SoSe 2019

DLS-WS2018-19

Alle Vorträge finden jeweils 17:00 Uhr in SR 3325 am Ernst-Abbe-Platz 2 statt!

show Content 17. April 2019 · Lena Wiese (Georg-August-Univeristät Göttingen) · Flexible and Secure Management of Biomedical Data

Wiese

Flexible and Secure Management of Biomedical Data


Due to the ongoing progress of the digital transformation of research and science, large bodies of data (so-called big data) have to be analyzed with the support of novel data management technologies for
example in personalized medicine. Moreover – for sake of reproducibility and validation of research results (for example in clinical studies) – research data should gradually be transferred into repositories that make the raw data accessible to other researchers (so-called open data). These repositories also allow to connect several independent data sets (so-called deep data) to produce previously impossible scientific insights in the individual disciplines (like better personalized health predictions) or even across disciplines. To provide efficient access to data, novel services are required comprising enhanced data storage, management and analysis technology. As a special application domain, in the Life Sciences there is a growing demand to process increasing amounts of biological and medical data; for
these data, additional concerns arise with respect to data protection because secure management of patient-related data has to be ensured. In this talk I will discuss several approaches to enable efficient and security-enhanced data analysis in health applications. 

 

 

 

 

 

 

show Content 15. Mai 2019 · Claudia Müller-Birn (FU Berlin) · Designing human-machine collaborations – how to effectively interweave human and algorithmic activities

Müller-Birn

Designing human-machine collaborations – how to effectively interweave human and algorithmic activities

Drawing from my many years of experience of the open peer production system Wikipedia, I introduce various means of quality assurance mechanisms that effectively combine human and algorithmic activities. Over the last two decades, Wikipedia has developed a very effective systems consisting of bots, filters, and interfaces. By using one example of a machine learning-based, i.e., algorithmic, system, I will show possible sources of biases and explain how we can re-interprete these sources into points of participation, where humans are urgently needed to monitor, validate, configure these algorithmic systems. Based on that argument, I infer the concept of human-machine collaboration and introduce first design parameters: level of control, level of coordination, and level of interpretability. For each of these design parameters, I provide examples from our current research (annotation, ideation, visualization). I conclude this talk with an outlook on future research questions and reflect on the societal impact of my research.

 

 

 

show Content 12. Juni 2019 · Holger Theisel (Otto von Guericke Univerisät Magdeburg) · Optimal Reference Frames, Scalings, and Features in Visualization

Theisel

Optimal Reference Frames, Scalings, and Features in Visualization Holger Theisel, University if Magdeburg

In Visualization, the success or failure of an analysis often depends on the choice of some subtle parameters or design choices.
While simple heuristics are often sufficient, in some cases they make the analysis miserably fail. We present three approaches
in visualization where a careful choice of optimal parameters results in new algorithms:

- the choice of a reference frame for finding objective vortices in flow visualization, 
- the choice of a scaling of high-dimensional data sets for finding linear projections to 2D in information visualization,
- the choice of a feature definition along with numerical extraction methods for visualizing recirculation phenomena in flows.

 

 

 

show Content 19. Juni 2019 · Marcus Paradies (DLR) · Near-Data Processing - In-Storage and In-Network Computing for Big Data Workloads

Paradies

Near-Data Processing - In-Storage and In-Network Computing for Big Data Workloads

TBA