BIOSTATECH

PhD in Biology and Master in Biostatistics from the USC, with studies in Medicine (UNAV and USC).

She has extensive research experience at the USC (Marine Invertebrates Laboratory and Biostatistics Unit for Medicine) (2004-2010), and at the Pyrenean Institute of Ecology (CSIC) (2010-2014). Head of the Biostatech Advisory Area since 2015.

High knowledge and experience in the application of multiple statistical techniques and tools. Specialist in advanced regression models (GAM), STAR models and extensions, spatial statistics, mixed models, meta-analysis, and in statistics applied to business (big data, complex data, predictive models and time series). Programming in R.

Graduate in Physics from the Complutense University of Madrid. Master in Current Techniques in Applied Statistics (UNED) and University Master in Renewable Energies (CEU-USP).
Programmer at Almis I.F., researcher at the Max Planck Institute Mainz (Germany) (2012-2014), and scientific-technical manager of the Statistical Consultancy at the University of Castilla-La Mancha (2015-2017). She began her activity at Biostatech in April 2017.
She has leadership and experience in statistical advice to scientists and companies, and training of researchers in the university, health and business fields; programming in R and Matlab; application of Machine Learning in the marketing and innovation environment for companies.

PhD in Biology (UVigo). Master’s Degree in Statistical Techniques (UDC, USC and UVigo).

Extensive research experience in the development of multiple projects with different institutions. Initially, he completed internships in a company at Isidro de la Cal (statistical quality control), and developed a statistical modelling project at the Office of the Environment (UDC). Collaborating researcher at the Institut de Ciències del Mar (ICM-CSIC) (Barcelona), and associated researcher in a European project developed by the Observatorie Océanologique de Banyuls (France). Ministerial predoctoral contract at UVigo (2014-2017).

Experience in teaching, and specialist in multiple advanced statistical methodologies, supervised classification, image analysis and mathematical simulations, among others.

High level in programming: R, Matlab, Python.

Degree in Genetics (Autonomous University of Barcelona), Master in Biomedical Research (USC) and Master in Bioinformatics for Health Sciences (UDC). She has been selected with one of the highest scores in the second call for Industrial Doctorates of the Xunta de Galicia. Through her doctoral thesis she will address the discovery of new biomarkers through data obtained using proteomic analysis technologies.

Previous experience in internships at the Center for Animal Biotechnology and Gene Therapy (CBATEG) and in research at the Santiago de Compostela Health Research Institute (IDIS).

High level in programming (R, Python, among others), bioinformatics and biostatistics.

Degree in Mathematics (University of Oviedo) and Master in Statistical Techniques (UDC). In his Master’s thesis “Changes in serum N-glycome in cancer patients” he has specialized in compositional data, although his mathematical training specialized in statistics allows him to adapt to a wide variety of methodologies.

Advanced level of programming in R and handling of Matlab.

Degree in Biology from the University of Vigo and student of the Master in Bioinformatics for Health Sciences (UDC). In his Master’s thesis “Radiomics to Predict Radiotherapy-Induced Toxicity in Lung Cancer”, he specialized in the extraction and processing of clinical image data.

Previous experience in research internships at the Galician Public Foundation of Genomic Medicine (FPGMX).

Advanced level in programming (Python, R, Bash, among others) and bioinformatics.

Bachelor’s degree in Mathematics (USC) and Master’s degree in Statistical Techniques (USC). In her Master’s thesis, «Analysis of the Effect of E-Consultation in the Cardiology Service of the Health Area of ​​Santiago de Compostela and Barbanza,» she specialized in generalized additive models (GAM) and survival analysis.
She has an advanced level of programming in R and knowledge of Matlab, AMPL, and Python.

Investigo Program (Subsidies intended to finance the hiring of young job seekers to carry out research and innovation initiatives)(TR349V)

Bachelor’s Degree in Mathematics from the University of Santiago de Compostela (USC) and Master’s Degree in Statistical Techniques (USC). His academic and applied trajectory has focused on biostatistics, through his Bachelor’s thesis titled “ROC curves and their application in Biomedicina”, and on complex predictive modeling, addressing in his Master’s thesis the analysis of transport costs and the impact of fuel prices on Inditex’s logistics network using GAMM models.

He brings practical experience from NTT DATA and Inditex, participating in big data management and integration projects, data cleaning, and statistical modeling. Additionally, he possesses an advanced level of proficiency in R and SQL, along with working knowledge of Python, MATLAB, AMPL, and Power BI.

Investigo Program (Grants aimed at funding the hiring of young job seekers to carry out research and innovation initiatives).

Degree in Biotechnology (Polytechnic University of Valencia). Master’s Degree in Biostatistics (University of Valencia).

He has been selected with one of the highest scores in the first call for Industrial Doctorates of the Xunta de Galicia. His doctoral thesis focuses on the modelling of longitudinal and survival data from a Bayesian perspective.

Specialist in data mining and machine learning, with a high level of programming in R, Python and SAS, as well as having experience in the field of Bioinformatics and in the analysis of omics data.

PhD in Mathematics from the Complutense University of Madrid. She currently works as a quantitative analyst in the Quantitative & Business Solutions team at BBVA Bancomer and completed her doctoral thesis in Mathematics in collaboration with the CSIC (Institute of Fundamental Physics) and the Complutense University of Madrid on mathematical methods in quantum information.