|
|
|
Graduate in Mathematics (2010) and Computer Science (2010) at Universidad Autónoma de Madrid. MsC on Mathematics and applications at Universidad Autónoma de Madrid (2011). PhD in Mathematics (2015) at Universidad de Castilla-La Mancha. His research interests are discrete mathematics, machine learning, image processing and their applications to Mathematical Biology. |
Publications
-
Tackling the rank aggregation problem with evolutionary algorithms
Applied Mathematics and Computation 222, 632-644 (2013)
J.A. Aledo, J.A. Gámez, D. Molina
Projects
- Establishing a non-invasive approach to accurately diagnose and assess brain tumors
Canadian Cancer Society (2024 - 2027) - Improving New Therapies in Oncology and Related Fields using mathematical models and biomedical data
Ministry of Science and Innovation (National Research Plan) (2023 - 2026) - Mathematical models for the digital transition in neuro-oncology: In-silico design of a clinical trial for glioblastoma.
Ministerio de Ciencia e Innovación (NextGenerationEU) (2023 - 2024) - Validation and transfer of oncological data and biomarkers based on mathematical models (AFTERbioMATH)
Ministry of Science and Innovation (Spain), Proof of Concept Program (2023 - 2024) - Mathematical models in Oncology
Ministerio de Ciencia e Innovación (Spain) (2020 - 2023)
Molina García, David
|
Graduate in Mathematics (2010) and Computer Science (2010) at Universidad Autónoma de Madrid. MsC on Mathematics and applications at Universidad Autónoma de Madrid (2011). PhD in Mathematics (2015) at Universidad de Castilla-La Mancha. His research interests are discrete mathematics, machine learning, image processing and their applications to Mathematical Biology. |
Publications
- Tackling the rank aggregation problem with evolutionary algorithms
Applied Mathematics and Computation 222, 632-644 (2013)
J.A. Aledo, J.A. Gámez, D. Molina
Projects
- Establishing a non-invasive approach to accurately diagnose and assess brain tumors
Canadian Cancer Society (2024 - 2027) - Improving New Therapies in Oncology and Related Fields using mathematical models and biomedical data
Ministry of Science and Innovation (National Research Plan) (2023 - 2026) - Mathematical models for the digital transition in neuro-oncology: In-silico design of a clinical trial for glioblastoma.
Ministerio de Ciencia e Innovación (NextGenerationEU) (2023 - 2024) - Validation and transfer of oncological data and biomarkers based on mathematical models (AFTERbioMATH)
Ministry of Science and Innovation (Spain), Proof of Concept Program (2023 - 2024) - Mathematical models in Oncology
Ministerio de Ciencia e Innovación (Spain) (2020 - 2023)
