Dr Angelica Aviles Rivero | Faculty of Mathematics

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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

Single-Shot Plug-and-Play Methods for Inverse Problems

Y Cheng, L Zhang, Z Shen, S Wang, L Yu, RH Chan, C-B Schönlieb, AI Aviles-Rivero

(2023)

(link to publication)

TRIDENT: The Nonlinear Trilogy for Implicit Neural Representations

Z Shen, Y Cheng, RH Chan, P Liò, C-B Schönlieb, AI Aviles-Rivero

(2023)

(link to publication)

Contrastive Registration for Unsupervised Medical Image Segmentation

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

– IEEE transactions on neural networks and learning systems

(2023)

PP,

1

(doi: 10.1109/TNNLS.2023.3332003)

Traffic Video Object Detection using Motion Prior

L Liu, Y Cheng, D Chen, J He, P Liò, C-B Schönlieb, AI Aviles-Rivero

(2023)

(link to publication)

Learning Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation

J Zou, N Debroux, L Liu, J Qin, C-B Schönlieb, AI Aviles-Rivero

(2023)

(link to publication)

Continuous U-Net: Faster, Greater and Noiseless

C-W Cheng, C Runkel, L Liu, RH Chan, C-B Schönlieb, AI Aviles-Rivero

(2023)

(link to publication)

TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation

Z Deng, Y Chen, L Liu, S Wang, R Ke, C-B Schonlieb, AI Aviles-Rivero

(2022)

(link to publication)

NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning

Z Deng, R Ke, C-B Schonlieb, AI Aviles-Rivero

(2022)

(link to publication)

SCOTCH and SODA: A Transformer Video Shadow Detection Framework

L Liu, J Prost, L Zhu, N Papadakis, P Liò, C-B Schönlieb, AI Aviles-Rivero

(2022)

(doi: 10.48550/arxiv.2211.06885)

(link to publication)

LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semisupervised Classification.

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

– IEEE Transactions on Neural Networks and Learning Systems

(2022)

PP,

1

(doi: 10.1109/tnnls.2022.3203315)