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Esa Ollila

  • Phone+358 50 4696607
  • Maarintie 6 (T-talo)

20022021

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Personal profile

Artistic and research interests

Esa Ollila received the M.Sc. degree in mathematics from the University of Oulu, in 1998, the Ph.D. degree in statistics with honors from the University of Jyväskylä, in 2002, and the D.Sc.(Tech) degree with honors in signal processing from Aalto University, in 2010. From 2004 to 2007 he was a postdoctoral fellow of the Academy of Finland and from August 2010 to May 2015 an academy research fellow of the Academy of Finland. He has also been a Senior Researcher and a Senior Lecturer at Aalto University and University of Oulu, respectively. Since June 2015 he was appointed as an associate professor at the Department of Signal Processing and Acoustics, Aalto University, Finland. He is also an adjunct Professor (statistics) of University of Oulu. For the Fall-term 2001, he was a Visiting Researcher with the Department of Statistics, Pennsylvania State University, State College, PA, while during the academic year 2010–2011 he was a Visiting Post-doctoral Research Associate with the Department of Electrical Engineering, Princeton University, Princeton, NJ. His research interests focus on theory and methods of statistical signal processing, statistical learning for high-dimensional data, array and radar signal processing and robust statistical methods in general. He has authored or coauthored more than seventy papers in the top journals and conference in signal processing and statistics. He is also a member of EURASIP SAT in Theoretical and Methodological Trends in Signal Processing (TMTSP).

Education/Academic qualification

Doctor of Science (Technology), Electrical Engineering, Aalto-yliopisto, Sähkötekniikan korkeakoulu

Award Date: 12 Apr 2010

Doctor of Philosophy, Mathematics, Jyväskylän yliopisto

Award Date: 11 Dec 2002

Master of Science, Mathematics, Oulun yliopisto

Award Date: 2 Dec 1998

Keywords

  • Statistical signal processing
  • Multivariate Statistics
  • Compressed sensing
  • Sparse approximation
  • Big data
  • Array processing
  • Robust statistical methods
  • Blind source separation

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