23 blogs
  • Managing/Being a Master’s Student during a Pandemic

    For the past five years, Elsevier has been an enthusiastic participant in the UvA Master’s Student programme. In total, more than 45 students have been supervised by researchers across the company, which has led to 12 new recruits for our Data Science teams.

    • Magdalena Mladenova
      Magdalena Mladenova
    • Anita de Waard
      Anita de Waard
    • Thom Pijnenburg
      Thom Pijnenburg
  • Data as a material for fashion: How treating data as a material enables a new future for design

    Data Science is rapidly changing industries around the world, yet the digital transformation remains difficult for Fashion. Fashion (Design, Business, Branding, and Marketing) has never been known for maths geniuses. (There are a few, but they keep it a secret.) While maths and data may not be a given in the industry, people who work in fashion are material experts. So what would it mean if we treated data as if it were a material?

    • Troy Nachtigall
      Troy Nachtigall

  • Programming Training for Refugees

    In August 2020, VodafoneZiggo and Accenture wrapped up their three-month CodeMasters training programme for refugees. The training course was tailor-made to help refugees integrate in the Dutch labour market by teaching participants to write computer code.

    • Gabriel Lopez
      Gabriel Lopez
  • Making the sum greater than the parts – FAIR Data

    Obtaining access to the right data is a first, essential step in any Data Science endeavour. But what makes the data “right”?

    • Jaap Heringa
      Jaap Heringa
  • Digital Humanism

    For a long time, Informatics has been viewed as a foremost technical scientific discipline; an idea aptly captured by the name "Computer Science". This view may have been justified in the past, but as our society becomes increasingly digitized, ICT technologies undeniably have a full-scale fundamental impact on society at large. An international and multi-disciplinary group of academics seek to analyse this new digital society and to ensure that technology is developed in line with human values and needs.

    • Hans Akkermans
      Hans Akkermans
  • The Discovery Lab: the pearls and perils

    Elsevier reflects on its experience in working with the University of Amsterdam and the VU Amsterdam to establish the Discovery Lab. What was the most important element for collaboration and what were the success factors that led to the launch of the Lab?

    • Mark Siebert
      Mark Siebert
  • IoT as an enabler of AI: Revolutionizing fish production

    With the use of IoT and AI, the Blue Planet Ecosystems (BPE) bioreactor can farm fish in any location. The founders of the biotech startup want to revolutionize methods of food production. In their artificial ecosystems, they produce fish from sunlight. In this concept, algae, water, sunlight and IoT support fish-farming in containers.

    • Lukas Winkler
      Lukas Winkler
  • Responsible Artificial Intelligence in Practice

    AI has created a wealth of opportunities for innovation across many domains. However, along with these opportunities comes unexpected and sometimes unwanted consequences. For example, algorithms can discriminate or lead to unfair treatment of groups of people. This calls for a responsible approach to AI.

    • Pascal Wiggers
      Pascal Wiggers
  • AI for Social Good & Equality of Opportunity

    AI for Social Good is on the rise. Many organisations in the public, private and nonprofit sectors are deploying AI for positive social initiatives, with varying degrees of success. The potential of AI to reduce socio-economic inequality, one of the UN’s Sustainable Development Goals, is yet to be realized. The Civic AI Lab was launched to address this challenge.

    • Sennay Ghebreab, Hinda Haned, Jacco van Ossenbruggen
      Sennay Ghebreab, Hinda Haned, Jacco van Ossenbruggen
  • Europe needs its own Google!

    On October 20th 2020, the US Department of Justice announced a lawsuit against Google for violating antitrust laws. On November 18th and 19th 2020, the GAIA X Summit will take place. The GAIA X project, a collaboration between the German and French governments and industry, aims to build a European cloud infrastructure for data. Although these two events seem unrelated, they share one clear objective: to limit the power of the existing Big Tech elite.

    • Marc Salomon
      Marc Salomon
  • Why we need the UvA Data Science Centre

    As more data becomes available and the applicability of data science increases, the University of Amsterdam is looking to capitalise on these factors. Its mission is to enable all its faculties to take advantage of cutting-edge data science technology to further research across all domains. With this in mind, the UvA Data Science Centre will be launched in 2021.

    • Paul Groth
      Paul Groth
  • AI Research with China: to Collaborate or not to Collaborate – is that the Question?

