description of my research areas

Universal ethical code for scientists (2006) (Council for Science and Technology, UK. CST report)

A. Vellido in dblp , Google Scholar , ResearchGate , ORCID

Research areas Machine Learning and Computational Intelligence. Biomedical, bioinformatics, business, and other applications of ML and CI.

Research projects

Until 2023 I worked, as a main researcher (investigador principal - IP) in KAPPA-AIM, KAPPA-AIM 2 and ML-PROMOLDYN (investigating, using deep learning methods, the molecular dynamics of GPCR proteins) Spanish AEI TIN area research projects in the area of pharmacoproteomics.
From 2023, I am working as IP of eyeAI (Machine Learning-based decision support in ophthalmology from multi-modal retinal images) in the area of Ophthalmology, in collaboration with Dr. Javier Zarranz (Institut Clínic de Oftalmologia (ICOF), Hospital Clínic de Barcelona).

For older projects of the SOCO Research Group, you can find further information here

Member of the ACIA , XARTEC-SALUT, TECSAM and IABIOMED research networks, Also member of the CIBER-BBN, and the IEEE-CIS Data Mining and Big Data Analytics Technical Committee , in which I am Chair of the Explainable Machine Learning (EXML) Task Force and a member of the Task Force on Medical Data Analysis.

Some more past special sessions/workshops in the last decade:
The Coming of Age of Explainable AI (XAI) and Machine Learning, IJCNN 2023, (Queensland, Australia)
Explainable AI in Healthcare (XAI-Healthcare 2023) Workshop, AIME 2023, (Portoroz, Slovenia)
Analysis of Molecular Dynamics Data in Proteomics, IWBBIO 2023, (Gran Canaria, Spain)
ESANN 2021 (virtual event) "Interpretable Models in Machine Learning and Explainable Artificial Intelligence"
IJCNN 2021 (virtual event) "Transparent and Explainable Artificial Intelligence (XAI) for Health"
WCCI/IJCNN 2020 (Glasgow, Scotland, UK) "Explainable Computational/Artificial Intelligence Methods"IJCNN 2019 (Budapest, Hungary) "Explainable Machine Learning"
IDEAL 2019 (Manchester, UK) "Machine Learning in Healthcare"
ESANN 2019 (Bruges, Belgium) "Societal Issues in Machine Learning: When Learning from Data is Not Enough"
WSOM+2019 Conference in Barcelona, Spain (June 26-28, 2019)ESANN 2018 (Bruges, Belgium) "Deep Learning in Bioinformatics and Medicine",
IWBBIO 2018 (Granada, Spain) "Interpretable Models in Biomedicine and Bioinformatics",
IJCNN 2017 (Anchorage, Alaska, USA) "Machine Learning for Enhancing Biomedical Data Analysis", and
NIPS 2017 (Long Beach, CA, USA) "Transparent and interpretable Machine Learning in Safety Critical Environments"

Research students

Already doctors
Iván Olier Variational Bayesian Algorithms for Generative Topographic Mapping and its Extensions. Awarded, Dec'08.
Raúl Cruz Generative Manifold Learning for the Exploration of Partially Labeled Data. Awarded, Sept'09
Carlos Julio Arizmendi Signal Processing Techniques for Brain Tumour Diagnosis from Magnetic Resonance Spectroscopy Data. Awarded, Feb'12

Sandra Ortega-Martorell On the Use of Advanced Pattern Recognition Techniques for the Analysis of MRS and MRSI Data in Neuro-Oncology. Awarded, June'12 (UAB)
Vicent Ribas On the Intelligent Management of Sepsis at the Intensive Care Unit. Awarded January'13
David García Exploration of Customer Churn Routes Using Machine Learning Probabilistic Models. Awarded April'14
Alessandra Tosi Visualization and Interpretability in Probabilistic Dimensionality Reduction Models. Awarded December '14 [ESTEVA-VIVANCO FAMILY PRIZE: BEST PHD THESIS ON ARTIFICIAL INTELLIGENCE (accessit)]
Albert Vilamala Multivariate Methods for Interpretable Analysis of Magnetic Resonance Spectroscopy Data in Brain Tumour Diagnosis. Awarded December '15
Victor Mocioiu Towards an automated analysis pipeline for MRS data: a prototype based on brain tumors. Awarded, July'16 (UAB)
Martha Ivón Cárdenas A Computational Intelligence Analysis of G Protein-Coupled Receptor Sequences for Pharmacoproteomic Applications. Awarded, September'17
Caroline König Analysis of Class CG-Protein Coupled Receptors Using Supervised Classification Methods. Awarded, October'18
Yanisleydis Hernández Desarrollo de Técnicas de Control de Calidad Automatizado para la Mejora de la Clasificación de Tumores Cerebrales a partir de sus Espectros de Resonancia Magnética Usando Convex NMF. Awarded, January'22 (UAB)
Gulnur Ungan On the Use of Machine Learning Techniques for the analysis of MRS and MRSI Data from Brain Tumors. Awarded, November'23 (UAB)
Carla Pitarch Advances towards a robust AI-based glioma grading system. Awarded, September '24
Mario Alberto Gutiérrez Mondragón Exploring the Dynamics of the β2-Adrenergic Receptor: Insights from Explainable AI in GPCR Research. Awarded, September '25

