BPI cluster meeting 30May’18

This week we will have a guest presenter, Gabriele Modena from ImproveDigital company. The place of the presentation is Pav.K1.6 and time is between 12:30-13:30.


Machine Learning Methods in Adtech

Short Abstract:

Machine Learning systems are used in adtech to drive decision making, revenue, and personalise the user’s online experience. ML is used to answer questions such as is the visitor a human or a bot? What creative should be displayed in order to maximise the probability of a click? What is the optimal reserve price for an impression? How many clicks will a new ad-placement system get?

Typically we need to answer these questions several tens of thousands of times per second, under soft real-time constraints.

In this presentation we’ll give an overview of use cases in the industry, and how machine learning is used at scale in the Improve Digital platform.

 About the company:

Improve Digital has the All-in-One Advertising Platform for Publishers, Content Providers and Broadcasters. Improve Digital annouces its mission as building smart, efficient, and responsible digital businesses for its enterprise customers. It creates the technology that makes advertising marketplaces possible.

You are warmly invited.

BPI cluster meeting 23May’18

This week we will have a guest presenter, dr. Kalliopi Zervanou, is currently a lecturer in Information and Computing Sciences in Utrecht University. The place of the presentation is Pav.K1.6 and time is between 12:30-13:30.


Linking multi-disciplinary data sources in the Time Capsule system



Time Capsule is a historical research system for botanical remedies from the New World in the early modern period (17-18th century): the historical evolution of economic importance, ethical attitudes, scientific interests, trade and knowledge circulation.


Historical data is scattered across collections developed for various domains and purposes. Its amount and complexity raises the need for a presentation allowing exploration and detailed inspection. Finally, the problem of information validation and sharing must be addressed. In Time Capsule we have i) integrated and linked multidisciplinary data sources and ii) developed an online research platform that supports data access, presentation, validation and sharing.


In our approach, data source integration entails concept mapping, not only across disciplines, but also in time. Thus, it calls for support for the scientific evolution from the 16th century onwards in re-classifying and re-defining concepts. Additionally, it entails dealing with phenomena of historical term variation and ambiguity which gradually give way to spelling standardisation and current nomenclature conventions in e.g. botany and biology. Furthermore, it requires addressing under-specificity and ambiguity of information found in historical sources while maintaining associations with potentially related concepts and context. Most importantly, it requires providing references for information provenance tracing and validation.

You are warmly invited.





BPI cluster meeting 2May’18

Our cluster meeting on 2nd May will be in Pav. K.16 between 12:30-13:30. This week we will have a guest lecturer Maurits Kaptein

The title and the abstract of Maurits Kaptein’s lecture are as follows:


Personalization and bandits with applications in health.



In this talk prof. Maurits Kaptein will talk about his work in the computational personalization lab at JADS (Den Bosch). Starting from a (contextual) multi-armed bandit formalization of treatment personalization Maurits will present his work on developing novel multi-armed bandit policies (e.g., bootstrapped thompson sampling), on software development to evaluate multi-armed bandit policies in the field (https://github.com/Nth-iteration-labs/streamingbandit), and on the use of this framework for personalization in (e)Health and online marketing. The talk will provide a broad overview of the work carried out by Maurits and his PhD students over the last five years.


Short Bio of Maurits Kaptein:

He received his Ph.D. with honors form the Eindhoven University of Technology, Eindhoven, the Netherlands. Next, he worked as a postdoctoral researcher at the Aalto school of Economics, Aalto, Finland. Afterwards he worked for 2 years as an assistant professor of Statistics and Research Methods at the University of Tilburg. He has previously (during his Ph.D. work) worked as a research scientist at Philips Research, Eindhoven, the Netherlands and as a distinguished visiting scholar at the CHIMe lab of Stanford University, Stanford CA, USA. He has also worked as an assistant professor in Artificial Intelligence (AI) at the Radboud University Nijmegen where he was the track leader of a master track called “Web and Language”.

BPI cluster meeting 25Apr’18

Our BPI cluster meeting on 25th  April will be a joint meeting with Data Science Center Eindhoven (DSC/e).

The DCS/e lecture will be given by Marie-Jeanne Lesot. She is an associate professor in the department of Computer Science Lab of Paris 6 (LIP6) and a member of the Learning and Fuzzy Intelligent systems (LFI) group.

Where: Kennispoort (Grote Zaal), J.F. Kennedylaan 2, Eindhoven.

When: 25th  April, as usual, between 12:30-13:30. (doors open at 12:00).

The title and the abstract of Marie-Jeanne Lesot’s lecture is as follows:


Extracting knowledge in linguistic form


Machine learning can be seen as aiming to allow users to understand the huge quantities of data they are faced with. One way to facilitate interpretation of the results consists in presenting them in natural language, offering linguistic expressions which may be easier to understand. The choice of such result formulation then has an impact on the machine learning techniques to be applied to the data. This talk will present three tasks in this framework, considering different types of data.

