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From ozone measurements to pensions

Barbara D'Ambrogi-Ola, Ilmarinen Mutual Pension Insurance Company

Once upon a time a young mathematician was enchanted by aurora borealis….
This was the start of a journey going through Gibbs samplers, ozone measurements, teaching, fractional Brownian motions, utility functions and Black-Scholes equation.  The current chapter of the story is taking place in a pension insurance company. So, what does a mathematician do in an insurance company? And does an insurance company need somebody with a PhD in math? Finally a small survival kit for working in industry will be provided.

Number theory meets digital television

Camilla Hollanti, Aalto University

In this talk I will explain how digital TV broadcasting can benefit from algebraic number theory via using so-called space-time codes. We will see how to translate purely practical questions into equivalent purely number theoretic questions

Luzin's condition (N)

Pekka Koskela, University of Jyväskylä

For a model of elastic deformations to be realistic, one must among other things rule out creation of matter.
In mathematical terms, this means that the mapping representing the change of positions of particles should not
map any set of volume zero onto a set of positive volume. This is called Luzin's condition (N) after Nikolai Luzin, who is more famous for his theorem on quasicontinuity of measurable functions.
I will review recent research on Luzin's condition (N).

Games, analytics, business, academia – how do they mix together?

Ville Suur-Uski, Supercell

What’s the value of a scientific education at Supercell, a mobile games company?
The work a researcher does in academia shares some common features to being a data scientist analysing Clash of Clans. The speech will have examples of skills needed in both trades, examples where mathematics is just a must and what do statistics mean for a game designer.

Mathematics in Patient Monitoring

Kimmo Uutela, GE Healthcare

GE Healthcare is a multinational medical device manufacturer. GE Healthcare Finland is developing patient monitors for hospitals. The patient monitor measures signals from the patient and calculates indicators that help the caregivers in their work. The monitor uses signal processing, estimation methods and statistical methods to provide useful and reliable information for clinical decision making. The presentation includes examples of development tasks where mathematical methods have been used.

Reasons behind cost overruns – systematic bias, selection bias, or both?

Eeva Vilkkumaa, Aalto University

Projects that are selected for implementation based on uncertain cost estimates often end up costing considerably more than estimated. For instance, cost overruns have occurred in 90% of large transportation infrastructure projects worldwide with an average overrun of 28%. Cost overruns are typically attributed to a systematic downward bias in the projects’ cost estimates. However, even if the cost estimates among project proposals are unbiased but only those projects with the lowest estimates are implemented, cost overruns are to be expected due to selection bias. We develop a model for systematic bias and selection bias in project selection, and show how the relative magnitudes of these biases can be determined using an Expectation Maximization algorithm. Furthermore, we show how the model can be used to mitigate cost overruns resulting from both biases.