Doctors-to-be

Juan Manuel López Correa (MD data analysis using DL / Proteomics)

MSc Thesis directed

Jorge S. Velazco: The Effect of Noise and Sample Size in the Performance of an Unsupervised Feature Relevance Determination Method for Manifold Learning. Awarded, June'08
Julià Amengual: Advanced Statistical Machine Learning Methods for the Analysis of Neurophysiologic Data with Medical Application. Awarded, June'10
Martha I. Cárdenas: Kernel-Based Manifold Visualization of GPCR Sequences. Awarded, June'11
Àngela Martín: Cartogram Representations of Self-Organizing Virtual Geographies. Awarded, September'13
Stavros Koulas: Using Fuzzy Methods for Rule Extraction in the Discrimination of Class C GPCR Subtypes from their Subsequences . Awarded, September'14
Christiana Halka: Class C GPCR Metabotropic Glutamate Receptor Subtype Discrimination Using Computational Intelligence Methods. Awarded, November'14
Emil Racec: A Stochastic Approach for Automatic Layout Synthesis in Interior Design, Using a Learning-Based Scoring Function. Awarded, July'16
Stefania Budulan: Probabilistic Methods for Furnishing Bedrooms in Interior Design: Bayesian Networks for occurrence Modeling and GMMs for Furnishing Arrangement. Awarded, July'16
Carla Morant: Random Forest-Based Discrimination of G Protein-Coupled Receptors. Awarded, September'16 (Universitat de Vic - UVic)
Carles Morales: Investigating Prognostic Factors in Sepsis Using Computational Intelligence Methods. Awarded, October'16
Ilmira Terpugova Protein Classification from Primary Structures in the Context of Database Biocuraton. Awarded May'17
Elva Sinaí Gutiérrez: A Virtual Reality Mobile App for Exploratory Data Navigation in Data Mining Education. Awarded, July'17
Helen Byrne: Using peptidomics and machine learning techniques to predict mortality of patients with septic shock. Awarded April'18
Ahsan Bilal: Big Data Analytics for Obesity Prediction. Awarded April'18
Alex Aushev: Clinical assessment of Shock through Machine Learning techniques. Awarded April'18
Olga Fetisova: Store layout optimization for a luxury fashion retailer. Awarded June'18
Andrés di Giovanni: Análisis de Datos para la Detección de Anomalías en Procesos de Soldadura, Awarded June'18
Ángel Astudillo: Advanced Models of Visualization of Cosumption Curves for Electricity Supply Companies. Awarded October'18
Xavier Schmoor: Providing Data Support to Health and Social Care Small and Medium-sized Enterprises as part of the Big Data Corridor team at Birmingham City University. Awarded October'18
Daniel Duato: Decoding neural representations of auditory frequency sweeps in the human brain using computational intelligence methods. Awarded October'18
Malika Ibrahimova: Predicting financial distress through Machine Learning. Awarded January'19
Adrián Bazaga: Genome-wide investigation of gene-cancer associations using machine learning on biomedical data for the prediction of novel therapeutic targets Awarded July'19
Rachel Rapp: A framework for Artificial Data Generation Based on Anatomical Differences for Electroencephalography-Based Brain-Computer Interfaces enhancing subject-independent classification by forcing anatomical invariance. Awarded July'19
Nafiseh Banirazimotlagh: Classification of neurodegenerative diseases using AI methods. Awarded October'20
Ricard Meyerhofer Parra: Forecasting football results. Awarded October'20
Damián Rubio Cuervo: Exploratory analysis of the molecular dynamics of Cannabinoid receptor proteins. Awarded October'20
Joel Cantero Priego: Predicting the number of likes on Instagram with TensorFlow. Awarded October'20
