Statistical Methods and Big Data applied to Economy, Tourism and other Social Sciences
Scientic Supervisor / Contact Person
Name and Surname
Adolfo Hernández
ORCID (link)
Researcher ID (link)
Other ID (link)
Localization & Research Area
Faculty / Institute
Faculty of Commerce and Tourism
Department
Financial & Actuarial Economics & Statistics
Research Area
Social Sciences and Humanities (SOC)
MSCA & ERC experience
Research group / research team hosted any MSCA fellow?
No
Research group / research team have any ERC beneficiaries?
No
Research Team & Research Topic
Research Team / Research Group Name (if any)
Statistical Methods and Big Data applied to Economy, Tourism and other Social Sciences
Website of the Research team / Research Group / Department
Brief description of the Research Team / Research Group / Department
Research questions:
Is it possible to carry out Machine Learning and Big Data methods to analyse phenomena in the fields of economics, tourism, and education?
How do we reap the full efficiency of quantitative methods in modelling and prediction of different phenomena?
Which are the factors that have the biggest influence in the academic efficiency and what type of relevant information can be taken from the virtual environments in order to detect an early dropout?
How could quantitative techniques be implemented for the analysis of touristic sector?
Which new factors and methods could be applied in the study and analysis of social science?
Is it possible to carry out Machine Learning and Big Data methods to analyse phenomena in the fields of economics, tourism, and education?
How do we reap the full efficiency of quantitative methods in modelling and prediction of different phenomena?
Which are the factors that have the biggest influence in the academic efficiency and what type of relevant information can be taken from the virtual environments in order to detect an early dropout?
How could quantitative techniques be implemented for the analysis of touristic sector?
Which new factors and methods could be applied in the study and analysis of social science?
Research lines / projects proposed
Since the team was created, the participation in 2 projects from the Spanish National Plan for Scientific Research stands out. The main researcher of both projects is the main researcher of the team. In the last few years, funding has been obtained through two calls of the UCM-Santander Programme (2017 and 2020) and, recently (September 2021), through the Ministry of Science and Innovation via the 2020 call of «R&D&I Projects». Doctor Adolfo Hernández is the main researcher of these projects and most of the members of the group are part of the research team of these projects.. In addition, all members have participated in several projects with national competitive funding, including 14 national projects from the “Spanish National R&D&I Plan” and 5 projects from other public institutions (Community of Madrid, Generalitat de Catalunya, etc.).
LINE OF RESEARCH TO BE DEVELOPED
RESEARCH LINE: Data Science and Its Applications to the Social Sciences
This research line aims to harness advanced data science tools—such as machine learning,
big data analytics, and statistical methodologies—to address complex and multidisciplinary
social phenomena. By integrating descriptive, explanatory, and predictive approaches, it
seeks to deepen the understanding of these phenomena and generate actionable insights in
areas such as education, public health, economics, and other social sciences. Ultimately, the
goal is to enhance decision-making processes and contribute to the development of
evidence-based public policies, aligning with Spain's educational priorities and the EU's
Digital Strategy.
The OBJECTIVES are as follow:
a. Methodological Advancements: Develop and refine innovative data science
methodologies to improve the accuracy, interpretability, and scalability of predictive
and descriptive models. This includes integrating novel algorithms and optimization
techniques tailored to complex social datasets.
b. Application to Social Phenomena: Investigate key societal challenges—such as
academic performance disparities, school dropout rates, public health risks, and labor
market dynamics—through multidisciplinary approaches that combine data science
with domain-specific expertise.
c. Development of Practical Tools: Design adaptive and accessible tools, such as
personalized virtual assistants, geographic information systems, and decision-support
systems, to empower policymakers, educators, and public health officials in
addressing inequalities and optimizing resource allocation.
ALIGNMENT WITH TRAJECTORY, STRATEGIC PRIORITIES, AND EXPECTED IMPACT:
This research line builds on my expertise in predictive modeling and statistical methodologies
applied to education and public health while expanding to broader socio-economic
phenomena through a multidisciplinary lens. It is aligned with Spain's efforts to address
educational disparities, reduce school dropout rates, and increase STEM participation,
particularly among underrepresented groups. In the public health domain, it aligns with
priorities in predictive disease modeling and resource optimization in healthcare systems.
