TOM – 27.04.2016

Venue: Biomedicum 1, Meeting room 3, P floor 

Timing: 15:30 – 17:00

Speaker: Mikhail Shubin, PhD Student

Data Visualization

Everybody loves visualization! It is used to search the data for new discoveries and to communicate these discoveries to fellow scientists.

But are we doing it right? Could we do it better? Is it possible we were lost in a hype?

I will try to give a brief overview of scientific visualization from the point of graphic design. I will use keywords like “information/noise ratio”, “human visual cognition”, “fashion”, “colour blindness” and “everybody is doing it wrong”. But I would prefer having a discussion instead of a lecture; I will bring a number of examples which (I hope) we can talk about together.

About me: My name is Mikhail, I have a masters degree in CS and bioinformatics. Now I’m spending my last months as a PhD student in Statistics. I have a blog about scientific visualization

TOM – 12.04.2016

Venue: Biomedicum 1, 5th Floor, Meeting room 7

Timing: 15:30 – 17:00

Speaker: Svetlana Ovchinnikova, PhD Student, Group Anders, FIMM

Metric Learning Algorithms

Many popular machine learning approaches strongly rely on distance/similarity between samples. Yet a pre-defined distance metric is not always relevant for the considered property and an appropriate transformation of the feature space can increase the effectiveness of a machine learning algorithm.

In my presentation, I will give an overview of existing approaches to metric learning and talk about mathematical background behind these algorithms. I will also demonstrate two Matlab packages: LMNN (Large Margin Nearest Neighbours) and MLKR (Metric Learning for Kernel Regression), which can be used for classification and regression problems respectively.

Weinberger, K. Q., Blitzer, J., & Saul, L. K. (2005). Distance metric learning for large margin nearest neighbor classification. In Advances in neural information processing systems (pp. 1473-1480). –

Weinberger, K. Q., & Tesauro, G. (2007). Metric learning for kernel regression. In International Conference on Artificial Intelligence and Statistics (pp. 612-619). –

Kireeva, N. V., Ovchinnikova, S. I., Kuznetsov, S. L., Kazennov, A. M., & Tsivadze, A. Y. (2014). Impact of distance-based metric learning on classification and visualization model performance and structure–activity landscapes. Journal of computer-aided molecular design, 28(2), 61-73. –