Multidimensional fluorescence imaging is a fundamental tool to study biophysical processes from cellular to tissue level. In particular, the possibility of simultaneously acquiring spectral, temporal (at picosecond time scale) and spatial (imaging) information allows one to get significant insights about biophysical processes such as the case of photosynthetic processes in plant/algae and metabolic processes at tissue level. The last one is fundamental to identify markers of diagnostic interest. The complexity of a multidimensional approach in terms of big data-set and speed of acquisition needs a careful design of the acquisition and detection strategies, which have to take into account how data will be analyzed and, possibly, a compression at the acquisition level. This approach, named “computational imaging”, will combines optical spectroscopy, optical design and data processing with the aim of the development of an optimized system.
Different master thesis are currently available.

• Thesis 1: The aim of this thesis is to setup a new system for fast multidimensional fluorescence imaging based on patterned illumination/detection for biological tissue characterization at microscopic level. The system will be characterized on cellular samples to identify the capability of the system to study biophysical processes.
• Thesis 2: The aim of this thesis is to develop novel acquisition and processing methods to reduce the data set (and consequently the acquisition time) while preserving the information content fundamental to extract the physical parameters of interest. Novel algorithms will be developed and tested with the existing experimental set-up.