Jan Lemeire


Job description


Job Description
I am professor at the Department of Industrial Sciences (INDI) and Department of Electronics and Informatics (ETRO) of the Faculty of Engineering (IR) at the Vrije Universiteit Brussel (VUB).  My job consists of research and teaching. See  Parallel Website for more information about my educational activities.


Domains: self-learning robots.

Nowadays less actif
: GPU Computing, Parallel Performance, Causal Performance Models and Causal Structure Learning.
    Our work on the performance of GPUs for general purpose programming is published on www.gpuperformance.org.

Journals and book chapters
  1. Jan Lemeire, Jan G. Cornelis, Elias Konstantinidis. Analysis of the Analytical Performance Models for GPUs and Extracting the Underlying Pipeline Model, (PDF PREPRINT), Journal of Parallel and Distributed Computing, Volume 173, Pages 32-47, March 2023.
  2. Matthew Tonkin, Jan Lemeire, Pekka Santtila, Jan Winter. Linking Property Crime Using Offender Crime Scene Behaviour: A Comparison of Methods, Journal of Investigative Psychology and Offender Profiling, 2019.
  3. Jan Lemeire, Bruno da Silva, An Braeken, Jan G. Cornelis and Abdellah Touhafi. Efficiency Analysis Methodology of FPGAs based on Lost Frequencies, Area and Cycles, Journal of Parallel and Distributed Computing, 2018.
  4. Bob Andries, Adriaan Munteanu, Jan Lemeire, Optimized Wavelet-Based Texture Representation and Streaming for GPU Texture Mapping, Multimedia Tools and Applications, 2018.
  5. Paolo Viviani, M. Aldinucci, Roberto d’Ippolito, Jan Lemeire, Dean Vucinic, A Flexible Numerical Framework for Engineering—A Response Surface Modelling Application. In: Öchsner A., Altenbach H. (eds) Improved Performance of Materials. Advanced Structured Materials, vol 72. Springer, 2018.
  6. Tingting Liu, Jan Lemeire, Efficient and effective learning of HMMs based on identification of hidden states (pdf), Mathematical Problems in Engineering, 2017.
  7. Jan Lemeire, Francesco Cartella, The Forward Procedure for HSMMs based on Expected Duration, IEEE Signal Processing Letters, Vol. 23 No. 8, pp. 1116-1120, 2016.
  8. Bob Andries, Adriaan Munteanu, Jan Lemeire, Scalable Texture Compression using the Wavelet Transform, The Visual Computer, pp. 1-19, 2016.
  9. Jan G. Cornelis, Jan Lemeire, Tim Bruylants, Peter Schelkens, Heterogeneous Acceleration of Volumetric JPEG 2000 using OpenCL, International Journal of High Performance Computing Applications, pp 1-17, 2016.
  10. Jan Lemeire, Conditional Independencies under the Algorithmic Independence of Conditionals. Special issue on Causal Inference, Journal of Machine Learning Research (JMLR), 17(151):1−20, 2016.
  11. Petar Marendic, Jan Lemeire, Dean Vucinic, Peter Schelkens, A novel MPI reduction algorithm resilient to imbalances in process arrival times, Journal of Supercomputing, Volume 72, Issue 5, pp 1973-2013, 2016.
  12. Francesco Cartella, Jan Lemeire, Luca Dimiccoli and Hichem Sahli, Hidden semi-Markov Models for Predictive Maintenance, Mathematical Problems in Engineering, 2015.
  13. Alexander Statnikov, Nikita I. Lytkin, Jan Lemeire, Constantin F. Aliferis, Algorithms for Discovery of Multiple Markov Boundaries, Journal of Machine Learning Research (JMLR), 2013.
  14. Jan Lemeire, Dominik Janzing, Replacing Causal Faithfulness with Algorithmic Independence of Conditionals, Minds and Machines, 2012, DOI 10.1007/s11023-012-9283-1.
  15. Jan Lemeire, Stijn Meganck, Francesco Cartella, Tingting Liu, Conservative Independence-Based Causal Structure Learning in Absence of Adjacency Faithfulness, International Journal of Approximate Reasoning (IJAR), 2012.
  16. Jan Winter, Jan Lemeire, Stijn Megank, Jo Geboers, Gina Rossi, Andreas Mokros, Comparing the Predictive Accuracy of Case Linkage Methods in Serious Sexual Assaults, Journal of Investigative Psychology and Offender Profiling (JIPOP), 2012.
  17. Dominik Janzing, Joris Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniusis, Bastian Steudel, Bernhard Scholkopf, Information-geometric approach to inferring causal directions, Artificial Intelligence, 2012.
  18. Jan Lemeire, Kris Steenhaut, Abdellah Touhafi, When are Graphical Causal Models not Good Models? In Causality in the Sciences, J. Williamson, F. Russo and P. McKay, editors, pages 562-582, Oxford University Press, March 2011.
  19. Walter Colitti, Kris Steenhaut, Didier Colle, Mario Pickavet, Jan Lemeire and Ann Now? Integrated routing in GMPLS based IP/WDM networks, in Photonic Network Communications, 2010.
  20. Jan Lemeire, Kris Steenhaut. Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions, In JMLR Proceedings, Volume 6: Causality: Objectives and Assessment (NIPS 2008), 2010.
  21. Abdellah Touhafi, Kris Steenhaut, Jan Lemeire, De watertoren: een integratief project voor toekomstige ingenieurs, in Onderwijsvernieuwing: een continu proces, Thea Derks, Jan Driesen, Arnout Horemans, Frederik Questier, Kris Steenhaut en Hilde Van Lindt (eds.), VubPress, 2008. (Onderwijsvernieuwing & OnderwijsServiceCentrum)
  22. Jan Lemeire, Erik Dirkx, Walter Colitti, Modeling the Performance of Communication Schemes on Network Topologies, Parallel Processing Letters, Vol. 18, No. 2, 2008.
  23. Jan Lemeire, Erik Dirkx, Frederik Verbist, Causal Analysis for Performance Modeling of Computer Programs. Scientific Programming, Vol. 15, No 3, pp. 121-136, IOS Press, 2007.
  24. Jan Lemeire et al., Adaptive Load Balancing of Parallel Applications with Multi-Agent Reinforcement Learning on Heterogeneous Systems, Scientific Programming journal, Vol 12, No 2, 2004.

