Modeling and simulation
- Mechanical or Structural.
- Thermal or thermodynamics.
- Fluid: gases, liquids.
- Fluid-structure interaction.
- Acoustic or Vibroacoustic.
- Multiphysical phenomena.
- Valuation of financial products and their risks, portfolio management.
Statistics and Big Data
- Statistical advice and data analysis.
- Time series forecasting.
- Production of maps from spatial data.
- Environmental and energy statistics.
- Tourism statistics.
- Customer, market and product studies.
- Risk and financial analysis.
- Design of experiments, clinical trials.
- Biostatistics, epidemiology.
- Applications in the field of health.
- Quality control and reliability.
- Production, process and stock control.
- Statistical applications for industry or public administrations.
- Statistical analysis of Big Data with incomplete information (censoring, truncation, etc.) or with sampling biases.
- Optimization of processes.
- Stocks optimization.
- Resources optimization.
- Optimization in the location of resources.
- Planning of transport routes.
- Work planning.
- Support for optimal decision-making.
- Optimization of industrial and business processes.
- Optimisation of inversions and investment portfolios.
- Development and implementation of numerical optimisation methods with HPC tools.
- Automatic learning.
- Neural networks.
- Deep learning.
- Machine learning.
- Hybrid models based on data and physics.
- Development of digital twins.
- Programming in scientific languages: Fortran, C, C++, Python, Matlab, R.
- Development of software packages.
- Development of graphic interfaces.
- Implementation of commercial and free software packages.
- Parallelization of algorithms.
- Use of commercial software packages.
- Use of free software packages.
- Programming on GPUs.
- High performance computing.
- Data modelling / Databases.
- Quantum computing.
Areas of experience:
Challenge 1: Health
- Visualisation, processing and analysis of medical images. Characterisation of false positives. Learning models using large databases (machine/deep learning).
- Characterisation of medical waste. Classification algorithms for biological samples.
- Evolution of demographic rates. Population projections and generation of scenario trees over a time horizon.
- Characterisation of population habits. Simulation of user flow by services.
- Optimisation of the planning of the location of primary health centres in uncertain environments of the evolution of demographic rates over a time horizon.
- Optimisation of the planning of the acquisition of health material in uncertain environments of the evolution of demographic rates and types and intensity of diseases and epidemics over a time horizon.
- Optimisation of ambulance fleet types: Sizing and location in an uncertain service demand environment.
- Optimization of the distribution of health resources, both human and material, in emergency situations.
- Epidemiological models, genetic regulatory networks.
Challenge 3: Energy
- Generation of demand uncertainty scenarios for the various types of energy for domestic and industrial uses.
- Optimization of the planning of the expansion capacity of energy generating elements and their transmission.
- Optimization of electricity generation in industrial and residential buildings for self-consumption and its connection to the distribution network.
- Support for optimal decision-making in energy processes.
- Energy price evolution models.
- Valuation of derivatives in energy markets.
- Numerical simulation of heat transfer processes and combustion processes.
- Numerical simulation of thermoelectric, thermomagnetic and thermomechanical processes.
Challenge 5: Environment
- Simulation, optimization and control of production and distribution processes.
- Modeling and simulation of forest fires.
- Simulation, prediction and impact of natural disasters such as floods and earthquakes.
- Optimisation of irrigation planning for agricultural and livestock purposes.
- Numerical simulation of rivers and estuaries.
Estimation of the amount of impurities from water and solids in aviation fuels from ISO codes.
An original mathematical approach was proposed based on the use of historical data for fuels to which known proportions of water or solid impurities had been added. The proportions of each of the two types of impurities were estimated using maximum likelihood methods.
The developed software was delivered, resulting in a publication in a scientific journal. This is a collaboration with the company PECOFacet Ibérica, part of the CLARCOR group.
Detection of failed areas on crankshafts using digital image analysis and supervised statistical classification methods
Partial least squares (PLS) and principal component analysis (PCA) methods were used to transform the original feature matrix. Then, multivariate supervised classification statistical methods were applied to the vector database to determine whether an area of a crankshaft is defective.
The developed software was delivered, resulting in a publication in a scientific journal. It is a collaboration with the company CIE Galfor.
Optimal allocation of samples to laboratory plates
The developed algorithm, simPCR, has been integrated into the NextLims laboratory software. This software automatically manages all the tasks related to Sanger and NGS sequencing techniques.
The software developed, which is the simPCR Library (library for optimising the filling of PCR plates in the Sanger sequencing process), was registered. It is a collaboration with the company Health in Code.
European Industrial Doctorate WEAKUPCALL
This is an industrial doctorate from the 2014 MCSA-ITN-EID call, developed in the period 2015-2018. The subject matter is in the field of computational finance. It is a collaboration between groups from the universities of Delft, Bologna and A Coruña, the CWI centre in Amsterdam and Dutch, Italian and Spanish companies. Two doctoral theses were supervised by the UDC in collaboration with Banco Santander and Analistas Financieros Internacionales.
European Industrial Doctorate ABC-EU-XVA
This is an industrial doctorate of the 2018 MCSA-ITN-EID call, developed in the period from November 2018 to October 2022. The subject matter is in the field of valuation adjustments for financial derivatives. It is a collaboration between groups from the universities of Delft, Free Brussels, Bologna and A Coruña, the CWI centre in Amsterdam and Dutch, Belgian, Italian and Spanish companies. Two doctoral theses have been developed at the UDC in collaboration with Unipol SAI and Rabobank/EY.