The first task aims at extracting gradual itemsets from numerical data, as well as contextual variants thereof, linguistically expressing information about the feature covariations, as illustrated by the example “the higher the speed, the greater the danger”. A second task aims at summarising temporal series, in particular their periodicity, using the specific quantifier “regularly”. In both cases, the question is to precisely define the associated semantics and to define efficient extraction algorithms.  A third task investigates the measure of the relevance of the linguistic terms used to express the summaries, both with respect to the data structure, in case of linguistic variables, and with respect to the cognitive interpretation, in case of approximate numerical expressions.

Short bio:

Marie-Jeanne Lesot obtained her PhD in 2005 from the University Pierre et Marie Curie in Paris. Since 2006 she has been an associate professor in the department of Computer Science Lab of Paris 6 (LIP6) and a member of the Learning and Fuzzy Intelligent systems (LFI) group. Her research interests focus on fuzzy machine learning with an objective of data interpretation and semantics integration and, in particular, to model and manage subjective information; they include similarity measures, fuzzy clustering, linguistic summaries, affective computing and information scoring.


BPI cluster meeting 4Apr’18

Our BPI cluster meeting on 4th  April will be in Pav. K.16 between 12:30-13:30.

During that session, Joao Paulo Carvalho will be our guest lecturer. Joao is from Instituto Superior Técnico, University of Lisbon, Portugal. He is nowadays on his sabbatical in our group. For detailed information about Joao Paulo Carvalho you can check the following link  https://www.l2f.inesc-id.pt/w/João_Paulo_Carvalho .

The title and the abstract of Joao’s talk is as follows:

Fuzzy Fingerprints: Identification and classification based on top-k values


Fuzzy Fingerprints (FFP) were developed as a technique to allow the identification of an individual out of a large number of suspects based on their usage habits. They were inspired by the fact that many types of data studied in the physical and social sciences can be approximated with a Zipfian distribution, where the frequency of an item is inversely proportional to its rank in the frequency table. Fuzzy Fingerprints efficiently use the implicit information contained in top-K most frequent data values to perform identification in large datasets. The term “fingerprint” is used in the sense that fingerprints are unique, and are usually left unintentionally, allowing us to identify their “owners”.

“Identification” can be seen as a specific classification task where the number of classes is unusually large. Despite being originally used as a “user identification” technique, FFP have been extended to identify and classify from single users to categories, topics or classes, and have shown to be competitive with machine learning techniques even when dealing with a small number of classes ,while exhibiting some interesting properties.

In this talk I will approach the ideas behind Fuzzy Fingerprints and show case studies and applications involving: identification of anonymous users based on their phone and web usage habits; text author identification based on their writing habits; classification and identification in social data (e.g. detecting tweets related to a given trending topic); classification based on medical text data; movie recommendation; etc.

BPI cluster meeting 28Mar’18

Our BPI cluster meeting on 28th  March will be in Pav. K.16 between 12:30-13:30.

During that session, one PhD student Jason Rhuggenaath will talk about his current studies.

The promotor of Jason is Prof. Uzay Kaymak, and co-promoters are dr. Yingqian Zhang and dr. Alp Akcay.


Please find the title and the abstract below:



Fuzzy decision trees



A popular method in machine learning for supervised classification is decision trees. In this work we propose a new framework to learn fuzzy decision trees using mathematical programming. More specifically, we encode the problem of constructing fuzzy decision trees using a Mixed Integer Linear Programming (MIP) model, which can be solved by any optimization solver.

We compare the performance of our method with the performance of off-the-shelf decision tree algorithm CART and Fuzzy Inference Systems (FIS) using benchmark data-sets. Our initial results are promising and show the advantages of using non-crisp boundaries for improving classification accuracy on testing data.


BPI cluster meeting 28Feb’18

The speaker is Joost van Twist. He obtained his MSc. Degree in the Eindhoven University of Technology . He worked in companies that are major players in their markets like Quintiq and Philips. Now he works at Viggo as a software engineer implementing planning and scheduling algorithms for Eindhoven airport.



Applications of operations research and data science at Viggo



At Viggo we are responsible for the ground handling at Eindhoven airport. In our organization of more than 400 employees and with an airfield that is continuously growing, there are many challenging reallife planning puzzles such as: Assigning parking stands and gates to planes, the scheduling of employees, and logistic puzzles for ground equipment and luggage. On top of that,  as the operations are being more and more digitalised, we have access to wide variety of data, that gives oppurtunities for doing data anlaysis. For example, being able to predict when steps in the operations will cause a delay in the flight. A lot of software is developed in-house which is unique for ground handling companies and this software is also used to perform various consultancy services.