This year's Data Science Day was a great success, providing a platform for exciting insights into previous Data Analysis Projects by our master students in Business Analytics, Data Science, and Digital Humanities. A particular highlight of the event was the Poster Awards, which recognised outstanding contributions by our master students.

We are pleased to announce the winners of the three Poster Awards:

1. Edina MARICA
   Counting Triangles in Graphs
2. Blanka VISY
   How do ML models influence us?
3. Ruslan SHKIRA, Michael STARK & Christof WIESINGER
   Influence of demographic factors on Individuals news article preference

All participating posters (in alphabetical order):

• Analysis of Passenger Counts vs. Ticket Validations – Cooperation with Austrian Railways by Dávid Chmelík, Yuka Obayashi & Tomáš Tax
• Cross-attribute evaluation of fairness mechanisms by Razvan-Andrei Morariu
• Cross-session stability analysis and invariant feature extraction of MEA data by Mario Surlemont
• Curiositäten- und Memorabilien Lexicon von Wien. Topics and Co-occurence Network of a mid-19th Century Lexicon by Nikola Krisztian Czindrity
• Exploring the Current State of the Art in Deep Multi-View-Clustering by Michael Oster & Alexander Sturm
• Population Explorer by Daniel Illes, Michael Knaus, Oskar Nyland, Yujie Wang & Maximilian Zürn
• Seat Reservation by Ia Diakonidze, Raela Salja & Dunja Milojevic
• Synthesizing Speech from Invasive Neural Data Using an Encoder-Decoder Framework by Damian Bednarz & Alina Behrens
• Web-Scraping Blogspot Data as an Individual-Based Diachronic Text Corpus for Measuring Linguistic Prevalence by Maximilian Berens, Patrick Konrad, Marja Nikolčič & Sebnem Yayla 

Congratulations to the winners and many thanks to all participants for their impressive contributions!

In addition to the poster presentations, the event featured an enlightening keynote lecture by Stephan Günnemann, Professor for Data Analytics and Machine Learning at the School of Computation, Information and Technology at the TU Munich. His talk explored the application of graph machine learning techniques across various scientific domains. Professor Günnemann highlighted the potential of these methods to address complex problems by leveraging the multi-scale nature of scientific data, offering new insights and advancing the frontiers of research in data science.

The Data Science Day would not have been possible without the generous support of our sponsors Deloitte, Entrepreneurship @ Uni Vienna and the Alumni Association. Their invaluable contributions have significantly impacted the success of this event, fostering innovation and supporting the data science community. We look forward to continuing this journey together and achieving even greater milestones in the future.

Photos: Petra Schönfelder

About Stephan Günnemann

Stephan Günnemann conducts research in the area of machine learning and data analytics. His main research focuses on how to make machine learning techniques reliable, thus, enabling their safe and robust use in various application domains. Prof. Günnemann is particularly interested in studying machine learning methods targeting complex data domains such as graphs/networks and temporal data.

He acquired his doctoral degree in 2012 at RWTH Aachen University in the field of computer science. From 2012 to 2015 he was an associate of Carnegie Mellon University, USA; initially as a postdoctoral fellow and later as a senior researcher. Prof. Günnemann has been a visiting researcher at Simon Fraser University, Canada, and a research scientist at the Research & Technology Center of Siemens AG. In 2015, Prof. Günnemann set up an Emmy Noether research group at TUM Department of Informatics. He has been a professor at TUM since 2016. He is the Executive Director of the Munich Data Science Institute and Director of the Konrad Zuse School of Excellence in Reliable AI.

https://www.professoren.tum.de/en/guennemann-stephan

About iLabs

The innovation labs are aimed at students, doctoral candidates, and early-stage researchers interested in learning and applying the fundamentals of entrepreneurship. Through a blend of theoretical knowledge and practical application, participants engage in an accelerated venture creation process, working to evolve their ideas from conception to pitch. Learn more about the innovation labs here: https://ilabs.univie.ac.at/

About Alumni Association

The Alumni Association is the official alumni network of the University of Vienna: a platform established by graduates for graduates, offering opportunities for engagement, enrichment, and active participation. The aim is to harness the involvement of our alumni community to launch new initiatives and projects for the university's future and the next generation of graduates. Activities are developed not just for alumni, but with alumni. Read about the Alumni Association here: https://alumni.ac.at/portal/home

About Deloitte

Deloitte Austria is the leading professional services firm. Approximately 1,900 employees in 15 offices across Austria provide audit, tax, consulting, financial advisory and risk advisory services to public and private clients. Clients benefit from high-quality services through top experts and from profound industry insights. Deloitte Legal and Deloitte Digital complete our extensive range of services. Deloitte defines itself as a smart initiator for positive development of the business location Austria. The Deloitte Future Fund develops social initiatives. As an employer, Deloitte has the ambition to be "Best place to work". Find out more at www.deloitte.at