Ann Christin Rathert: Prediction of Diabetic Retinopathy (DR), DR Progression and Relationship with Clinical Data using Optical Coherence Tomography Angiography (OCTA), OCT and Retinal Fundus Images Awarded October'21
Guifré Ballester Basols: Text summarization of online hotel reviews with sentiment analysis. Awarded October'21
Anass Benali Bendhamane: Using machine learning on the sources of retinal images for diagnosis by proxy of diabetes mellitus (DM) and diabetic retinopathy (DR). Awarded June'21
Sofía Orfelia Nuñez: Sistema de Recomendación de Canciones. Awarded January'22
Albert Mercadé: Analytical Tools for Retail Marketing. Awarded January'22
Aleix Dalmau: Machine Learning for SaaA Observability and Anomaly Detection. Awarded June'22
Gabriela León: Data visualization and forecasting of a clothing representative sale: A real case from a Brazilian brand. Awarded June'22
Yazmina Zurita: Predicting Diabetic Retinopathy in OCTA Images using a Deep Learning Framework. Awarded January'23
Demokan Çoban: Application of Generative Models on Modeling Biological Molecules. Awarded May'23
Goran Kirov: Comparing Algorithms for Predictive Data Analytics. Awarded June'23
Marta Gil García: Using deep learning to predicting Diabetic Retinopathy in OCTA images adding clinical information. Awarded October'23
Torben Guijarro Heeb: Evaluating quantitative biomarkers in Optical Coherent Tomography Angiography images to predict diabetic retinopathy. Awarded June'24
Marco Rizzo: Assessing the Predictive Capability of Vascular Tortuosity Measures in OCTA Images for Diabetic Retinopathy using Machine Learning Algorithms. Awarded May'24 (ETSEIB-UPC)
Yara Yousef: Emergent behaviour in a Kuramoto coupled oscillator model of the brain. Awarded June'24
Wafaa Guendouz: Exploratory data analysis for medical treatment of psychosis. Awarded June'24
Marta Aicart: Assessing the effect of an antitumor drug from preclinical models through advanced data analysis. Awarded July'24
Levente Zsolt Nagy: Predicting Brain Connectivity Mapping Using Radiomics Features in Anatomical MRI. Awarded January'25
Joao Agostinho Valerio: On the Prediction of Diabetic Retinopathy Progression from OCTA Images Using a Deep Learning Framework. Awarded May'25
Agata Mosinska: Towards Reliable Brain Tumor Segmentation in MRI Neuroimaging: Integrating Uncertainty Estimation and Ensemble Methods for Clinical Applications. Awarded May'25
Javier González Béjar: Advanced Machine Learning Strategies to Estimate the Inflection Point of Response and Relapse in Preclinical GL261 Glioblastoma. Awarded May'25
Daniel Pulido: DeepTrust: Robust Multi-Step Classification with Divergent Learned Representation Models for Android Malware Detection. Awarded July'25
Pau Balanzà: Study of the Robustness of Machine Learning predictions for Diabetes and Cardiovascular Risk among different OCTA Datasets. Awarded October'25

Main research collaborators

Paulo JG Lisboa, Sandra Ortega and Iván OIier (Neural Computation. Liverpool John Moores University. Liverpool, UK)

Wael El-Deredy (The University of Manchester. Manchester, UK / Valparaiso University, Chile / Universitat de Valencia, Spain)

Carles Arús, Margarida Julià-Sapé and Ana Paula Candiota (GABRMN, UAB. Spain)

José D. Martín (UV, Spain)
Vicent Ribas (Eurecat, Spain)

Miguel Hueso (Nephrology, Hospital de Bellvitge, Spain)
Javier Zarranz (Ophthalmology, Hospital Clinic de Barcelona, Spain)

Previous collaborations

    Adriano O Andrade (Biomedical Engineering Laboratory. Federal University of Uberlandia, Brasil)
    Eugenia Martí (CEAB-CSIC. Blanes, Spain) Quim Comas (LEQUIA, Universitat de Girona. Girona, Spain)
    Jesús Giraldo (Systems Pharmacology and Bioinformatics, UAB, Spain)

last updated 05/03/26