Conferences and workshops
  1. Jan Lemeire, Stefan Buijsman, Defining the Optimal Degree of Abstraction in Explanations with Kolmogorov complexity, BNAIC/Benelearn Conference on AI and Machine Learning, Delft, The Netherlands, November 2023.
  2. Jan Lemeire, Nick Wouters, Marco Van Cleemput, Aron Heirman, Contextual Qualitative Deterministic Models for Self-Learning Embodied Agents, Proceedings of the 4th International Workshop on Active Inference (IWAI 2023), 13-15 September 2023, Ghent, Belgium.
  3. De Smet, R., Thielemans, S., Lemeire, J., Braeken, A. & Steenhaut K., Educational software-as-a-service based on JupyterHub and nbgrader running on Kubernetes, 2022 IEEE 9th International Conference on e-Learning in Industrial Electronics (ICELIE), 2022
  4. Marcelo Brandalero et al. AITIA: Embedded AI Techniques for Embedded Industrial Applications. In Procs. of 2020 International Conference on Omni-layer Intelligent Systems, COINS 2020.
  5. Jan G. Cornelis, Jan Lemeire. The Pipeline Performance Model: A Generic Executable Performance Model for GPUs, PDP 2019.
  6. Bruno da Silva, Jan Lemeire, An Braeken, and Abdellah Touhafi, A Lost Cycles Analysis for Performance Prediction using High-Level Synthesis, in Proceedings of the 12th International Symposium on Applied Reconfigurable Computing (ARC), Rio de Janeiro, Brazil, 22-24 March, 2016.
  7. Jan Lemeire, Jan G. Cornelis, Laurent Segers, Microbenchmarks for GPU characteristics: the occupancy roofline and the pipeline model, Procs of 24th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), Heraklion, Greece, 2016.
  8. Jan G. Cornelis, Jan Lemeire, Tim Bruylandts and Peter Schelkens, Heterogeneous Acceleration of Volumetric JPEG2000, Procs. of PDP 2015.
  9. Bob Andries, Jan Lemeire, Adrian Munteanu, Optimized Quantization of Wavelet Subbands for High Quality Real-Time Texture Compression, Procs. of ICIP, 2014.
  10. Tingting Liu, Jan Lemeire and Lixin Yang, Proper Initialization of Hidden Markov Models for Industrial Applications, in Procs. of ChinaSIP, 2014.
  11. Bob Andries, Adriaan Munteanu, Jan Lemeire, and Peter Schelkens, Real-time texture sampling and reconstruction with wavelet filters, IEEE International Workshop on Multimedia Signal Processing (MMSP), Italy, pp.328 - 332, 2013.
  12. Jan Lemeire, Stijn Meganck, Albrecht Zimmermann and Thomas Dhollander, Detecting marginal and conditional independencies between
    events and learning their causal structure
    , The 12th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), Utrecht, The Netherlands, 2013.
  13. Laurent Segers, Bart Spiers, An Braeken, Bruno Da Silva, Erik H. D’Hollander, Jan Lemeire, Abdellah Touhafi and Jan G. Cornelis, Programming Framework for a Multi-Accelerator Multi-Core High-Performance Platform, HiPEAC 2013.
  14. Bruno Da Silva, An Braeken, Erik H. D’Hollander, Abdellah Touhafi, Jan G. Cornelis and Jan Lemeire, Performance and Toolchain of a Combined GPU/FPGA Desktop, International Symposium on Field-Programmable Gate Arrays, February 13, 2013.
  15. Bruno Da Silva, An Braeken, Jan Cornelis, Erik H. D’Hollander, Jan Lemeire, Abdellah Touhafi and Valentin Enescu, A combined GPGPU-FPGA High-Performance Desktop,  HiPeaC conference Paris, 2012.
  16. Petar Marendic, Jan Lemeire, Tom Haber, Dean Vucinic, Peter Schelkens, An Investigation into the performance of reduction algorithms under load imbalance, in Proceedings of International European Conference on Parallel and Distributed Computing (Euro-Par), Greece, 2012.
  17. Francesco Cartella, Tingting Liu, Stijn Meganck, Jan Lemeire and Hichem Sahli, Online adaptive learning of Left-Right Continuous HMM for bearings condition assessment, Proceedings of COMADEM 2012.
  18. Tingting Liu, Jan Lemeire, Francesco Cartella, Stijn Meganck, An improved segmentation-based HMM learning method for Condition-based Maintenance, Proceedings of COMADEM 2012.
  19. Ahmed Mabrouk, Jan Lemeire, Rune Erlend Jensen, Analyse causale de performance des algorithmes ?partir de données d’observation, in Proceedings of 6èmes Journées Francophones sur les Réseaux Bayésiens (JFRB), Nantes, 10-11 Mai 2010.
  20. Tom Haber, Petar Marendic, Dean Vucinic, Jan Lemeire, Philippe Bekaert. Exascale In-Situ Visualization using Raytracing, International Conference for High Performance Computing, Networking, Storage and Analysis (SC11), Seattle, 2011.
