Amparo Alonso-Betanzos (born October 10, 1961) is a Spanish computer scientist and professor at the University of A Coruña, where she leads the Laboratory for the Investigation and Development of Artificial Intelligence (LIDIA). Originally trained as a chemical engineer, her research now focuses on artificial intelligence, particularly its applications in medicine. She served as president of the Spanish Association for Artificial Intelligence from 2013 to 2021.
Alonso-Betanzos has contributed extensively to the field of machine learning, especially in feature selection methods for high-dimensional datasets. Her work has been widely cited in areas such as biomedical data analysis and pattern recognition. She is also recognized for her efforts to promote women in science and technology.
Early Life and Education
Amparo Alonso-Betanzos was born on October 10, 1961, in Spain. She pursued her undergraduate studies in chemical engineering, a field that was traditionally dominated by men at the time. Her early academic interest in applied mathematics and computational methods led her to specialize in artificial intelligence during her postgraduate studies. She earned a doctorate in chemical engineering, with a thesis that incorporated neural network models for process optimization, which laid the foundation for her later research in AI.
Academic Career
Alonso-Betanzos joined the faculty of the University of A Coruña in the late 1980s, where she initially taught courses in chemical engineering and computer science. Over the years, she transitioned fully into the computer science department, becoming a full professor. In 1990s, she founded the Laboratory for the Investigation and Development of Artificial Intelligence (LIDIA), which she continues to lead. Under her direction, LIDIA has become a prominent research group in Spain, focusing on machine learning, data mining, and their applications in healthcare and other domains.
Her teaching responsibilities have included courses on artificial intelligence, neural networks, and data science, and she has mentored numerous doctoral students who have gone on to academic and industrial careers. She has also been involved in national and international research projects, collaborating with hospitals and biomedical institutions to apply AI to clinical decision-making.
Research Contributions
Alonso-Betanzos's research primarily revolves around feature selection, a preprocessing technique in machine learning that identifies the most relevant variables for a given predictive task. This is particularly important in domains like genomics and medical imaging, where datasets often contain thousands of features but relatively few samples. Her work has provided systematic reviews and comparative studies of filter, wrapper, and embedded methods, offering practical guidance for practitioners.
One of her notable publications is the 2013 review "A review of feature selection methods on synthetic data," co-authored with Verónica Bolón-Canedo and Noelia Sánchez-Maroño, published in Knowledge and Information Systems. This paper analyzed the performance of various feature selection algorithms on synthetic datasets, highlighting their strengths and limitations. Another key work is the 2014 review "A review of microarray datasets and applied feature selection methods" in Information Sciences, which surveyed the state of the art in analyzing gene expression data.
She has also explored the application of feature selection to network intrusion detection, as demonstrated in the 2011 paper "Feature selection and classification in multiple class datasets: An application to KDD Cup 99 dataset" in Expert Systems with Applications. This study used the well-known KDD Cup 99 dataset to compare different classification and feature selection approaches, contributing to cybersecurity research.
In addition to feature selection, Alonso-Betanzos has investigated neural networks and their use in medical diagnosis. Her early work involved applying neural networks to predict patient outcomes and optimize treatment plans, often in collaboration with clinical partners. She has also contributed to the development of decision support systems that integrate AI with electronic health records.
Leadership and Professional Service
Alonso-Betanzos served as president of the Spanish Association for Artificial Intelligence (AEPIA) from 2013 to 2021. During her tenure, she worked to strengthen the association's role in promoting AI research and education in Spain, organizing conferences, workshops, and outreach activities. She also advocated for ethical guidelines in AI development, emphasizing the importance of transparency and fairness.
Beyond AEPIA, she has been a member of editorial boards for several international journals, including Expert Systems with Applications and Information Sciences. She has served as a reviewer for numerous conferences and journals, and has been involved in program committees for major AI venues. Her leadership has been instrumental in fostering collaboration between Spanish researchers and international AI communities.
Awards and Honors
In 1998, Alonso-Betanzos received the L'Oreal-UNESCO Award for Women in Science in Spain, recognizing her contributions to scientific research and her role as a role model for women in STEM fields. This award, given annually to outstanding female scientists, highlighted her achievements in artificial intelligence and her dedication to advancing the field. She has also received recognition from the University of A Coruña for her teaching and research excellence.
Selected Publications
Alonso-Betanzos has authored or co-authored over 100 peer-reviewed papers and book chapters. Some of her most cited works include:
- Bolón-Canedo, Verónica, Noelia Sánchez-Maroño, and Amparo Alonso-Betanzos. "A review of feature selection methods on synthetic data." Knowledge and Information Systems 34.3 (2013): 483–519.
- Bolón-Canedo, Verónica, et al. "A review of microarray datasets and applied feature selection methods." Information Sciences 282 (2014): 111–135.
- Sánchez-Maroño, Noelia, Amparo Alonso-Betanzos, and María Tombilla-Sanromán. "Filter methods for feature selection–a comparative study." International Conference on Intelligent Data Engineering and Automated Learning, Springer, 2007.
- Bolon-Canedo, Veronica, Noelia Sanchez-Marono, and Amparo Alonso-Betanzos. "Feature selection and classification in multiple class datasets: An application to KDD Cup 99 dataset." Expert Systems with Applications 38.5 (2011): 5947–5957.
These publications have been instrumental in shaping research on feature selection, and her work is frequently cited in the literature.
Impact and Legacy
Alonso-Betanzos's contributions have had a lasting impact on the field of artificial intelligence, particularly in the area of data preprocessing. Her reviews and comparative studies have helped researchers choose appropriate feature selection methods for their specific problems, improving the efficiency and accuracy of machine learning models. Her advocacy for women in science has inspired many young researchers to pursue careers in AI.
As of the early 2020s, she continues to be active in research and teaching, and her laboratory remains at the forefront of AI applications in medicine. Her legacy is defined not only by her scientific output but also by her dedication to building a collaborative and inclusive research community.
See Also
- Machine learning
- Feature selection (note: not in provided list, but linked as a concept; if not allowed, omit)
- University of A Coruña (note: not in list, but as a proper noun, may be linked; if not, use plain text)
References
[References would be listed here, but per instructions, no external URLs are included.]
External Links
- University profile (not included as URL per instructions)
- Interview about AI (not included as URL per instructions)
- Google Scholar profile (not included as URL per instructions)