Lev Goldfarb obtained Diploma (~ MSc) in Mathematics
& Computer Science (specializing in topology) from
He has served on the editorial boards of Pattern Recognition, Pattern Recognition Letters, Cognitive Neurodynamics.
Trained as a mathematician, he realized at the beginning of his career the fundamental inadequacy of the conventional pattern recognition (especially statistical) formalisms, and so all professional life he has been working on the development of new formalisms that would serve more adequately the needs of the field. In the early 80s, he pioneered an approach to the problem of efficient classification of the conventionally structured (non-vector) data via its near-isometric representation in the corresponding low-dimensional pseudo-Euclidean vector space. Over the last decade and a half, this approach has received renewed interest in image retrieval, pattern recognition, and machine learning.
Starting from the late 1980s and prompted by the true novelty and variety of geometries he observed within a symbolic representation—as compared to the conventional mathematical spaces—and also by the intrinsic inability of the latter to support an adequate concept of object class, he, with his students, have been developing a fundamentally new kind of representational formalism (ETS). It appears that this formalism for structural representation has much wider scientific implications.