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Bio

Esa Ollila received the M.Sc. degree in mathematics from the University of Oulu, in 1998, Ph.D. degree in statistics with honors from the University of Jyvaskyla, in 2002, and the D.Sc.(Tech) degree with honors in signal processing from Aalto University, in 2010. From 2004 to 2007 he was post-doctoral fellow of the Academy of Finland. He has also worked as Senior Researcher and Senior Lecturer at the University of Oulu, respectively. He was appointed as Academy Research Fellow (a prestigious research fellowship position nominated by the Academy of Finland) for a period of five years, starting in August 2010 at Aalto University. Currently, from June 2015 he has been employed as Associate Professor of Signal Processing at Aalto University, School of Electrical Engineering. He was tenured in Jan 2019. He also holds a title of docent (adjunct Professor) in Statistics at the University of Oulu, Finland.

Prof. Ollila has had several long-term research visits abroad. Fall-term 2001 he was Visiting Researcher with the Department of Statistics, Pennsylvania State University, while the academic year 2010-2011 he spent as Visiting Post-doctoral Research Associate with the Department of Electrical Engineering, Princeton University.

He serves frequently as a reviewer to numerous journals in signal processing, statistics and machine learning. He is an elected member (term 2022-2024) of IEEE SPS Signal Processing Theory and Methods (SPTM) Technical Committee and an associate editor of Scandinavian Journal of Statistics (2021-present). Previously, he also served as a member of the EURASIP SAT in Theoretical and Methodological Trends in Signal Processing (TMTSP) He has co-authored a book, Robust Statistics for Signal Processing, published in 2018 by Cambridge University Press. At Aalto University, he serves as the director of the BSc major in Information Technology since 2016 and MSc major in Signal Processing and Data Science (SPDS) since 2021. His research interests lie in the intersection of the fields of signal processing, machine learning and statistics.