PhD, Hydro­ge­ol­ogy, Uni­ver­sity of Neucha­tel, Switzer­land, 2009
MS, Geol­ogy, Uni­ver­sity of Neucha­tel, Switzer­land, 2003

The main research inter­ests of Gre­goire reside in the devel­op­ment of sto­chas­tic meth­ods that char­ac­ter­ize the spa­tial and tem­po­ral vari­abil­ity inher­ent to hydro­log­i­cal sys­tems. He devel­ops new numer­i­cal tech­niques using high-order, non-parametric sta­tis­tics, which allow for min­ing the full rich­ness of mod­ern data sets. This dense infor­ma­tion con­tent offers new avenues for data assim­i­la­tion, inte­gra­tion of scale depen­den­cies and prior spa­tial concepts.

One of his con­tri­bu­tions has been to develop numer­i­cal meth­ods that offer improved pos­si­bil­i­ties to inte­grate dif­fer­ent kinds of data, espe­cially soft knowl­edge such as geo­log­i­cal con­cepts, expressed using the semi-qualitative con­cept of a train­ing image. He designed algo­rithms to per­form recon­struc­tions and sto­chas­tic sim­u­la­tions, and also to address mul­ti­ple non-linearly depen­dant vari­ables. Par­al­lel com­put­ing for the Earth Sci­ences is also a major topic of interest.

The aim is to make these meth­ods appeal­ing enough to be rou­tinely used for describ­ing hydro­log­i­cal processes and quan­ti­fy­ing their uncer­tainty. This includes work on improve­ment, per­for­mance and usabil­ity of sto­chas­tic algo­rithms, as well as a wide range of appli­ca­tions to sur­face hydrol­ogy, hydro­ge­ol­ogy and paleoclimate.

Before join­ing UNSW, Gre­goire worked at the Uni­ver­sity of Neucha­tel (2006–2009) and at Stan­ford Uni­ver­sity (2009–2010)

For more infor­ma­tion, see detailed CV, pub­li­ca­tions list, and some descrip­tions of cur­rent research projects.