Neural computation (Artificial Neural Networks); machine learning; large, high-dimensional, complex data; manifold learning, self-organized learning, clustering and classification, pattern recognition; variable selection, data mining and visualization
Dr. Merényi focuses on understanding the structure of large, complex, high-dimensional data with brain-like computational intelligence techniques. She develops theoretical and simulation tools to discover and express relevant details of relationships in complicated data. Her research is motivated by real problems in Earth and planetary science, astronomy, and medicine. Her collaborative applications are in information extraction from remote sensing hyperspectral imagery, resource mapping, discovery, environmental diagnostics on planetary surfaces; generation of brain maps from functional MRI, analysis of clinical data; and intelligent data compression. She is looking to respond to the next-generation challenge by 21st century astronomical “big data”.