Dr Angelica Aviles Rivero

My research lies at the intersection of computational mathematics and machine learning for applications to large-scale real world problems. My central research is to develop new data-driven algorithmic techniques that allow computers to gain high-level understanding from vast amounts of data, this, with the aim of aiding the decisions of users. These methods are based on mathematical modelling and machine learning methods.

Keywords: $\bigstar$ Applied Mathematics $\bigstar$ Computational Mathematics $\bigstar$ Inverse problems $\bigstar$ Image Analysis $\bigstar$ Graph Learning $\bigstar$ Machine Learning.

Publications

Learning optical flow for fast MRI reconstruction

T Schmoderer, AI Aviles-Rivero, V Corona, N Debroux, C-B Schönlieb

– Inverse Problems

(2021)

37,

095007

(doi: 10.1088/1361-6420/ac164a)

Delving Into Deep Walkers: A Convergence Analysis of Random-Walk-Based Vertex Embeddings

D Kloepfer, AI Aviles-Rivero, D Heydecker

(2021)

(link to publication)

LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semi-Supervised Classification

P Sellars, AI Aviles-Rivero, C-B Schönlieb

– IEEE Transactions on Neural Networks and Learning Systems 2022

(2021)

(link to publication)

Dynamic spectral residual superpixels

J Zhang, AI Aviles-Rivero, D Heydecker, X Zhuang, R Chan, C-B Schönlieb

– Pattern Recognition

(2021)

112,

107705

(doi: 10.1016/j.patcog.2020.107705)

Variational multi-task MRI reconstruction: Joint reconstruction, registration and super-resolution.

V Corona, AI Aviles-Rivero, N Debroux, CL Guyader, C-B Schönlieb

– Med Image Anal

(2020)

68,

101941

(doi: 10.1016/j.media.2020.101941)

Rethinking medical image reconstruction via shape prior, going deeper and faster: Deep joint indirect registration and reconstruction

J Liu, AI Aviles-Rivero, H Ji, C-B Schönlieb

– Medical Image Analysis

(2020)

68,

101930

(doi: 10.1016/j.media.2020.101930)

Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction.

AI Aviles-Rivero, N Debroux, G Williams, MJ Graves, C-B Schönlieb

– Med Image Anal

(2020)

abs/1810.10828,

101933

(doi: 10.1016/j.media.2020.101933)

Contrastive Registration for Unsupervised Medical Image Segmentation

L Liu, AI Aviles-Rivero, C-B Schönlieb

(2020)

(link to publication)

The GraphNet Zoo: An All-in-One Graph Based Deep Semi-supervised Framework for Medical Image Classification

M de Vriendt, P Sellars, AI Aviles-Rivero

– UNCERTAINTY FOR SAFE UTILIZATION OF MACHINE LEARNING IN MEDICAL IMAGING, AND GRAPHS IN BIOMEDICAL IMAGE ANALYSIS, UNSURE 2020, GRAIL 2020

(2020)

12443 LNCS,

187

(doi: 10.1007/978-3-030-60365-6_18)

Controllable Image Processing via Adaptive FilterBank Pyramid

D Chen, Q Fan, J Liao, A Aviles-Rivero, L Yuan, N Yu, G Hua

– IEEE Transactions on Image Processing

(2020)

29,

8043

(doi: 10.1109/tip.2020.3009844)

Department of Applied Mathematics and Theoretical Physics

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