**Biography.** Massimo Zanetti received the M.S. degree in Mathematics (summa cum laude) from the University of Ferrara, (Ferrara, Italy),
discussing a Thesis on "Variational Methods for Image Segmentation: the Blake-Zisserman model".
Currently, he is working toward the Ph.D. degree in the Remote Sensing Laboratory, Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.
His research interests include change detection, multispectral images analysis, variational methods for segmentation, and numerical optimization.
He is a reviewer for international journals, among them IEEE Transactions on Image Processing, IEEE Transactions on Geoscience and Remote Sensing.

**Research activity. ** In order to address research actively, one usually first tries to devop (or find) suitable models that well describe the physics of the
phenomena that are originating the data.
To provide finest descriptions of what is not actually observable, one then attempts to exploit the mathematical properties
that can be derived from the models. This process sometimes makes it possible to explain the solution of the target problem as solution of a
coupled mathematical problem such as: an energy optimization problem, or a set of partial differential equations (any others?).

**Research interests.** Currently my research interests are mainly related to image processing and numerical optimization. In particular, change detection and variational segmentation are at the center of my
investigations. The change detection problem is considered from the statistical point of view and it is applied to optical multispectral images. Image segmentation is considered
in a variational framework such as in the Mumford-Shah and Blake-Zisserman functional models. Numerical strategies to address the minimization problem on large images are under study.