Ernst Lindelöf award in mathematics given to master's thesis on Big Data

In his award-winning master’s thesis doctoral candidate Jerri Nummenpalo analysed a method for Big Data computation.

The thesis included analysing the functionality of a mathematical method in dealing with large sets of data, i.e. Big Data, and the method was also shown to work in a more common situation than originally. Mathematicians all over the world are particularly interested in solving computational challenges connected to Big Data.

Jerri Nummenpalo, a doctoral candidate at ETH Zurich was pleasantly surprised about the award.

'When dealing with large sets of data, it is no longer possible to use complex mathematical models due to limitations of computational capacity. My thesis investigates a method in which a certain model is simplified, which makes it functional again. This increases the statistical error, but the large set of data can be used to compensate for the problem. It is interesting how it is possible to describe the interaction between the statistical error and required amount of data', Jerri Nummenpalo characterises his master's thesis.

'Working on my master's thesis developed my mathematical thinking a great deal. Good cooperation with members of Engström's research group was particularly rewarding', Jerri explains.

The master's thesis was supervised by Professor Alexander Engström of Aalto School of Science.

The aim of the Finnish Mathematical Society (FMS), established in 1986, is to promote research and avocation in mathematics in Finland. http://www.matemaattinenyhdistys.fi/

The Engström Group: http://math.aalto.fi/~alex/index.php

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