Strategic Directions

ADS is active in many areas in terms of Research, Education and Entrepreneurship. Below we give a brief overview of the planned Strategic Directions for ADS over the next 5 years from 2018-2022

Research

-Foundations of Data Science

  • Data management systems architectures: To facilitate large-scale data analysis for statistical data and the analysis of network structures
  • Knowledge-driven analytics: To use reasoning frameworks and process analytics to study reproducible results for a wide range of data-driven problems in business and science
  • Machine learning foundations: To determine the quantity of data needed in order to reach concrete conclusions, with a specific focus on health
  • Models and techniques for analytics: To address big data variety and veracity challenges
  • Sustainable systems and software architectures: To build, monitor and analyse complex, scalable interacting systems for commercial and communication problems

-Health and Life Sciences

  • Bioinformatics: To use data science techniques to study low-level molecular and high-level behavioural problems in a wide range of applications
  • Deep learning and health: To use deep learning techniques in the medical imaging domain through the new AML4Healthcare Research Lab
  • Translational genomics: Leveraging millions of genomes into clinical applications

-Digital Business

  • Big data analytics: To investigate innovative ways for organisations to create real value from (big) data and analytics
  • Business model innovation: To find new innovative ways to create and appropriate value
  • Data Science in business: To bring data science technology to all functions of businesses (e.g. finance, HR, marketing, supply chain)
  • IoT: To enable business to get insights from sensor data
  • Human resources analytics: To enable businesses to improve and innovate through better insights on talent acquisition, workforce planning, and employee retention

-City Analytics

  • Urban analytics: To apply data science techniques to data from the City of Amsterdam on observations (e.g. traffic streams), interviews and questionnaires
  • Urban well-being and citizen empowerment: To develop data-driven solutions with a predictive and preventive focus on well-being as a system of interactions and events

-Responsible Data Science

  • Fairness, Accuracy, Confidentiality & Transparency (FACT): To future-proof responsible data science methods research is needed focussing on FACT. The methods developed will be inspired by questions from business, government, health, and science
  • Ethics: To explore ethics in relation to society and business further
  • Security: Development of systems to secure privacy

Education

To promote Data Science Education and explore innovative teaching opportunities:

  • At all levels: Bachelor, Masters and Postgraduate
  • In all domains, but with a specific focus on:
    • Encouraging data science study for actuarial sciences, business, economics, econometrics students
    • Extending existing programmes to other domains, such as health

Entrepreneurship

To stimulate data science related entrepreneurship by:

  • Linking with the Municipality of Amsterdam and local industries
  • Linking startups with our data science community
  • Stimulating students and researchers to become entrepreneurs