  21. Jan G. Cornelis, Jan Lemeire. Benchmarks Based on Anti-Parallel Patterns for the Evaluation of GPUs, Parallel Computing Conference (ParCo), Ghent, Belgium, September 2011.
  22. Jan Lemeire, Stijn Meganck, Francesco Cartella, Tingting Liu and Alexander Statnikov, Inferring the Causal Decomposition under the Presence of Deterministic Relations, Special session Learning of causal relations at the ESANN conference, Bruges, Belgium, April, 2011.
  23. Jan Lemeire, Stijn Meganck, Francesco Cartella, Robust Independence-Based Causal Structure Learning in Absence of Adjacency Faithfulness, in Procs of European Workshop on Probabilistic Graphical Models (PGM), Helsinki, Finland, 2010.
  24. Jan Lemeire, Causal structure learning and inductive inference based on Kolmogorov complexity, in Dagstuhl Seminar Proceedings, Machine learning approaches to statistical dependences and causality, September, 2009.
  25. Jan Lemeire, Yan Zhao, Peter Schelkens, Steve De Backer, Frans Cornelissen and Bert Torfs, Towards Fully User Transparent Task and Data Parallel Image Processing, in Procs. of Workshop on Parallel and Distributed Computing in Image Processing, Video Processing, and Multimedia, ISPA Symposium, 2009.
  26. Jan Lemeire, Kris Steenhaut, Constraint-based Causal Structure Learning when Faithfulness Fails, Annual machine learning conference of Belgium and The Netherlands (BeneLearn 2009), Tilburg, The Netherlands, 2009.
  27. Frans Cornelissen, Steve De Backer, Jan Lemeire, Bert Torfs, Rony Nuydens, Theo Meert, Peter Schelkens and Paul Scheunders. Fibered fluorescence microscopy (FFM) of intra epidermal nerve fibers--translational marker for peripheral neuropathies in preclinical research: processing and analysis of the data. In Proc. of Applications of Digital Image Processing XXXI, part of SPIE Symposium on Optical Engineering and Applications, August 2008, San Diego, CA USA.
  28. Frans Cornelissen et al., Fibered fluorescence microscopy of intra epidermal nerve fibers as translational marker for peripheral neuropathies in preclinical research ?Processing and analysis of the data, Knowledge for Growth 2008, Gent, Belgium.
  29. An Alternative Approach for Playing Complex Games like Chess, Annual machine learning conference of Belgium and The Netherlands (BeneLearn 2008), Spa, Belgium 2008. (slides of Presentation)
  30. Colitti Walter, Steenhaut Kris, Nowe Ann, Lemeire Jan, Multilayer Quality and Grade of Service Support for High Speed GMPLS IP/DWDM Networks, NBiS 2007, LNCS, Volume: 4658, pp: 187 - 196, 2007.
  31. The Representation and Learning of Equivalent Information in Causal Models. Technical Report IRIS-TR-0099, May 2006.
    • Proposes a solution to the well-known problem that deterministic relations cannot be represented by faithful Bayesian Nets.
  32. Causal Performance Models of Computer Systems: Definition and Learning Algorithms. Technical Report IRIS-TR-0100, 2006.
    • About the utilization of causal models in performance analysis.
  33. A Refinement Strategy for a User-Oriented Performance Analysis. (Euro-Pvm 2004, slides of presentation)
  34. Causal Models for Parallel Performance Analysis, Fourth PA3CT-Symposium, Edegem, Belgium, September 2004.
  35. Lookahead Accumulation in Conservative Parallel Discrete Event Simulation. The 2004 High Performance Computing & Simulation (HPC&S) Conference.
  36. Exploiting Symmetrical Properties for Partitioning of Models in Parallel Discrete Event Simulation. (PADS 2004, Kufstein, Austria)
  37. Complexity-Preserving Functions (DIMACS Workshop on Complexity and Inference 2003 presentation 'Complexity and Symmetry'
  38. Automated Experimental Parallel Performance Analysis (2002) (2nd PACT Symposium 2002
  39. Adaptive Load Balancing of Parallel Applications with Reinforcement Learning on Heterogenous Networks (2002) (DCABES 2002
  40. Performance Factors in PDES (2001) (ESM conference 2001)
  1. Causal Inference on Data Containing Deterministic Relations, February 2008.
  2. Causal Models as Minimal Descriptions of Multivariate Systems, 2006.
  3. Talk 'Practical Parallel Processing' at Royal Military Academy, May 2004, Brussels.
  4. Causes of Blocking Overhead in Message-Passing Programs (2003)
  5. Towards a Generalised Performance Analysis of Parallel Processing (2003)
  6. Natuurlijke Taal in de Formele Wereld van de Informatica (2001)
  7. Neural Networks: What's Inside. The Explicitness Hypothesis (2001)



