Mission Statement

Prof. Dr. Bart De Moor, bart.demoor@kuleuven.be
Prof. Dr. Geert Molenberghs, geert.molenberghs@kuleuven.be
May 2016

One of the disruptive trends in science, technology and society is the growing impact of data and information in all its forms. We consume about 40 petabytes on the internet in 30 minutes, which is more than the annual world consumption of rice grains (27.5 quadrillion). Data are permanently being generated, stored and shared in all sectors of society, a trend triggered by Moore's law (memory and computational power of our computers doubles every 18 months), the fact that the World Wide Web is a small world network (any information on any website is but 6 clicks away) and the growing interconnectivity of people and devices in a new, global infrastructure that we call the Internet-of-Things.

The growing impact of data also drastically changes our research paradigms. Science has evolved from empirical verification (17th century), over theory development (18-19th century), to computational verification and simulation (20th century) and nowadays data-driveness in all disciplines. Science has evolved from formulating one hypothesis and then challenging it, to massive quantification of empirical observations that allow us to find which hypotheses can be evidence-based.

At a comprehensive university like the KU Leuven, data are ubiquitous in all scientific disciplines and departments, ranging from all sciences (biology, chemistry, physics, cosmology), engineering (chemical, materials, electrical, mechanical, bio-engineering, ...), medicine and health, and last but not least, also in the (digital) humanities.

In departments like mathematics, statistics, computer science, mathematical engineering, etc., lots of expertise is available in data-driven and processing algorithms, software methodologies and machine learning approaches, to predict, cluster, classify, detect outliers, filter and normalize, assess relevance, rank, fuse data sources, etc.

All of these algorithms are deployed in generic application areas: in ICT in communication networks, home automation and customization, security applications; in finance in fraud and churn detection, portfolio management, credit and risk assessment; in education in scientometrics, performance monitoring of students and teachers, automated grading; in smart cities in traffic management, predictive maintenance of utilities, utility grids such as power, water, gaz, etc.; in health in diagnostics, therapy monitoring, genomics, evidence-based policy decision support, etc.

It goes without saying that there is abundant potential for interaction and deployment of this expertise in science, society and industry, in ‘vertical’ application areas like industry 4.0, ‘smart cities’, ‘internet-of-things’, ‘digital health for patients, professionals and policy makers’, ‘digital government’, fintech, culture and arts.

The objectives of creating a Big Data @ KU Leuven platform are:

• Internal branding: To act as a platform were we regroup all researchers active in machine learning and data science, where we detect opportunities and complementarities, where data scientist can network;
• External branding: to increase the visibility of the KU Leuven as a top-notch leading university in machine learning and data-driven sciences;
• Detecting funding opportunities by exploiting complementarity wherever possible;
• To build a platform that organizes university broad seminars, workshops, conferences, ...;
• To act as a catalyst between ‘horizontal’ expertise, and ‘vertical’ application areas (health, ICT, smart cities, industry 4.0, ...);
• To maintain a ‘portal’ website that supports the ‘big data platform’ in all of its endeavours and objectives;
• ...

The platform is coordinated by a ‘light-weight’ steering committee.