WEBSITE(S)| Dr. Merényi's website

Dr. Merényi focuses on understanding the structure of large, complex, high-dimensional data with neural 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; discovery from 21st century astronomical “big data”.

Research Areas

Neural machine learning (Artificial Neural Networks); large, high-dimensional, complex data; manifold learning, self-organized learning, clustering and classification, pattern recognition; variable selection, data mining and visualization.

Industry Impact & Relevance

Automatic multi-class target recognition in highly cluttered background; Image classification with reduced set of training labels


Ph.D. Computational Science, Szeged (Attila József) University, Hungary

M.Sc. Mathematics, Szeged (Attila József) University, Hungary

Advisory Role

Ph.D. and Postdoctoral mentor

Senior Fellow, Rice University Academy of Fellows

College Faculty Associate

Teaching Areas

Neural Machine Learning

Regression and Linear Models

Probability and Random Processes

Hyperspectral Image Analysis

Societies & Organizations

Institute of Electrical and Electronics Engineers (IEEE)

Neural Network Society (INNS)

International Astronomical Union

American Society For Photogrammetry and Remote Sensing

Division of Planetary Science, American Astronomical Society


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