The work presented in my thesis consists of a philosophical, theoretical and practical exploration of causal inference and its benefits for the performance analysis of (parallel) computer programs.

PhD final text    promotion text (ook in het Nederlands) and abstract 

Presentation given at the public defense (19 december 2007)

Listen to the recorded talk: Evaluation of causal discovery with Bayesian networks with the principle of Kolmogorov Minimal Sufficient Statistic, Thursday 16 October 2008
                Website of machine learning reading group
        Introduction to Bayesian networks, presentation given at Verhaert, 30th january 2009 (Up2Date seminars)

        Seminar at Machine Learning Group of the ULB, 15th November 2006 (MLG-ULB)

        General talk about my PhD research, 24th May, 2006 (one of the weekly ETRO seminars)


    I am responsible for the following courses:
   I'm the mentor of several students doing projects or their final thesis.

Jan  Lemeire
Vrije Universiteit Brussel    (VUB-map, find the VUB)
Faculty of Engineering, INDI dept.
Pleinlaan 2, B-1050 Brussels,  Belgium

Campus Etterbeek: building K, third floor, room K3.53
Tel  +32 2 629.16.79
Email : jan.lemeire@vub.be

Jan graduated from the VUB in 1994 and received his (masters) diploma of electrotechnics engineer. He did his thesis at the VUB Artificial Intelligence-lab in the context of expert systems. Then completed his studies with an additional masters degree in Computer Science (Faculty of Science), also at the VUB, in 1995.

At the start of his professional carreer, Jan worked for 3.5 years in the private sector: first as a programmer for Cap Gemini, an IT consulting firm, then for Warmoes & Van Damme, a company specialised in knowledge systems (now, partly Aktor). He developed his professional skills during these years, but also found out that research is his real passion. He therefore returned to the academic world to prove himself in a scientific carreer...

At first he was allocated on a project with Alcatel Bell on parallel simulation. In march 2001 he was employed as an assistent, teaching with a lot of enthusiasm and combining it with pursuing a PhD about parallel performance analysis and causal inference. In December 2007 Jan received his PhD and got a postdoc position as VUB doctor-assistant. Since then he participated in several projects focusing on probabilistic models and GPU computing.

In October 2008 he got a part-time professorship at the Electronics and Informatics department (ETRO), in which he is responsible for the master course Parallel Systems and, since October 2011, the bachelor course 'Informatica'. Since October 2012 he is for 90% part of the department of Industrial Sciences (INDI), for which he teaches the master course Computer Architecture and the bachelor's Electronics and Informatics courses.


Besides my job, my life consists mainly of: