Plos ONE publication and hiring

Hi All,

I am delighted to share the fact that today PLOS ONE will publish our new work on Lock-in feedback. Its called:

“Uncovering noisy social signals: using optimization methods from experimental physics to study social phenomena”

and its pretty cool! Here is an author copy of the work.

Also, note that we are hiring a number of PhD students for diverse projects (adaptive clinical trials, sequential decision making, human-computer bandit problems, etc.). If you are looking for a PhD position please do let us know (by sending a mail to maurits [at] maurits kaptein [dot] com).


JADS, course, and some other updates


I wanted to share with you the JADS seminar series that Linnet Taylor and I are organizing together. Every week, during lunch on Tuesday, we house a seminar at JADS. Here are a few of the talks: The seminars are free to attend, and we have a great lunch so feel free to drop by if you see an interesting topic!

Also, we have just started teaching a new data mining course. You can find more information here.

Finally, we are still awaiting a number of reviews on recent submission; I will share these as soon as we know more.

For now, that’s it!



oFMLR package on Github


We just released a new version of the oFMLR package for fitting (finite) mixtures of logistic regression in data streams. You can find the code here:

It is very easy to use:


suffices to get started.

Have fun, and let us know if anything does not work. Note that a publication describing the package in more detail including examples is coming up shortly.

A great 2017

Hi all,

First of all, a great 2017 to all of you! We are working on some awesome research projects in the coming year, so I am really looking forward to it!

However, for now I wanted to share our joint work that is presented at HICSS. Unfortunately I could not travel there myself, but our lab co-organizes a minitrack their and published some new work. We have the following contributions:

  • Parvinen, P., Kaptein, M., Pöyry, E., & Hamari, J. (2017). Introduction to Customer Analytics and Data-Led Omnichannel Commerce Minitrack.  In Proceedings of the 50th Annual Hawaii International Conference on System Sciences (HICSS), Hawaii, USA, January 4-7, 2017.
  • Pöyry, E., Hietaniemi, N., Parvinen, P., Hamari, J., & Kaptein, M. (2017). Personalized Product Recommendations: Evidence from the Field. In Proceedings of the 50th Annual Hawaii International Conference on System Sciences (HICSS), Hawaii, USA, January 4-7, 2017.

Finally, note that we are organizing a seminar series at JADS. For details see:

Wrap up update

Hi all,

Hope you are doing well! In the (probably) last post in 2016 I want to take the opportunity to thank you for reading our updates and to wish you an awesome 2017!

Here are a few minor updates:

• Robin van Emden, Davide Iannuzzi and I finally submitted a more thorough version of our recent Arxiv paper (; we hope to be able to announce more soon!
• Together with Reza Mohammadi we are working on implementing novel sampling methods for additive regression tree models. See our response at We would kindly like to thank Matthew Pratola for sharing his code with us!
• We have finally written up a full paper about StreamingBandit; we hope to be able to share it soon! (for now, see
• We will be presenting new work during “Physics@Veldhoven”; see also
• At JADS we are starting our seminar series. On the 24th of January we will have Frank Buytendijk speaking, and on the 31st Martijn Willemsen will take the stage; we are lookking forward to the inspiring talks (

Well, we will keep it at this for now. Just one final note: we are looking for PhD candidates. If you are interested, send me a mail with your resume at maurits [at] maurits kaptein [dot] com.


Decoy work published

Hi all,

I am happy to announce that the work Prof. Davide Iannuzzi, Robin van Emden, and I did on the decoy effect (using lock-in feedback for optimal placements of decoys) has now been published! You can find the work at:

Besides this note that we have pushed another update of StreamingBandit quite recently ( and hope to publish an overview article about the development and use shortly.


ERC and Palgrave

Hi All,

The last few weeks have been busy teaching a new course called “Data Statistics”; I am teaching the course together with Prof. Dr. Edwin van den Heuvel and its pretty awesome. We will try to share the lecture notes here soon.

Also, good news, our (Robin van den Emden, Davide Iannuzzi, and Maurits Kaptein) paper on the decoy effect has been accepted by Palgrave Communications; we are awaiting the proofs and will than share a copy.

Finally, I have submitted an ERC starting grant application; here is the abstract:

“In 2011 listed the book The Making of a Fly for a mere $23,698,655.93 (plus $3.99 shipping). The incident is a famous example of autonomous decision making by computers gone awry. As humorous as this glitch may be, erroneous decisions by computers could well be far less amusing; what if your digital health assistant gives you the fully automated and fully personalized, but also quite deadly, recommendation to take 15 grams of paracetamol?

On the other hand, computers could be extremely helpful for making sequential decisions in the face of uncertainty. It is not far-fetched that a computer could learn the exact dosage of paracetamol that is best for you personally, outperforming your general practitioner. These algorithms already exist in a narrow sense; in online selling, for example, the prices of products are often automatically determined and personalized. However, these algorithms are not feasible outside the online selling domain since their success depends on assumptions that are violated in other domains. Furthermore, existing methods can be regarded as a “black box,” because it is unclear why a given result is produced. When similar methods are used in impactful domains such as healthcare, this lack of information is unacceptable: we need to keep the human in the loop to inspect, and possibly alter, computer-suggested decisions. Finally, when these algorithms are used to select personalized treatments, we need to ensure the privacy of the people involved.

Combining my expertise in human-computer interaction (HCI) and methods for sequential decision making, I have designed a research program to develop responsible methods for shared sequential decision making by computers and humans. The program is developed in four projects in which my team and I will relax current assumptions, allow humans to inspect and alter computer-generated decisions, and develop methods to ensure privacy when sequential decision algorithms are used to personalize treatments.”

Let’s see how that goes….

Finally, our data science program is alive and kicking with many cool events (check, and my new book is coming up…