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School of Mathematical Sciences

Dr Nicolás Hernández


Lecturer in Statistics

Room Number: Mathematical Sciences Building, Room: MB-125
Office Hours: Please email for an appointment


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. 


Research Interests:

Functional Data Analysis

  • Causal Inference
  • Domain selection
  • Two sample tests
  • Extreme Value Analysis
  • MCMC methods for functional regression
  • Simultaneous inference for Functional Time Series
  • Depth measures
  • Outlier detection 

High-Dimensional Data Analysis

  • Variable selection
  • Uncertainty Quantification
  • Fine-mapping of genetic variants


  • Chemometrics - NIR Spectral Data
  • Environment - Tsunami Data, Extreme value data, NOx contamination levels.
  • Electrocardiogram Signals 
  • Genomic data
  • Economic, Business and Social data analysis - Energy consumption data. 


Hernández, N., et al. ”Density kernel depth for outlier detection in functional data.” Int J Data Sci Anal (2023).

Hernández, N., et al. ”The flashfm approach for fine-mapping multiple quantitative traits.” Nature Communications 12.1 (2021): 1-14. 

Martos, G., Hernández, N., Muñoz, A. & Moguerza, J. M. (2018). “Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection”. Entropy, 20(1), 33. 

Muñoz, A., Hernández, N., Moguerza, J. M. & Martos, G. (2018). “Combining entropy measures for anomaly detection”. Entropy, 20(9), 698.

Hernández, N., A. Muñoz. (2016). “Kernel Depth Measures for Functional Data with Application to Outlier Detection”. Lecture Notes in Computer Science, vol 9887, pp 235-242

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