Jefrey Lijffijt, Dr. Sc. (Tech)

Brief bio

Hi! I am Jefrey, a FWO [Pegasus]² Marie Skłodowska-Curie Fellow, working at Ghent University's Internet Technology and Data Science Lab (IDLab), in the Dept. of Electronics and Information Systems.

Together with people in the AI & Data Analytics group, I design algorithms and systems to extract knowledge and insight from data. My research is mostly about personalised, visual, and interactive data exploration.

• In this context, personalisation is about enabling algorithms to take intoaccount what is most relevant (to focus on) and what you already know (to discount for).
• Similarly, data visualization is a powerful method to communicate the contents of data and models to any type of end-user, whether that is an analyst, subject expert, manager, or the general public.
• In order to personalize the results, it is necessary to make algorithms and systems interactive, such that the person analysing some data can steer the analytics and modelling process.

Together, this triad of topics provides a powerful paradigm for building the next generation of data science tools.

Non-exhaustive list of active research topics:

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.

Recent highlights (2019/late 2018)

Publications

• Anes Bendimerad, Jefrey Lijffijt, Marc Plantevit, Celine Robardet, Tijl De Bie. Contrastive Antichains in Hierarchies. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 294 - 304, 2019. (Original [open access])
• Bo Kang, Kai Puolamäki, Jefrey Lijffijt, Tijl De Bie. A Constrained Randomization Approach to Interactive Visual Data Exploration with Subjective Feedback. IEEE Transactions on Knowledge and Data Engineering, online ahead of print. (Original [open access])
• Bo Kang, Jefrey Lijffijt, Tijl De Bie. Conditional Network Embeddings. In Proceedings of the International Conference on Learning Representations, 2019. (Original [open access])
• Florian Adriaens, Jefrey Lijffijt, Tijl De Bie. Subjectively Interesting Connecting Trees and Forests. Data Mining and Knowledge Discovery 33(4): 1088-1124, 2019. (Original [open access])
• Alexandru Mara, Jefrey Lijffijt, Tijl De Bie. EvalNE: A framework for evaluating network embeddings on link prediction, In Proceedings of the 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning, pp. 5 - 13, 2019. (Manuscript)

Pre-prints (arXiv)

• Bo Kang, Jefrey Lijffijt, Tijl De Bie. ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions, arXiv:1904.12694, 2019. (Manuscript)
• Xi Chen, Panayiotis Tsaparas, Jefrey Lijffijt, Tijl De Bie. Opinion Dynamics with Backfire Effect and Biased Assimilation, arXiv:1903.11535, 2019. (Manuscript)
• Florian Adriaens, Tijl De Bie, Aristides Gionis, Jefrey Lijffijt, Polina Rozenshtein. From acquaintance to best friend forever: robust and fine-grained inference of social tie strengths, arXiv:1802.03549, 2018. (Manuscript)

Travel

• Attending ECML-PKDD, Wurzburg, Germany, September 16 - 20, 2019.
• Attending ACM SIGKDD, Anchorage, Alaska, August 4 - 8, 2019.

Talks/events

• ECML PKDD 2019
• Organizing the Graph Embedding and Mining (GEM) workshop.
• ACM SIGKDD 2019
• Anes Bendimerad, Jefrey Lijffijt, Marc Plantevit, Celine Robardet, Tijl De Bie. Contrastive Antichains in Hierarchies, main research track.
• Junning Deng, Bo Kang, Jefrey Lijffijt, Tijl De Bie. Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach, Mining and Learning with Graphs workshop.
• Xi Chen, Panayiotis Tsaparas, Jefrey Lijffijt, Tijl De Bie. Opinion Dynamics with Backfire Effect and Biased Assimilation, Mining and Learning with Graphs workshop.
• Florian Adriaens, Cigdem Aslay, Tijl De Bie, Aristides Gionis, Jefrey Lijffijt. Discovering Interesting Cycles in Graphs, Mining and Learning with Graphs workshop.
• ICLR 2019
• Bo Kang, Jefrey Lijffijt, Tijl De Bie. Conditional Network Embedding, main track.
• Alexandru Mara, Jefrey Lijffijt, Tijl De Bie. EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction, Workshop on Reproducibility in Machine Learning.
• SDM 2019
• Alexandru Mara, Jefrey Lijffijt, Tijl De Bie. EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction, Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning.
• Ghent University Faculty
• Presented our poster 'Knowledge Discovery from High-dimensional Data' at the FEA Research Symposium at Ghent University, February 19, 2019. (Poster)

Organization

• The European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML-PKDD) will be hosted in Ghent in September 2020. I will be acting as PC Chair.

