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Wilhelmiina Hämäläinen is a postdoctoral researcher by Academy of Finland, currently working in the School of Computing, University of Eastern Finland. She received a M.Th. degree 1998 and a M.Sc. degree 2002, both from the University of Helsinki, a Ph.Lic. degree 2006 from the University of Joensuu, and a Ph.D. degree 2010 (Computer Science) from the University of Helsinki. She has worked as a teacher, lecturer, and researcher in the university since 1996, including 2 years as a university researcher of biology (applied data mining). She has often worked with interdisciplinary problems, involving computer science, statistics, and mathematics. Her main achievements in data mining are efficient algorithms for finding reliable statistical dependency patterns (a related award from The Finnish Society for Computer Science and Research foundation of the Finnish Information Processing Association). Her expertise areas cover statistical dependency analysis and significance testing, optimization algorithms, and applied knowledge discovery (biology, educational technology). Her research interests include statistically sound data mining, mathematics, algorithmics, and general number crunching.
Statistically Sound Pattern Discovery
as author at 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), New York 2014,
together with: Geoff Webb,
Thorough analysis of log data with dependency rules: Practical solutions and theoretical challenges
as author at Practical Theories for Exploratory Data Mining (PTDM), Brussels 2012,