Manos Papadakis is an associate professor in the University of Houston's Department of Mathematics. He works on developing tools for harnessing latent information in 3D image data sets using wavelets and other multiscale representations combined with sophisticated classification algorithms.

His methods have applications in cardiovascular imaging and neuroscience,  where complicated structures such as the walls of coronary arteries or tiny neuronal structures need to be imaged. Visualizing the walls of coronary arteries in CT-Angiography will help clinicians to pinpoint suspect regions -- those likely to cause infarctions or those developing atherosclerotic plaque -- without the use of invasive diagnostic methods. Rapid structural reconstructions of neurons will enable the 3D-visualization of the neuronal function and lead to a better understanding of how single neurons or small groups of similar neurons work, which is a crucial first step for understanding the most complicated structure, the human brain.

Papadakis is a member of the Center for Mathematical Biosciences, which is poised to become the world's leading center for integrating advanced mathematics with medical research. He collaborates with several bioscience mathematicians in the UH Mathematics Department and with other UH faculty members from the Department of Computer Science and from the Texas Learning and Computation Center at the University of Houston.

Visit the homepage of Dr. Papadakis to learn more.

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