Contact info

Address:

IDLab, Ghent University, Technologiepark-Zwijnaarde 122, B-9052 Ghent, Belgium

Telephone:

+32 474 539 464

Email:

jefrey.lijffijt@ugent.be

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

Goto:

Curriculum Vitae

Career path

• ('17-'19) FWO [Pegasus]² Marie Skłodowska-Curie Fellow, embedded in the Internet Technology and Data Science Lab, Ghent Univesity
• ('16) Postdoc at the Data Science Lab at Ghent University and iMinds, working with Prof. Tijl De Bie on the FORSIED project
• ('14-'15) Research Associate at the University of Bristol, also with Tijl De Bie
• ('14) Postdoctoral Researcher at Aalto University with Prof. Aristides Gionis and Prof. Samuel Kaski
• ('09-'13) Doctoral dissertation at Aalto University, my advisor was Prof. Heikki Mannila. I graduated with distinction and I received an award from Aalto University School of Science for the best doctoral disseration of 2013
• ('08-'09) Before my doctoral studies, I have worked as a consultant in predictive analytics at Crystalloids, Amsterdam, and as a research intern at Philips Research, Eindhoven

Awards

• Best paper award Mining and Learning with Graphs Workshop @ KDD, 2018
• For the paper "From acquaintance to best friend forever: robust and fine-grained inference of social tie strengths", Florian Adriaens, Tijl De Bie, Aristides Gionis, Jefrey Lijffijt, Polina Rozenshtein. (Manuscript)
• Participant at the Heidelberg Laureate Forum, 2016
• Best Doctoral Dissertation of 2013, Aalto University School of Science
• Dr. Sc. (Tech.) degree awarded with distinction (highest mark possible) at Aalto University School of Science, Dec 2013
• For my PhD Thesis "Computational methods for comparison and exploration of event sequences". (Original)

Research grants

• Marie Skłodowska-Curie Fellowship (FWO-EU COFUND) Personalised, interactive, and visual exploratory mining of patterns in complex data (2017-2019).
• Co-supervisor FWO Project Data mining without spilling the beans: Preserving more than privacy alone (2017-2020 PI Prof. Tijl De Bie).
• Advisor in Academy of Finland Project Reassessing language change: the challenge of real time (2014-2018, PI Prof. Terttu Nevalainen).
• Funded PhD Position 2012-2013, Finnish Doctoral Programme in Computational Sciences.

Tutorials, panels and invited talks

• Jefrey Lijffijt, Tijl De Bie. Mining Subjectively Interesting Patterns in Data. Tutorial at ECML-PKDD 2018. (Website with slides)
• Jefrey Lijffijt. Personalised Pattern Mining. Invited talk at DECADE Symposium, LIRIS/CNRS, Lyon, 2017. (Slides)
• Jefrey Lijffijt. Word occurrence distributions, and their implications for statistical analysis and comparison of corpora. Invited talk at University of Helsinki, Department of Modern Languages, 2016. (Slides)
• Jefrey Lijffijt, Eirini Spyropoulou, Tijl De Bie. Making Sense of Relational Data. Tutorial at ECML-PKDD 2015. (Website with slides)
• Jefrey Lijffijt. ''So what?'' -- On acing your PhD. Invited talk at the doctoral consortium of ECML-PKDD 2015. (Annotated presentation)
• Stefan Evert, Gerold Schneider, Vaclac Brezina, Stefan The. Gries, Jefrey Lijffijt, Paul Rayson, Sean Wallis, Andrew Hardie. Corpus statistics: key issues and controversies. Panel discussion at Corpus Linguistics 2015, Lancaster, UK. (Slides)
• Jefrey Lijffijt. Are you talking Bernoulli to me? Significance testing and burstiness of words in text corpora. Department of Mathematics and Statistics, University of Jyväskylä, 11 November 2011, Jyväskylä, Finland. (Presentation)

