María Pérez-Ortiz

Computer Scientist

I am a researcher in computer science with a passion for machine learning and biomedicine/environmental applications. I am currently a postdoctoral Research Fellow at University College London (UK). For more detailed information, download my CV below.

Contact me:
contact@mariaperezortiz.com

Who am I?

I'm a statistically rare mix of science and literature trying to embrace my juxtapositions. I was brought up between logic puzzles, chess and poetry, which later developed as an avid interest in artificial intelligence. I have been doing research in machine learning for the past 9 years, focusing mostly on ranking, synthetic data generation and image processing. Regarding my applied research I have worked on applications of machine learning to organ transplantation allocation, climate change, sustainable agriculture and more recently, educational recommender systems.

Experience

2018 - Present

Research Fellow

Department of Computer Science & UCL AI Centre, University College London (UK)

2017 - 2018

Research Associate

Department of Computer Science and Technology, University of Cambridge (UK)

2013/2017

Visiting academic

School of Computer Science, University of Birmingham (UK)

2015 - 2017

Lecturer

Department of Quantitative methods, University Loyola Andalucia (Spain)

2014 - 2015

Research assistant

Spanish National Research Council (Spain)

2011 - 2014

Research assistant

Department of Computer Science and Numerical Analysis, University of Cordoba (Spain)

Education

2012 - 2015

PhD in Machine Learning

University of Cordoba (Spain)

2011 - 2012

MSc (Hons) in Intelligent Systems

University of Cordoba (Spain)

2008 - 2011

BSc in Computer Science

University of Cordoba (Spain)

2015 - 2016

Certified Health Coach

Institute of Integrative Nutrition (US)

Other interests

Writing

I love writing and I try to contribute to popular science dissemination in press.

Teaching innovation

I am interested in the use of humour, mindfulness, coaching and gaming in teaching environments. Also, on how to bring transversal topics to the classroom, such as nutrition. Currently, I do student tutoring in AI for a Beijing-based company (ViaX).

Dancing

I have completed the first part of the dance conservatoire, focusing on Ballet and flamenco. Now I am interested in the use of dancing as a mean of meditation and mindfulness.

Health coaching

I hold a master on health coaching from the Institute for Integrative Nutrition in the US.

Selected publications

  • Oversampling the Minority Class in the Feature Space. M. Perez-Ortiz, PA Gutierrez, P. Tino and C. Hervas-Martinez, IEEE Transactions on Neural Networks and Learning Systems, 8, 2015.

  • Graph-based approaches for over-sampling in the context of ordinal regression. M. Perez-Ortiz, PA Gutierrez, C. Hervas-Martinez, X. Yao, IEEE Transactions on Knowledge and Data Engineering 27 (5), 1233-1245, 2015.

  • Projection-Based Ensemble Learning for Ordinal Regression, M. Perez-Ortiz, PA Gutierrez and C. Hervas-Martinez, IEEE Transactions on Cybernetics, 2013.

  • Ordinal regression methods: survey and experimental study. PA Gutiérrez, M. Perez-Ortiz, J. Sanchez-Monedero, F. Fernandez-Navarro and C. Hervas-Martinez, IEEE Transactions on Knowledge and Data Engineering, 28(1), 127-146, 2016.

  • Semi-supervised learning for ordinal Kernel Discriminant Analysis. M. Perez-Ortiz, PA Gutierrez, M. Carbonero-Ruz and C. Hervas-Martinez, Neural Networks 84, 57-66, 2016.

  • A study on multi-scale kernel optimisation via centered kernel-target alignment. M. Perez-Ortiz, PA Gutierrez, J. Sanchez-Monedero and C. Hervas-Martinez, Neural Processing Letters 44 (2), 491-517, 2016.

  • An organ allocation system for liver transplantation based on ordinal regression. M. Perez-Ortiz, M. Cruz-Ramirez, M.D. Ayllon-Teran, N. Heaton, R. Ciria and C. Hervas-Martinez, Applied Soft Computing 14, 88-98, 2014.

  • A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. M. Perez-Ortiz, JM Pena, PA Gutierrez, J. Torres-Sanchez, F. Lopez-Granados and C. Hervas-Martinez, Applied Soft Computing, 37, 533-544, 2015.

  • Synthetic semi-supervised learning in imbalanced domains: Constructing a model for donor-recipient matching in liver transplantation. M. Perez-Ortiz, PA Gutierrez, MD Ayllon-Teran, N. Heaton, R. Ciria and C. Hervas-Martinez, Knowledge-Based Systems 123, 75-87, 2017.

  • On the use of evolutionary time series analysis for segmenting paleoclimate data. M. Perez-Ortiz, A.M. Duran-Rosal, PA Gutierrez, J. Sanchez-Monedero, A. Nikolaou, F. Fernandez-Navarro and C. Hervas-Martinez, Neurocomputing, 2017.

  • Incorporating privileged information to improve manifold ordinal regression. M. Perez-Ortiz, PA Gutierrez and C. Hervas-Martinez, International Conference on Neural Computation Theory and Applications (NCTA), 187-194, 2014.

  • Adapting Linear Discriminant Analysis to the Paradigm of Learning from Label Proportions. M. Perez-Ortiz, PA Gutierrez, M. Carbonero-Ruz and C. Hervas-Martinez, IEEE Symposium Series on Computational Intelligence (IEEE SSCI), 2016.

  • Binary Ranking for Ordinal Class Imbalance. R. Cruz, K. Fernandes, J.F. Pinto-Costa, M. Perez-Ortiz and J.S. Cardoso, Pattern Analysis and Applications, 2018.

  • Partial order label decomposition approaches for melanoma diagnosis. J. Sanchez-Monedero, M. Perez-Ortiz, A. Saez, P.A. Gutierrez and C. Hervas-Martinez, Applied Soft Computing 64, 341-355, 2018.

  • A practical guide and software for analysing pairwise comparison experiments. M. Perez-Ortiz and R. K. Mantiuk. In: arXiv pre-print, 2017, (project page).

  • A mixture of experts model for predicting persistent weather patterns. M. Perez-Ortiz, P. A. Gutierrez, P. Tino, C. Casanova-Mateo and S. Salcedo-Sanz, IEEE International Joint Conference on Neural Networks (IJCNN), 2018.

  • Psychometric scaling of TID2013 dataset. A. Mikhailiuk, M. Perez-Ortiz and R. K. Mantiuk, International Conference on Quality of Multimedia Experience (QoMex), 2018.

  • Trained perceptual transform for quality assessment of high dynamic range images and video. N. Ye, M. Perez-Ortiz and R. K. Mantiuk, International Conference on Image Processing (IEEE ICIP), 2018.

  • Exploiting synthetically generated data with semi-supervised learning for small and imbalanced datasets. M. Perez-Ortiz, P. Tino, R. K. Mantiuk and C. Hervas-Martinez, AAAI Conference on Artificial Intelligence, 2019.

  • ORCA: A Matlab/Octave Toolbox for Ordinal Regression. J. Sanchez-Monedero, P.A. Gutierrez and M. Perez-Ortiz, Journal of Machine Learning Research, 2019.

  • From pairwise comparisons and rating to a unified quality scale. M. Perez-Ortiz, A. Mikhailiuk, E. Zerman, V. Hulusic, G. Valenzise and R. Mantiuk, IEEE Transactions on Image Processing, 2019.

Contact

Address
UCL Computer Science, 66-72 Gower Street
London (UK)
Email
contact@mariaperezortiz.com