Additionally, this line supports the EU's Digital Education Action Plan and Digital Strategy by
leveraging big data and machine learning to enable digital transformation in educational
institutions and public policy frameworks. The tools developed will promote equitable access
to education and enhance resource allocation efficiency through evidence-based
approaches. Expected outcomes include high-impact publications in Q1 and Q2 journals,
actionable frameworks for policymakers, and scalable tools for decision-making. The
research will strengthen international collaborations, enhancing Spain’s role in global
research networks and contributing to digital and social innovation at both national and
European levels.
LINE OF RESEARCH TO BE DEVELOPED
RESEARCH LINE: Data Science and Its Applications to the Social Sciences
This research line aims to harness advanced data science tools—such as machine learning,
big data analytics, and statistical methodologies—to address complex and multidisciplinary
social phenomena. By integrating descriptive, explanatory, and predictive approaches, it
seeks to deepen the understanding of these phenomena and generate actionable insights in
areas such as education, public health, economics, and other social sciences. Ultimately, the
goal is to enhance decision-making processes and contribute to the development of
evidence-based public policies, aligning with Spain's educational priorities and the EU's
Digital Strategy.
The OBJECTIVES are as follow:
a. Methodological Advancements: Develop and refine innovative data science
methodologies to improve the accuracy, interpretability, and scalability of predictive
and descriptive models. This includes integrating novel algorithms and optimization
techniques tailored to complex social datasets.
b. Application to Social Phenomena: Investigate key societal challenges—such as
academic performance disparities, school dropout rates, public health risks, and labor
market dynamics—through multidisciplinary approaches that combine data science
with domain-specific expertise.
c. Development of Practical Tools: Design adaptive and accessible tools, such as
personalized virtual assistants, geographic information systems, and decision-support
systems, to empower policymakers, educators, and public health officials in
addressing inequalities and optimizing resource allocation.
ALIGNMENT WITH TRAJECTORY, STRATEGIC PRIORITIES, AND EXPECTED IMPACT:
This research line builds on my expertise in predictive modeling and statistical methodologies
applied to education and public health while expanding to broader socio-economic
phenomena through a multidisciplinary lens. It is aligned with Spain's efforts to address
educational disparities, reduce school dropout rates, and increase STEM participation,
particularly among underrepresented groups. In the public health domain, it aligns with
priorities in predictive disease modeling and resource optimization in healthcare systems.
Additionally, this line supports the EU's Digital Education Action Plan and Digital Strategy by
leveraging big data and machine learning to enable digital transformation in educational
institutions and public policy frameworks. The tools developed will promote equitable access
to education and enhance resource allocation efficiency through evidence-based
approaches. Expected outcomes include high-impact publications in Q1 and Q2 journals,
actionable frameworks for policymakers, and scalable tools for decision-making. The
research will strengthen international collaborations, enhancing Spain’s role in global
research networks and contributing to digital and social innovation at both national and
European levels.
Key words
Application requirements
Professional Experience & Documents
Degree/PhD in Mathematics/Statistics. Desirable second degree in some social science area (Education, Economics or similar).
Participant in several research projects resulting in high-impact, open-access
publications with significant citation indices. Most of these publications should focus on developing
advanced predictive models based on machine learning and multivariate statistics applied to
diverse contexts, such as education, public health, and model performance improvement.
Strong international collaboration.
Active and essential role, contributing to conceptualization, methodological design, result
analysis, and manuscript writing, advancing knowledge and promoting societal impact.
Please submit CV (including all relevant publications) and letter of motivation.
Participant in several research projects resulting in high-impact, open-access
publications with significant citation indices. Most of these publications should focus on developing
advanced predictive models based on machine learning and multivariate statistics applied to
diverse contexts, such as education, public health, and model performance improvement.
Strong international collaboration.
Active and essential role, contributing to conceptualization, methodological design, result
analysis, and manuscript writing, advancing knowledge and promoting societal impact.
Please submit CV (including all relevant publications) and letter of motivation.
One Page Proposal
You can attach the 'One Page Proposal' to enhance the attractiveness of your application. Supervisors usually appreciate it. Please take into account your background and the information provided in Research Team & Research Topic section to fill in it.
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