Community services

Organisation of conferences, workshops, panels

• Bo Kang, Remy Cazabet, Christine Largeron, Polo Chau, Jefrey Lijffijt, Tijl De Bie. Workshop on Graph Embedding and Mining (GEM). Workshop at ECML-PKDD 2019, Wurzburg, Germany. (Website)
• Jefrey Lijffijt, Minsuk Kahng, Remco Chang, Polo Chau, Dafna Shahaf, Christos Faloutsos. KDD 2018 Workshop on Interactive Data Exploration and Analytics (KDD-IDEA). Workshop at ACM SIGKDD 2018, London, UK. (Website)
• Jefrey Lijffijt, Polo Chau, Jilles Vreeken, Matthijs van Leeuwen, Dafna Shahaf, Christos Faloutsos. KDD 2017 Workshop on Interactive Data Exploration and Analytics (KDD-IDEA). Workshop at ACM SIGKDD 2017, Halifax, Canada. (Website)
• Stefan Evert, Gerold Schneider, Vaclac Brezina, Stefan The. Gries, Jefrey Lijffijt, Paul Rayson, Sean Wallis, Andrew Hardie. Corpus statistics: key issues and controversies. Panel discussion at Corpus Linguistics 2015, Lancaster, UK. (Abstract)

Editor for journals

• Data Mining and Knowledge Discovery (DAMI)

Senior conference roles

• Program Co-Chair. European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML-PKDD), 2020.
• Senior Program Committee. ACM International Conference on Intelligent User Interfaces (IUI), 2019.

Reviewer for journals

• Nature Communications
• Data Mining and Knowledge Discovery (DAMI)
• Machine Learning Journal (MLJ)
• IEEE Transactions on Knowledge and Data Engineering (TKDE)
• ACM Transaction on Knowledge Discovery in Data (TKDD)
• International Journal of Corpus Linguistics (IJCL)
• Entropy

Program committee member for conferences and workshops

• ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015 - 2016, 2018 - now.
• The Web Conference (WWW), 2018 - now.
• AAAI Conference on Artificial Intelligence (AAAI), 2020.
• SIAM International Conference on Data Mining (SDM), 2018 - now.
• IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2018 - now.
• International Joint Conference on Artificial Intelligence (IJCAI), 2016, 2018.
• International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
• European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML-PKDD), 2012 - now.
• International Symposium on Intelligent Data Analysis (IDA), 2016 - 2017.
• Symposium on Visualization in Data Science (VDS) at IEEE VIS, 2018 - now.
• ACM SIGKDD Interactive Data Exploration and Analytics Workshop (KDD-IDEA), 2014 - 2018.
• Practical Theories of Data Mining Workshop (PTDM), 2012.

Students

• Alexandru Cristian Mara, PhD Thesis, Jul 2018 - now.
• Ruben Moortgat, MSc (Ir.) Thesis (Automatic detection of anomalies in DSL testing using Machine Learning), Jun 2018 - Jan 2019.
• Xi Chen, PhD Thesis, Aug 2017 - now.
• Junning 'Lemon' Deng, PhD Thesis, Apr 2017 - now.
• Ahmad Mel, PhD Thesis, Mar 2017 - now.
• Florian Adriaens, PhD Thesis, Sep 2016 - now.
• Bo Kang, PhD Thesis, June 2015 - now.
• Samir Tendjaoui, research intern, May - Aug 2016.
• Achille Aknin, research intern, Feb - Jul 2016.
• Matthew Bastow, MSc Thesis (patternVis: an interactive visualisation tool for RMiner), Feb - Sep 2015.
• Karmen Dykstra, research intern, July 2014 - Jan 2015.

Teaching

• Lecture + Exercise Data Visualization (Part of Big Data Science), Ghent University, Spring 2019.
• Assistent to Probability and Statistics, Ghent University, Spring 2018.
• Assistent to Probability and Statistics, Ghent University, Spring 2017.
• Assistent to Probability and Statistics, Ghent University, Spring 2016.
• Guest Lecture Text Mining & NLP (Part of Introduction to AI), University of Bristol, Spring 2015.
• Assistent to Algorithmic Methods of Data Mining, Aalto University, Fall 2012.
• Assistent to Algorithmic Methods of Data Mining, Aalto University, Fall 2011.
• Assistent to Statistical Significance Testing in Data Mining, Aalto University, Spring 2010.

Refereed publications

Journal articles

• Bo Kang, Kai Puolamäki, Jefrey Lijffijt, Tijl De Bie. A Constrained Randomization Approach to Interactive Visual Data Exploration with Subjective Feedback. IEEE Transactions on Knowledge and Data Engineering, online ahead of print. (Original [open access])
• Florian Adriaens, Jefrey Lijffijt, Tijl De Bie. Subjectively Interesting Connecting Trees and Forests. Data Mining and Knowledge Discovery 33(4): 1088-1124, 2019. (Original [open access])
• Bo Kang, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie. SICA: Subjectively Interesting Component Analysis. Data Mining and Knowledge Discovery 32(4): 949-987, 2018. (Original [open access])
• Matt McVicar, Benjamin Sach, Cédric Mesnage, Jefrey Lijffijt, Eirini Spyropoulou, Tijl De Bie. SuMoTED: An intuitive edit distance between rooted unordered uniquely-labelled trees. Pattern Recognition Letters 79: 52-59, 2016. (Original [open access])
• Jefrey Lijffijt, Eirini Spyropoulou, Bo Kang, Tijl De Bie. P-N-RMiner: A generic framework for mining interesting structured relational patterns. International Journal of Data Science and Analytics, 1(1): 61-76, 2016. (Original [open access], Code, Addendum)
• Jefrey Lijffijt, Terttu Nevalainen, Tanja Säily, Panagiotis Papapetrou, Kai Puolamäki, Heikki Mannila. Significance testing of word frequencies in corpora. Digital Scholarship in the Humanities, 31(2): 374-397, 2016. (Preprint, Original [free access])
• Jefrey Lijffijt, Panagiotis Papapetrou, Kai Puolamäki. Size matters: Choosing the most informative set of window lengths for mining patterns in event sequences. Data Mining and Knowledge Discovery, 29(6): 1838-1864, 2015. (Preprint, Original, Code)
• Jefrey Lijffijt, Panagiotis Papapetrou, Kai Puolamäki. A statistical significance testing approach to mining the most informative set of patterns. Data Mining and Knowledge Discovery, 28(1): 238-263, 2014. (Original, Itemset mining implementation)

Conference articles

• Bo Kang, Jefrey Lijffijt, Tijl De Bie. Conditional Network Embeddings. In Proceedings of the International Conference on Learning Representations, 2019. (Original [open access])
• Xi Chen, Jefrey Lijffijt, Tijl De Bie. Quantifying and Minimizing Risk of Conflict in Social Networks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 1197 - 1205, 2018. (Original [open access])
• Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamäki, Emilia Oikarinen, Tijl De Bie. Subjectively Interesting Subgroup Discovery on Real-valued Targets. In Proceedings of the IEEE International Conference on Data Engineering, pp. 1352 - 1355, 2018. (Original, Extended version at arXiv, Poster)
• Kai Puolamäki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl De Bie. Interactive visual data exploration with subjective feedback: An information-theoretic approach. In Proceedings of the IEEE International Conference on Data Engineering, pp. 1208 - 1211, 2018. (Original, Extended version at arXiv)
• Florian Adriaens, Jefrey Lijffijt, Tijl De Bie. Subjectively Interesting Connecting Trees. In Proceedings of the European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML-PKDD) - Part II, pp. 53 - 69, 2017. (Preprint, Original, Code)
• Jefrey Lijffijt, Terttu Nevalainen. A Simple Model for Recognizing Core Genres in the BNC. In Big and Rich Data in English Corpus Linguistics: Methods and Explorations (Studies in Variation, Contacts and Change in English 19). Research Unit for Variation, Contacts and Change in English, Helsinki, 2017. (Original [open access])
• Tias Guns, Achille Aknin, Jefrey Lijffijt, Tijl De Bie. Direct mining of subjectively interesting relational patterns. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pp 913 - 918, 2016. (Preprint, Original)
• Kai Puolamäki, Bo Kang, Jefrey Lijffijt, Tijl De Bie. Interactive visual data exploration with subjective feedback. In Proceedings of the European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML-PKDD) - Part II, pp. 214 - 229, 2016. (Preprint, Original)
• Bo Kang, Kai Puolamäki, Jefrey Lijffijt, Tijl De Bie. A tool for subjective and interactive visual data exploration. In Proceedings of the European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML-PKDD) - Part III, pp. 3 - 7, 2016. (Preprint, Original)
• Bo Kang, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie. Subjectively interesting component analysis: Data projections that contrast with prior expectations. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). (Preprint hosted by conference, Original)
• Tijl De Bie, Jefrey Lijffijt, Raúl Santos-Rodríguez, Bo Kang. Informative data projections: A framework and two examples. In Proceedings of the European Symposium on Artificial Neural Networks (ESANN), pp. 635 - 640, 2016. (Preprint, Original, Presentation)
• Jefrey Lijffijt, Eirini Spyropoulou, Bo Kang, Tijl De Bie. P-N-RMiner: A generic framework for mining interesting structured relational patterns. In Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA), pages 1-10. IEEE Computer Society, Washington, DC, USA, 2015. (Preprint, Original, Presentation)
• Matt McVicar, Cedric Mesnage, Jefrey Lijffijt, Tijl De Bie. Interactively exploring supply and demand in the UK independent music scene. In Proceedings of the European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML-PKDD) - Part III, pages 289-292. Springer-Verlag, Berlin-Heidelberg, 2015. (Demo, Preprint, Original)
• Karmen Dykstra, Jefrey Lijffijt, Aristides Gionis. Covering the egonet: A crowdsourcing approach to social circle discovery on Twitter. In Proceedings of the 9th International AAAI Conference on Web and Social Media (ICWSM), 2015. (Preprint, Original, Code+Data)
• Matt McVicar, Cédric Mesnage, Jefrey Lijffijt, Eirini Spyropoulou, Tijl De Bie. Supply and demand of independent UK music artists on the web. In Proceedings of the 2015 ACM Conference on Web Science, 2015. (Original [open access])
• Jefrey Lijffijt. A fast and simple method for mining subsequences with surprising event counts. In Proceedings of the European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML-PKDD 2013) - Part I, pages 385-400. Springer-Verlag, Berlin-Heidelberg, 2013. (Preprint, Original, Presentation)
• Jefrey Lijffijt, Tanja Säily, Terttu Nevalainen. CEECing the baseline: Lexical stability and significant change in a historical corpus. In Outposts of Historical Corpus Linguistics: From the Helsinki Corpus to a Proliferation of Resources (Studies in Variation, Contacts and Change in English 10). Research Unit for Variation, Contacts and Change in English, Helsinki, 2012. (Original [open access])
• Jefrey Lijffijt, Panagiotis Papapetrou, Kai Puolamäki. Size matters: Finding the most informative set of window lengths. In Proceedings of the European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML-PKDD 2012) - Part II, pages 451-466. Springer-Verlag, Berlin-Heidelberg, 2012. (Preprint, Original, Presentation, Poster)
• Turo Vartiainen, Jefrey Lijffijt. Premodifying -ing participles in the parsed BNC. In Corpus Linguistics and Variation in English: Theory and Description, pages 247-258. Rodopi, Amsterdam, 2012. (Preprint, Original, Presentation)
• Jefrey Lijffijt, Panagiotis Papapetrou, Kai Puolamäki, Heikki Mannila. Analyzing word frequencies in large text corpora using inter-arrival times and bootstrapping. In Proceedings of the European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML-PKDD 2011) - Part II, pages 341-357. Springer-Verlag, Berlin-Heidelberg, 2011. (Preprint, Original, Presentation, Poster)

