The 9th Swedish Workshop on Data Science
The 9th Swedish Workshop on Data Science (SweDS21) will be hosted by Linnaeus University in Växjö, Sweden, between the 2nd and 3rd of December 2021.
SweDS is a national event with a focus of maintaining and developing Swedish data science research and its applications by fostering the exchange of ideas and promoting collaboration within and across disciplines. This annual workshop brings together researchers and practitioners of data science working in a variety of academic, commercial, industrial, or other sectors.
Past workshops have included presentations from a variety of domains, e.g., computer science, linguistics, economics, archaeology, environmental science, education, journalism, medicine, health-care, biology, sociology, psychology, history, physics, chemistry, geography, forestry, design, and music.
Important dates
- Submission deadline: October 15th, 2021
- Author notification: November 5th, 2021
- Workshop: December 2-3, 2021
Keynote Speakers
Keynote 1: Professor Alexandru Telea:
Visualizing the black box of machine learning: Challenges and opportunities
Machine learning (ML) has witnessed tremendous successes in the last decade in classification, regression, and prediction tasks. However, many ML models are used, and sometimes even designed, as black boxes. When such models do not operate properly, their creators do not often know what is the best way to improve them. Moreover, even when operating successfully, users often require to understand how and why they take certain decisions to gain trust therein. We present how information visualization and visual analytics help towards explaining (and improving) ML models. These cover tasks such as understanding high-dimensional datasets; understanding unit specialization during the training of deep learning models; exploring how training samples determine the shape of classification decision boundaries; and helping users annotating samples in semi-supervised active learning scenarios.
Bio
Alexandru Telea is a Professor of Visual Data Analytics at the Department of Information and Computing Sciences, Utrecht University. He holds a PhD from Eindhoven University and has been active in the visualization field for over 22 years. He has been the program co-chair, general chair, or steering committee member of several conferences and workshops in visualization, including EuroVis, VISSOFT, SoftVis, and EGPGV. His main research interests cover unifying information visualization and scientific visualization, high-dimensional visualization, and visual analytics for machine learning. He is the author of the textbook "Data Visualization: Principles and Practice" (CRC Press, 2014).
https://webspace.science.uu.nl/~telea001/Main/HomePage
Keynote 2: Dr Antonina Danylenko: Machine Learning @Spotify: when prediction is not enough
Recommendation systems are an essential part of our daily life. Most of the applications support our decision making by selecting the items for our next purchase, next movies to watch, next music to listen to as well as suggestions for new friends on social networks. Traditionally recommendation systems were focused on a simple prediction problem and optimized for immediate reward, e.g. click or stream. However, in many cases recommendation becomes a sequential problem and a long-term reward in the sequence of decisions needs to be taken into account. To account for this we can employ Reinforcement Learning mechanisms, where advances in deep reinforcement learning allows us to apply these methods to massive amounts of data, states and possible actions.
In this talk I will discuss what is Spotify vision when it comes to the future of recommendation systems and what tools and technologies we use to achieve a company mission to unlock the potential of human creativity—by giving a million creative artists the opportunity to live off their art and billions of fans the opportunity to enjoy and be inspired by it.
Bio
Antonina Danylenko holds a PhD in Computer Science from Linnaeus University, Sweden. In her research she combined different Machine Learning algorithms into an algebraic framework and applied it to software engineering problems. After finishing PhD, she started her industrial career as Solution Architect and Data Scientist at IKEA of Sweden where she focused on strategic and hands-on levels of Data Science. Starting from 2018 she worked at Nordic Entertainment group (Viaplay) as a Head of Applied Machine Learning focusing on personalisation of video streaming service. Currently she is a Machine Learning Engineering manager at Spotify and together with her team she works on strategic content promotions.
https://www.linkedin.com/in/antoninadanylenko/?originalSubdomain=se
Keynote 3: Professor Per Runesson: Open data ecosystems – wishful thinking or successful business?
Sharing data between commercial entities is a practice in its infancy, despite the widespread focus on data-driven businesses. Data brokers exist in some domains, but tend to gravitate around the big data players. However, judging from the success of open source software in commercial contexts, there is a potential innovation growth in open data ecosystems (ODE), which we define as a networked community of actors collaborating on data and related resources.
We have studied cases of ODEs, to understand the challenges and remedies for them to function. This talk summarizes our findings regarding
• Value of data and collaboration
• Inherent characteristics of data
• Governance of data, platform and relations
• Evolution of business models and tools
The studied ODEs, in domains of industry 4.0, automotive, transportation, and the labor market, represent wide variation across real-time and batch data, public and private actors etc. They nevertheless share several joint characteristics, particularly, the value creation from the data or collaboration around data, the need for standardization of formats and procedures, aspects of platform ownership and data acquisition, and competition between actors, are essential. Business models and tool support for ODEs are identified for further research.
Bio
Dr. Per Runeson is a professor of software engineering at Lund University, Sweden, and leader of its Software Engineering Research Group (SERG). His research interests include empirical research and collaboration with industry on software development and management methods. He is particularly interested in studies on testing, and the role of open source and open data in software engineering. He is the principal author of ”Case study research in software engineering”, has coauthored ”Experimentation in software engineering”, serves on the editorial board of Empirical Software Engineering, IEEE Transactions on Software engineering, and Software Testing, Verification and Reliability, and is a member of several program committees.
https://cs.lth.se/per-runeson/
Dec 1-2, 2021: Big Data Conference 2021
Anyone interested in this conference might also be interested in this year's Big Data Conference, at Linnaeus University in Växjö, on December 1-2, 2021: Lnu.se/en/BigData2021.