    Many forms of collaborations with Chinese companies have seen increased scrutiny in the last few years. Politicians and public figures challenge collaborations, citing China’s human-rights abuses and the Chinese state’s far-reaching control of “public” companies. At the same time, China has established itself as a world-leader in AI research, technology and innovation. Where does this leave us in deciding whether or not to collaborate with Chinese colleagues?

    • Lynda Hardman
      Lynda Hardman
  • AI & Health in Amsterdam

    Nowadays it is hard to miss all the impressive headlines on AI and Data Science covering many societally relevant domains, including healthcare. When zooming in on the health domain, however, we see that the number of applications that are currently helping patients or doctors is still limited.

    • Mark Hoogendoorn, Paul Elbers
      Mark Hoogendoorn, Paul Elbers
  • Linked Data Innovation for a Smarter Government

    Government organisations have a lot to gain by incorporating linked data into their processes. Triply, a startup founded by two researchers from the Knowledge Representation and Reasoning (KR&R) group at VU Amsterdam, is working together with different Dutch ministries to improve efficiency using linked data innovations.

    • Wouter Beek
      Wouter Beek
  • Increasing Data Science and AI Diversity

    As one of the most diverse cities in Europe, Amsterdam has the potential to be a front-runner in diversifying its academic and corporate workforce. However there is still much improvement to be made reaching gender parity, let alone effectively reflecting the city’s diverse population in those working in STEM fields.

    • Animesh Trivedi
      Animesh Trivedi
  • Safe & Flexible Testing of COVID-19 Treatments: flexibly combining results from many trials around the world

    Recently, the Machine Learning group at CWI has developed a method for statistical hypothesis testing that are safer and more flexible than traditional ones. While until March 2020, most work was theoretical, now the time has come to put "safe testing" to the test: in a collaboration with UMC Utrecht and Radboud UMC, our method is employed for combining results of several COVID-19 related clinical trials that are currently running in a number of hospitals in various countries.

    • Peter Grünwald
      Peter Grünwald
  • A Practitioner’s Perspective on Fairness in AI

    The ubiquity of data-powered solutions that have an increasing impact on our lives has been raising legitimate concerns about potential (unwanted) biases and discrimination. Data Science and AI communities are becoming increasingly aware of the need for fairness in AI developments; yet, how close are we to achieving fair AI for all?

    • Aysenur Bilgin, Hinda Haned
      Aysenur Bilgin, Hinda Haned
  • Estimating Reproducibility of AI Research

    Being able to reproduce research is a key aspect of creating knowledge. If a study can be reproduced by another lab then the validity of the findings are confirmed. This is particularly important in AI research with questions around explainable and trustworthy AI.

    • Iris Kisjes
      Iris Kisjes
  • Collaborative data analysis using SWISH DataLab

    The SWISH DataLab addresses two of the main bottlenecks of Data Science – that of bringing data from different sources together, and cleaning and selecting data that is relevant for further analysis. 

    • Jan Wielemaker
      Jan Wielemaker
  • AI Technology for People

    The effect of AI on our daily lives continues to increase. Rather than reacting to the constant advancements in technology, Amsterdam has chosen to place itself at the forefront of these developments with the initiative AI Technology for People.

    • Marcel Worring
      Marcel Worring
  • The Data Studio: Innovating How We Learn and Teach Data Science and AI

    There is an international shortage of Data Science students. Educators need to scale up and update their programmes in an attempt to meet the increasing demand for well-educated, talented students. Is this “yet another hype” or do we need to innovate education in these fields in a structural way?

    • Nanda Piersma
      Nanda Piersma
  • The Role of Corporates in AI Ecosystems

    AI technologies and their applications are transforming our society and economy. A key to benefitting from this transformation is to build strong AI ecosystems where the most important ingredient is talent - of which there is a world-wide shortage. Other important ingredients are knowledge institutions, startups and scaleups, the availability of venture capital, and finally the presence of research labs of large, internationally recognised corporations, such as Google, Facebook, Bosch, Qualcomm, Microsoft, Philips and Amazon.

    • Max Welling
      Max Welling
  • The Quest for Hybrid Intelligence

    In late 2016 Prof Geoffrey Hinton, the godfather of modern neural networks, said that it is “quite obvious that we should stop training radiologists” as image perception algorithms are very soon going to be demonstrably better than humans. "From 2020, you will be a permanent backseat driver," The Guardian stated in 2015. Fully autonomous vehicles will "drive from point A to point B and encounter the entire range of on-road scenarios without needing any interaction from the driver”, Business Insider wrote in 2016.

    • Frank van Harmelen
      Frank van Harmelen