Workshop articles

• Kai Puolamäki, Panagiotis Papapetrou, Jefrey Lijffijt. Visually controllable data mining methods. In Proceedings of the 2010 IEEE International Conference on Data Mining Workshops, pages 409-417. IEEE Computer Society, Washington, DC, USA, 2010. (Preprint, Original)
• Jefrey Lijffijt, Panagiotis Papapetrou, Jaakko Hollmén, Vassilis Athitsos. Benchmarking dynamic time warping for music retrieval. In Proceedings of the 3rd International Conference on Pervasive Technologies Related to Assistive Environments (PETRA), article 59. ACM New York, NY, USA, 2010. (Preprint, Original, Data, Code, Results)
• Jefrey Lijffijt, Panagiotis Papapetrou, Jaakko Hollmén. Tracking your steps on the track: Body sensor recordings of a controlled walking experiment. In Proceedings of the 3rd International Conference on Pervasive Technologies Related to Assistive Environments (PETRA), article 58. ACM New York, NY, USA, 2010. (Preprint, Original, Data)

Non-refereed publications

Letters to journals

• Jefrey Lijffijt, Stefan Th. Gries. Correction to Stefan Th. Gries' "Dispersions and adjusted frequencies in corpora". International Journal of Corpus Linguistics, 17 (1), 147-149, 2012. (Preprint, Original)

Technical reports

• Jefrey Lijffijt, Panagiotis Papapetrou, Niko Vuokko, Kai Puolamäki. The smallest set of constraints that explains the data: a randomization approach. TKK-ICS-R31, TKK Reports in Information and Computer Science, Espoo, May 2010. (Original)
• Jefrey Lijffijt, Ingrid C. M. Flinsenberg. Compression-based activity classification and motif discovery in time series of acceleration data. TN-2008-00521, Koninklijke Philips Electronics N.V., Eindhoven, September 2008.

Doctoral thesis

• Jefrey Lijffijt. Computational methods for comparison and exploration of event sequences. Doctoral dissertation, Aalto University School of Science, Dec. 2013. (Original)

Master's thesis

• Jefrey Lijffijt. Compression-based activity classification and motif discovery in time series of acceleration data. Master's thesis, Utrecht University, Sep. 2008. (Original)