Yeni Plasencia Calaña (Y.)
Yenisel Plasencia Calaña is a researcher working at BISS and IDS in the fields of data science and artificial intelligence. She is a computer scientist graduated from Havana University and she completed her PhD degree in machine learning and artificial intelligence in TUDelft in the Netherlands.
Her main research interests include the study of data representations, which contribute towards more responsible artificial intelligence considering aspects such as reusability, transparency, explainability and fairness. She is also interested in scalable machine learning and in the different trade-offs when learning for small, moderate and large datasets. Her current projects include the development of FAIR data architectures, and the use of artificial intelligence for social good. Her work is inspired by the following research questions:
How to achieve transparency and interpretability for the different machine learning approaches and models? Related to this question also: How the dissimilarity representation for pattern recognition may benefit the interpretability of machine learning models.
How can we benefit from reusable data to perform data science analysis that provide insights with direct impact to social good?
How to develop machine learning and data science approaches for small data? Especially how to design transfer learning approaches for different types of small data, previously known as small sample size problems (SSS).
How to design machine learning strategies useful in limited computational resources or rephrased differently how to create computationally efficient machine learning?