Dr Nicolás Hernández

Nicolás Hernández

Lecturer in Statistics

School of Mathematical Sciences
Queen Mary University of London
www.qmul.ac.uk/maths/profiles/hernandezn.html
Centre for Probability, Statistics and Data Science

Research

High Dimensional and Functional Data Analysis, Time Series, Variable Selection, Outlier Detection, Prediction and Classification

Interests

Dr. Hernández is a Lecturer in Statistics within the Data Science, Statistics and Probability Centre at the School of Mathematical Sciences. He joined QMUL after spending 2 years as a Senior Research Fellow within the Institute of Mathematics and Statistical Science at the Department of Statistical Science, UCL. Previously he was appointed as a PDRA at the MRC Biostatistics Unit of the University of Cambridge. Before that he completed his PhD studied about ‘‘Statistical learning methods for functional data with applications to prediction, classification and outlier detection’’ at the Department of Statistics of Universidad Carlos III de Madrid.

His main research is oriented to develop statistical and machine learning methods to tackle inferential problems in high-dimensional and functional data over different fields such as: energy, economics, the environment, demography, business, finance, health and genetics. He has mainly focused on predictive confidence bands for functional time series; domain selection and classification in the Functional Data context; and outlier detection for stochastic processes using Information Theory tools.

Publications

solid heart iconPublications of specific relevance to the Centre for Probability, Statistics and Data Science

2024

bullet iconHernandez N and Martos G (2024). Domain Selection for Gaussian Process Data: An application to electrocardiogram signals. Biometrical Journal, Wiley-VCH Verlag 
28-11-2024
bullet iconHernández N, Cugliari J and Jacques J (2024). Simultaneous predictive bands for functional time series using minimum entropy sets. Communications in Statistics - Simulation and Computation, Taylor & Francis vol. ahead-of-print (ahead-of-print), 1-25.  
23-08-2024

2023

bullet iconHernández N, Muñoz A and Martos G (2023). Density kernel depth for outlier detection in functional data. International Journal of Data Science and Analytics, Springer Nature vol. 16 (4), 481-488.  
04-08-2023

2021

bullet iconHernández N, Soenksen J, Newcombe P, Sandhu M, Barroso I, Wallace C and Asimit JL (2021). The flashfm approach for fine-mapping multiple quantitative traits. Nature Communications, Springer Nature vol. 12 (1) 
22-10-2021

2018

bullet iconMuñoz A, Hernández N, Moguerza JM and Martos G (2018). Combining Entropy Measures for Anomaly Detection. Entropy, MDPI vol. 20 (9) 
12-09-2018
bullet iconMartos G, Hernández N, Muñoz A and Moguerza JM (2018). Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection. Entropy, MDPI vol. 20 (1) 
11-01-2018

2016

bullet iconHernandez N and Muñoz A (2016). Kernel Depth Measures for Functional Data with Application to Outlier Detection. Artificial Neural Networks and Machine Learning – ICANN 2016 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II , Editors: Villa AEP, Masulli P and Pons Rivero AJ. 
26-08-2016