Verena Schwarz wins the sponsorship award of the Ulm Forum for Economic Sciences

Verena Schwarz wins the sponsorship award of the Ulm Forum for Economic Sciences. She received the award for the best master degree in business mathematics in 2020/2021 on April 29th, 2022. Read more

Calculating the source of a sound


Where do you place sensors so that they pinpoint the source of a sound as accurately as possible? To answer this question, we need mathematics. Phuoc Truong Huynh is a doctoral student working on solutions required in many fields of application.

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New Colleague @ ICS

We are very happy to welcome our new colleague Aron Sacherer, BSc in the Department of Informatics Systems. Aron Sacherer started his work in the Information and Communication Systems research group as scientific project staff in the BBMRI.at project in March, 2022.

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ICS – publication “Dynamic Controllability of Processes without Surprises”

The research paper “Dynamic Controllability of Processes without Surprises“, written by the ICS group members O.Univ.-Prof. Dipl.-Ing. Dr. Johann Eder, Senior Scientist Dipl.-Ing. Dr. Marco Franceschetti and Univ.-Ass. Josef Lubas, BSc. MSc., is now available on the following link:

https://doi.org/10.3390/app12031461

The paper “Dynamic Controllability of Processes without Surprises” has been published at 29th of January 2022 in the Journal Applied Sciences and is available as free full-text by using the link above.

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Trends in Recommendations Systems – A Netflix Perspective

Thursday April 7th 2022 | 05.30 pm (CET) | via Zoom

Anuj Shah, Ph. D. | Senior Machine Learning Research Practitioner at Netflix |

Click here to register for the meeting:

https://zoom.us/meeting/register/tJYvdO-gqzMiEtKOfNIgcZAZOQ8jA3i_b3Pi

 

Abstract:

Recommendation systems today are widely used across many applications such as in multimedia content platforms, social networks, and ecommerce, to provide suggestions to users that are most likely to fulfill their needs, thereby improving the user experience. Academic research, to date, largely focuses on the performance of recommendation models in terms of ranking quality or accuracy measures, which often don’t directly translate into improvements in the real-world. In this talk, we present some of the most interesting challenges that we face in the personalization efforts at Netflix. The goal of this talk is to sunshine challenging research problems in industrial recommendation systems and start a conversation about exciting areas of future research.

 

Bio:

Anuj Shah is a Senior Machine Learning Research Practitioner at Netflix. For the past 10+ years, he’s been working on an applied research team focused on developing the next generation of algorithms used to generate the Netflix homepage through machine learning, ranking, recommendation, and large-scale software engineering. He is extremely passionate about algorithms and technologies that help improve the Netflix customer experience with highly personalized consumer-facing products like the Continue Watching row, the Top 10 rows amongst many others. Prior to Netflix, he worked on machine learning in the Computational Sciences Division at the Pacific Northwest National Laboratory focusing on technologies at the intersection of proteomics, bioinformatics and Computer Science for 8 years. He has a Ph.D. from the Computer Science department at Washington State University and a Masters in C.S. from Virginia Tech

Tanja Maier received her doctorate at the department of Statistics.

On 02/25/2022 Tanja Maier received her doctorate at the University of Klagenfurt.

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Enjoying the creativity of mathematics

Research mathematics is creative. One of those people with a particular affinity for imaginative puzzle-solving is Sarah Jane Selkirk. The South African came to Klagenfurt in 2020 as a doctoral student and is now a member of the doc.funds doctoral school “Modeling – Analysis – Optimization of discrete, continuous, and stochastic systems”.

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Improve and accelerate how we learn from health data: New approach reduces machine learning time by 60%

Electronic health records, like ELGA in Austria, provide an overview of laboratory results, diagnostics and therapies. Much could be learned from the personal and private data of individuals – with the help of machine learning – for use in the treatment of others. However, the use of the data is a delicate matter, especially when it comes to diseases that carry a stigma. Researchers involved in the EU project “Enabling the Big Data Pipeline Lifecycle on the Computing Continuum (DataCloud)” are working to make new forms of information processing suitable for medical purposes. Dragi Kimovski and his colleagues recently presented their findings in a publication.

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Swarm algorithms can improve production planning and scheduling

Industrial companies face an enormous challenge when it comes to the highly interconnected nature of their production facilities. These exhibit complex and dynamic behaviour, as can be observed in ants, bees, fish or birds. Inspired by models found in nature, the SWILT project models entire industrial plants as swarms. Three years on, we now have the results of the project: The simulations revealed that the overall performance of a large production planning system can be improved by a percentage in the single digits, which can represent significant financial gains for businesses.

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MPEG DASH video streaming technology co-developed in Klagenfurt wins Technology & Engineering Emmy® Award

The Emmy® Awards do not only honour the work of actors and directors, but they also recognise technologies that are steadily improving the viewing experience for consumers. This year, the winners include the MPEG DASH Standard. Christian Timmerer (Department of Information Technology) played a leading role in its development. Read more