Join us in commemorating the five-year anniversary of the Data Science Institute (DSI) with a special two-day event. This milestone offers us a moment to reflect on our achievements, thank our collaborators and look ahead to the future.
This event has already taken place.
Registration for UHasselt colleagues via e-mail link.
On the first day, we will focus on our academic network and the numerous collaborations that have enriched our institute. The highlight of the day will be the conferment of an honorary doctorate to Professor Dr. Dan Suciu, in recognition of his outstanding contributions to the field of data science and more specifically in the field of Data management, where he combined theoretical and systems-oriented research.
14:00 - 15:10 Welcome and presentation by Honorary Doctorate recipient, Professor Dr. Dan Suciu entitled "The Discrete Impact of Theory on Data Management"
15:10 - 15:30 Break
15:30 - 16:00 Honorary Doctorate Ceremony
16:00 - 17:00 Retrospective on Five Years of DSI
17:00 - 18:00 Reception
The second day will emphasize our commitment to supporting government, industry, healthcare, and other societal partners. Through a series of discussions and debates, we will explore current challenges in Data Science and the role of DSI in addressing them in collaboration with our partners.
13:30 - 14:00 Welcome coffee
14:00 - 14:15 Looking back at 5 years of DSI
14:15 - 15:00 Data Science Challenges from Various Perspectives
15:00 - 15:45 Panel Discussion
15:45 - 16:30 How Can DSI Support Companies and Organizations?
16:30 - 17:30 Reception
On day 2, we warmly invite companies, government agencies, healthcare providers, funding organizations and other stakeholders to join us for these insightful sessions. This event offers you the opportunity to strengthen collaborations and network with experts and professionals in the field. Join us in driving tomorrow's innovation in data science. More information on the external event page.
Contact and cancellation: dsi@uhasselt.be
Honorary doctorate for Professor Dan Suciu
Dan Suciu is a Microsoft Endowed Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. Suciu is conducting research in data management, on topics such as query optimization, probabilistic data, data pricing, parallel data processing, data security. He is a co-author of two books: Data on the Web: from Relations to Semistructured Data and XML, 1999, and Probabilistic Databases, 2011. He received the ACM SIGMOD Codd Innovation Award, received several best paper awards and test of time awards, and is a Fellow of the ACM, and a member of the American Academy of Arts and Sciences. Suciu is currently an associate editor for the Journal of the ACM. Suciu's PhD students Gerome Miklau, Christopher Re and Paris Koutris received the ACM SIGMOD Best Dissertation Award in 2006, 2010, and 2016 respectively; Nilesh Dalvi and Remy Wang were runner ups in 2008 and 2024 respectively.
Presentation 'The Discrete Impact of Theory on Data Management'
If Computer Science is the shining edifice of modern civilization, then Databases are its mechanical room and its plumbing. While all the headlines and spotlights are on latest applications and on the exciting successes of machine learning, beyond the scenes databases are the ones responsible for storing, updating, and analyzing the data that make these applications run. Database systems have evolved over decades, and are some of the most complex software artifacts to date. Yet, what is even less visible, is how theory has discretely impacted databases, and data management in general. Scaling up to big data has required the development of new kinds of algorithms and new abstractions, grounded in mathematical logic, automata theory, probability theory, and information theory. In this talk I will give a few examples on how theory has influenced the tools that we use today for data management, from the creation of the relational data model in the early 70s, up to the use of information theory to improve the scalability of data processing.