I just got word that my good friend, on-again-off-again-on-again colleague, and habitual co-conspirator, Leo Liberti, is giving an open seminar on “Distance Geometry in Data Science”, on January the 18, from 14h to 15h30 at SystemX (here’re access directions) at Palaiseau just next to Paris.
It’s a free seminar, and if you’re in the neighborhood, then Leo definitely is worth listening to. He’s one of those with the rare gift of being able to render theoretical mathematical concepts both comprehensible, and entertaining – in short, a brilliant scientist and a wonderful orator.
So hurry to sign up!
Here’s the abstract of Leo’s talk:
Many problems in data science are addressed by mapping entities of various kind to vectors in a Euclidean space of some dimension. Most of these methods (e.g. Multidimensional Scaling, Principal Component Analysis, K-means clustering, random projections) are based on the proximity of pairs of vectors. In order for the results of these methods to make sense when mapped back, the proximity of entities in the original problem must be well approximated in the Euclidean space setting. If proximity were known for each pair of original entities, this mapping would be a good example of isometric embedding. Usually, however, this is not the case, as data are partial, wrong and noisy. I shall survey some of the methods above from the point of view of Distance Geometry.
And, Leo’s (impressive) biography:
Leo Liberti obtained his Ph.D. in Global Optimization at Imperial College London, held postdoctoral fellowships at Politecnico di Milano and Ecole Polytechnique in France, where he then became professor and vice-president of his department. After two years as a Research Staff Member at IBM Research in New York, he became Research Director at CNRS and part-time professor at Ecole Polytechnique. His main research interests are mathematical programming with applications to industrial problems, optimization algorithms, and distance geometry.