weylandt;George Michailidis;T. Mitchell Roddenberry

Year: 2021 | Conference Paper |

Blind Inference of Eigenvector Centrality Rankings

T. Mitchell Roddenberry;Santiago Segarra

IEEE Transactions on Signal Processing
Year: 2021 | Volume: 69 | Journal Article |
Cited by: Papers (10)
We consider the problem of estimating a network's eigenvector centrality only from data on the nodes, with no information about network topology. Leveraging the versatility of graph filters to model network processes, data supported on the nodes is modeled as a graph signal obtained via the output of a graph filter applied to white noise. We seek to simplify the downstream task of centrality ranki...Show More

Blind Inference of Eigenvector Centrality Rankings

T. Mitchell Roddenberry;Santiago Segarra

Year: 2021 | Volume: 69 | Journal Article |

Network Topology Change-Point Detection from Graph Signals with Prior Spectral Signatures

Chiraag Kaushik;T. Mitchell Roddenberry;Santiago Segarra

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021 | Conference Paper |
Cited by: Papers (4)
We consider the problem of sequential graph topology change-point detection from graph signals. We assume that signals on the nodes of the graph are regularized by the underlying graph structure via a graph filtering model, which we then leverage to distill the graph topology change-point detection problem to a subspace detection problem. We demonstrate how prior information on the spectral signat...Show More

Network Topology Change-Point Detection from Graph Signals with Prior Spectral Signatures

Chiraag Kaushik;T. Mitchell Roddenberry;Santiago Segarra

Year: 2021 | Conference Paper |

Network Topology Inference with Graphon Spectral Penalties

T. Mitchell Roddenberry;Madeline Navarro;Santiago Segarra

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021 | Conference Paper |
Cited by: Papers (9)
We consider the problem of inferring the unobserved edges of a graph from data supported on its nodes. In line with existing approaches, we propose a convex program for recovering a graph Laplacian that is approximately diagonalizable by a set of eigenvectors obtained from the second-order moment of the observed data. Unlike existing work, we incorporate prior knowledge about the distribution from...Show More

Network Topology Inference with Graphon Spectral Penalties

T. Mitchell Roddenberry;Madeline Navarro;Santiago Segarra

Year: 2021 | Conference Paper |

Blind Estimation of Eigenvector Centrality from Graph Signals: Beyond Low-pass Filtering

T. Mitchell Roddenberry;Santiago Segarra

2020 54th Asilomar Conference on Signals, Systems, and Computers
Year: 2020 | Conference Paper |
Cited by: Papers (1)
This paper characterizes the difficulty of estimating a network's eigenvector centrality only from data on the nodes, i.e., with no information about the topology of the network. We model this nodal data as graph signals generated by passing white noise through generic (not necessarily low-pass) graph filters. Leveraging the spectral properties of graph filters, we estimate the eigenvectors of the...Show More

Blind Estimation of Eigenvector Centrality from Graph Signals: Beyond Low-pass Filtering

T. Mitchell Roddenberry;Santiago Segarra

Year: 2020 | Conference Paper |

Exact Blind Community Detection From Signals on Multiple Graphs

T. Mitchell Roddenberry;Michael T. Schaub;Hoi-To Wai;Santiago Segarra

IEEE Transactions on Signal Processing
Year: 2020 | Volume: 68 | Journal Article |
Cited by: Papers (14)
Networks and data supported on graphs have become ubiquitous in the sciences and engineering. This paper studies the `blind' community detection problem, where we seek to infer the community structure of a graph model given the observation of independent graph signals on a set of nodes whose connections are unknown. We model each observation as filtered white noise, where the underlying network st...Show More

Exact Blind Community Detection From Signals on Multiple Graphs

T. Mitchell Roddenberry;Michael T. Schaub;Hoi-To Wai;Santiago Segarra

Year: 2020 | Volume: 68 | Journal Article |

Metric Representations of Networks: A Uniqueness Result

Santiago Segarra;T. Mitchell Roddenberry;Facundo Mémoli;Alejandro Ribeiro

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2020 | Conference Paper |
In this paper, we consider the problem of projecting networks onto metric spaces. Networks are structures that encode relationships between pairs of elements or nodes. However, these relationships can be independent of each other, and need not be defined for every pair of nodes. This is in contrast to a metric space, which requires that a distance between every pair of elements in the space be def...Show More

Metric Representations of Networks: A Uniqueness Result

Santiago Segarra;T. Mitchell Roddenberry;Facundo Mémoli;Alejandro Ribeiro

Year: 2020 | Conference Paper |

Blind Inference of Centrality Rankings from Graph Signals

T. Mitchell Roddenberry;Santiago Segarra

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2020 | Conference Paper |
Cited by: Papers (8)
We study the blind centrality ranking problem, where our goal is to infer the eigenvector centrality ranking of nodes solely from nodal observations, i.e., without information about the topology of the network. We formalize these nodal observations as graph signals and model them as the outputs of a network process on the underlying (unobserved) network. A simple spectral algorithm is proposed to ...Show More

Blind Inference of Centrality Rankings from Graph Signals

T. Mitchell Roddenberry;Santiago Segarra

Year: 2020 | Conference Paper |

HodgeNet: Graph Neural Networks for Edge Data

T. Mitchell Roddenberry;Santiago Segarra

2019 53rd Asilomar Conference on Signals, Systems, and Computers
Year: 2019 | Conference Paper |
Cited by: Papers (26)
Networks and network processes have emerged as powerful tools for modeling social interactions, disease propagation, and a variety of additional dynamics driven by relational structures. Recently, neural networks have been generalized to process data on graphs, thus being able to learn from the aforementioned network processes achieving cutting-edge performance in traditional tasks such as node cl...Show More

HodgeNet: Graph Neural Networks for Edge Data

T. Mitchell Roddenberry;Santiago Segarra

Year: 2019 | Conference Paper |

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