Jefrey Lijffijt, Dr. Sc. (Tech)

I am a FWO [Pegasus]2 Marie Skłodowska-Curie Fellow, working at Ghent University's Internet Technology and Data Science Lab.

Broadly I work on personalised interactive data exploration. We design algorithms and systems to extract knowledge and insight from data. In this context, personalisation is about enabling algorithms to take into account what is most relevant (to focus on) and what you already know (to discount for).

IDLab, Ghent University, Technologiepark-Zwijnaarde 19, B-9052 Ghent, Belgium
+32 474 539 464

You can find me on Twitter, Facebook, LinkedIn, and Google Scholar.

Recent activity (2018)


Recent and upcoming travels


Pre-prints (arXiv)


bio/CV, publications

Brief bio

Jefrey Lijffijt is a FWO [Pegasus]2 Marie Skłodowska-Curie Fellow hosted at Ghent University, Belgium. He obtained his Dr. Sc. (Tech.) diploma in Information and Computer Science with distinction in December 2013 from Aalto University, Finland. His dissertation received the Best Doctoral Thesis of 2013 award from the Aalto University School of Science. He holds B.Sc. and M.Sc. degrees in Computer Science from Utrecht University. He has worked as a Research Associate at University of Bristol, as a Consultant in Predictive Analytics at Crystalloids, Amsterdam, and as a Research Intern at Philips Research, Eindhoven. His research interests include theory and practice of statistical modelling and pattern mining in various data, such as mining of interesting patterns in relational data, sequences, and graphs, as well as data visualisation, interactive data analysis, visual analytics, statistical testing, and information theory.

Research topics

I am interested in theory and practice of statistical modeling and pattern mining in various data. Currently, I am working mostly on a framework for pattern mining based on subjective interestingness in data analysis. As a side project I am active in analysis of natural language corpora. I have also been working on interactive visual data exploration, graph mining and social network analysis, mining web data and analysis of citation networks. More generally, I am interested in mining interesting/surprising patterns in transactional, sequential, relational data, and graphs, as well as in text mining, natural language processing, information theory, and statistical significance testing.

In 2010, together with Kai Puolamaki and Panagiotis Papapetrou, I proposed a more general definition of patterns that we may mine from data. At roughly the same time, Tijl De Bie was independently working on similar concepts and proposed the same definition. Tijl's framework, known as FORSIED, also included a specific approach to quantify the interestingness of such patterns, motivated by Information Theory. My current work is based on this notion. In principle it could be applied to any data exploration setting, but to execute this in practice, many questions remain. For example, in algorithm design/optimization, visualization (of data and results), interaction design/HCI, and scalability. Hence I am interested in all these areas.


Exploratory data mining, interactive data analysis, pattern mining, data visualisation, visual analytics, hypothesis testing, statistical significance, maximum entropy modeling, subjective interestingness, graph mining, natural language corpora.

Career path


Research grants

Tutorials, panels and invited talks

Community services

Organisation of conferences, workshops, panels

Editor for journals

Senior conference roles

Reviewer for journals

Program committee member for conferences and workshops



Refereed publications

Journal articles

Conference articles

Workshop articles

Non-refereed publications

Letters to journals

Technical reports

Doctoral thesis

Master's thesis