Keynote Speakers

Maria-Florina Balcan (to be confirmed)
Nina
Maria-Florina Balcan is Associate Professor at the School of Computer Science (MLD and CSD)  Carnegie Mellon University. Her main research interests are in machine learning, artificial intelligence, and theoretical computer science. Her honors include the CMU SCS Distinguished Dissertation Award, an NSF CAREER Award, a Microsoft Faculty Research Fellowship, an Amazon Research Award, a Sloan Research Fellowship, and several paper awards.
Vladimir Batagelj
Vladimir Batagelj
Vladimir Batagelj is Professor Emeritus of the University of Ljubljiana, Slovenia. He is also a member of the Institute of Mathematics, Physics and Mechanics (IMFM), Ljubljana, and of AMI, UP, Koper. His coauthored book Generalized Blockmodeling was awarded the 2007 Harrison White Outstanding Book Award by the Mathematical Sociology Section of the American Sociological Association. From the International Network for Social Network Analysis he was awarded the Georg Simmel Award (2007) and the Richards Software Award for the program Pajek (2013).
Theodoros Evgeniou
Theodoros Evgeniou
Theodoros Evgeniou is a Professor of Decision Sciences and Technology Management at INSEAD in Fontainebleau France and an Academic Director of INSEAD eLab, a research and analytics center at INSEAD that focuses on data analytics for business. He graduated first in the MIT class of 1995 dual degrees in Mathematics, won medals in international mathematical Olympiads, and European awards for business case studies. At INSEAD, Theodoros has been focusing on data analytics applied to a range of areas from customer insights and marketing to finance. He has been developing and teaching courses on Data Analytics, Statistics and Decision Making.
Michael Greenacre
Michael Greenacre
Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra, Barcelona, Spain. He has authored and co-edited nine books and 80 journal articles and book chapters, mostly on correspondence analysis. He has given short courses in fifteen countries to environmental scientists, sociologists, data scientists and marketing professionals, and has specialized in statistics in ecology and social science.
David Hunter
David Hunter
David Hunter is Professor at Penn State Department of Statistics. His research interests include statistical computing, models for social networks, and statistical clustering. He has published extensively on networks, optimization algorithms and mixture models.
Julie Josse
Julie Josse is Professor of Statistics at Ecole Polytechnique in France. She has specialized in missing data, visualization and the nonparametric analyses of complex data structures. She has published over 30 articles and written 2 books in applied statistics. Julie Josse has developed packages to transfer her works such as missMDA dedicated to missing values. She is deeply involved in the R community and is part of Rforwards to widen the participation of minorities in the communities.
Andy Mauromoustakos
Andronikos (ANDY) Mauromoustakos
Andy Mauromoustakos is Professor at the Department of Crop, Soil, and Environmental Sciences and works as an Applied Statistician for the AGRI STAT LAB at the University of Arkansas Fayetteville campus. Andy teaches graduate courses and does research related to Experimental Designs and Data Analysis for the AG Experiment​ Station and the Division of AG.
Sofia Olhede
Sofia Olhede is Professor of Statistics at University College London, director of UCL’s Centre for Data Science, an honorary professor of Computer Science and a senior research associate of Mathematics at University College London. Sofia has contributed to the study of stochastic processes; time series, random fields and networks. She is on the ICMS Programme Committee since September 2008, a member of the London Mathematical Society Research Meetings Committee, a member of the London Mathematical Society Research Policy Committee and an associate Editor for Transactions in Mathematics and its Applications. Sofia was also a member of the Royal Society and British Academy Data Governance Working Group, and the Royal Society working group on machine learning.