Arek Stopczynski Visit

We’re lucky to have Lab Alum Arkadiusz ‘Arek’ Stopczynski visiting the lab on January 9th and 10th. On the 9th, he’s busy being examiner at a PhD Defense, but on his second day in Denmark, he’s going to give a talk to tell us about what he’s been up to since starting at Google af couple of years ago.

In addition to working with us at DTU, Arek has also been a postdoc in Sandy Pentland’s lab at MIT’s MediaLab, he was an integral part of building the world’s first mobile brain scanner, and he’s given a great TEDx talk. And did I mention that we’ve just put out yet another paper together.

Below, you can find the talk details:

  • Date: January 10th, 2019
  • Time: 13:30-14:30
  • Place: DTU Building 321, in the first floor lab space
  • Title:  Data Science: Thinking Industry
  • Abstract: The practice of Data Science involves employing different methodologies, techniques, and tools, both deconstructive and constructive. In this talk we will discuss some fundamental differences in how Academia and Industry (exemplified by large tech companies) approach teaching and applying Data Science. These differences have important implications for how we teach students and conduct research.

I hope you can make it. Arek will stick around afterwards if you’d like to chat and hang out.

Complex Networks in Cambridge

I had an absolutely wonderful time at the Complex Networks 2018 conference last week in Cambridge, UK. I learned a lot and got caught up a bit with all the amazing work that’s going within complex network analysis and see some of the great new young researchers in the field.

At the community detection sessions, I also saw several talks that drew on our work on Link Clustering, expanding and building on those ideas. Now don’t get me wrong: That work is well cited, so I know people have been reading it. But my sense is that most of the citations are of the type “This is also something one could do” or from people applying the algorithm. Those are both great (and a much better fate than what befalls most of my papers), but it is still extra exciting to see people adopting, refining, and developing the ideas – using them for their own work with community detection methods!

Another exciting development was to see how lots of people are starting to apply machine learning (including embeddings, etc) to networks.

Finally, I also got to give my own keynote about our recent paper on the Chaperone Effect in Scientific Publishing. It was a brand new talk (since the paper just came out 2 days prior), but judging from the Twitter reaction, people liked it 🙂