The conference is organized by Ivane Javakhishvili Tbilisi State University (July 7-12, 2025, Tbilisi, Georgia), and will be held at 1 Ilia Chavchavadze Ave., Build. 1.
Participation in the conference is by invitation only. Talks should be aimed at a broad audience, include good heuristics, and also be accessible to graduate students and postdocs with solid background in statistics and probability theory. Talks presenting interesting data applications are welcome as well.

Professor Estate V. Khmaladze (FRSNZ, Victoria University of Wellington, NZ)

Outstanding mathematician and statistician, Estate Khmaladze was born in Tbilisi, Georgia, on October 20, 1944. He graduated with BSc in Physics and MSc in Applied Mathematics from I. Javakhishvili Tbilisi State University in 1966, and he earned his PhD in mathematics in 1971 and Doctor of Physical and Mathematical Sciences in 1988, both from M. Lomonosov Moscow State University.

From 1972 to 1990 he held positions at A. Razmadze Mathematical Institute, the leading research institute in mathematics of the Georgian Academy and interim positions at the V.A. Steklov Mathematical Institute of Soviet Academy, one of world centers of mathematical research at that time. From 1990 to 1999 he served as head of the Department of Probability and Mathematical Statistics at A. Razmadze Mathematical Institute. From 1996 to 2002 he worked at the School of Mathematics and Statistics of the University of New South Wales. Since 2002 he has been Professor of Statistics in the School of Mathematics and Statistics of Victoria University of Wellington where he currently is Emeritus Professor. E. Khmaladze is a Fellow of the Royal Society of New Zealand and of the US-based Institute of Mathematical Statistics. In 2015 he was awarded the Ivane Javakhishvili Medal, the highest honor from Tbilisi State University, and in 2019 he was awarded the Ivane Javakhishvili Tbilisi State University Gold Medal. In 2016 he was elected the Foreign Member of the Georgian Academy of Sciences.

Prof. Estate Khmaladze has a large number of followers and friends in statistical theory and its applications. Statistics is the field, where he is creating new beautiful and unexpected theories.

Among many contributions in different areas of statistics and mathematics there should be mentioned the solutions of several fundamental problems in statistical testing theory, both in the univariate and in the multivariate cases. For instance, in the univariate case: the martingale transformation, now known as Khmaladze transformation (KhT), provides a new class of distribution free goodness of fit tests. This was achieved via remarkable connection between semi-martingales in probability theory and goodness of fit tests in statistics. In the multivariate case: the introduction of a new type objects, known as “scanning martingales” enabled him to construct distribution free tests in the multidimensional time as well. The problem that has been known since the middle of 50’s, was solved about three decades later by means of KhT approach in Estate’s series of papers.

Currently, there are more than 15,000 references to KhT on. There is a wide variety of application of KhT since it provides unique solutions to many complicated problems in probability and statistics.

It also should be mentioned his work in set-valued analysis with new definition of derivative of set-valued functions. This work allowed to give the general form to the theory of local empirical processes of spacial statistics.

Remarkable structures were found by E.Khmaladze while analyzing the occupancy problem in connection with statistical theory of diversity and the fragmentation processes. His new approach connects statistical problems of diversity and occupation with ostensibly unrelated theory of infinite divisibility and subordinated processes.

Khmaladze suggested a new and very wide class of asymptotically distribution free goodness of fit tests for distributions (both the discrete and continuous time models) that serves as a basis for many new discoveries in statistical theory and applications. Among them, a unified approach has been proposed for analyzing the grouped dataset in a sparse regime and the analog of the theory of empirical process in the sparse regime case has been constructed.

In economics and financial mathematics, very strange properties of Ornstein-Uhlenbeck processes have been discovered. In 2013 the book on statistical methods with application to demography and life insurance was published. Another book on the general theory of goodness of fit tests will be completed soon that will provide a new point of view on many solutions of statistical and mathematical problems.

Even in the field of stochastic geometry, it was surprising to see that given the very long history of development and very large number of research publications, E. Khmaladze become the first mathematician to formulate the strong law of large numbers for random tessellations.

There should also be mentioned Khmaladze’s work in human genetics, electro-physiology and medical diagnostics and many other interesting papers from the theory of sequential ranks to the analysis of strange behavior of rule times in Imperial Rome and Imperial China.

It is difficult to imagine how one person could find so much new phenomena and new structures, but more importantly, to solve problems in such a remarkable and fascinating manner.