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.
Research
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.
Publications
Journals and book chapters
- 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.
- 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.
- 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.
- Bob Andries, Adriaan Munteanu, Jan Lemeire, Optimized
Wavelet-Based Texture Representation and Streaming for GPU
Texture Mapping, Multimedia Tools and Applications,
2018.
- 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.
- Tingting Liu, Jan Lemeire, Efficient
and effective learning of HMMs based on identification of
hidden states (pdf),
Mathematical Problems in Engineering, 2017.
- 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.
- Bob Andries, Adriaan Munteanu, Jan Lemeire, Scalable
Texture Compression using the Wavelet Transform, The
Visual Computer, pp. 1-19, 2016.
- 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.
- 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.
- 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.
- Francesco Cartella, Jan Lemeire, Luca Dimiccoli and Hichem
Sahli, Hidden
semi-Markov Models for Predictive Maintenance,
Mathematical Problems in Engineering, 2015.
- Alexander Statnikov, Nikita I. Lytkin, Jan Lemeire,
Constantin F. Aliferis, Algorithms
for Discovery of Multiple Markov Boundaries,
Journal of Machine Learning Research (JMLR), 2013.
- Jan Lemeire, Dominik Janzing, Replacing
Causal Faithfulness with Algorithmic Independence of
Conditionals, Minds and Machines, 2012, DOI
10.1007/s11023-012-9283-1.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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)
- Jan Lemeire, Erik Dirkx, Walter Colitti, Modeling
the Performance of Communication Schemes on Network
Topologies, Parallel Processing Letters, Vol. 18, No.
2, 2008.
- 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.
- 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
- 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.
- 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.
- 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
- 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.
- Jan G. Cornelis, Jan Lemeire. The
Pipeline Performance Model: A Generic Executable
Performance Model for GPUs, PDP 2019.
- 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.
- 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.
- Jan G. Cornelis, Jan Lemeire, Tim Bruylandts and Peter
Schelkens, Heterogeneous
Acceleration of Volumetric JPEG2000, Procs. of PDP 2015.
- Bob Andries, Jan Lemeire, Adrian Munteanu, Optimized
Quantization of Wavelet Subbands for High Quality
Real-Time Texture Compression, Procs. of ICIP, 2014.
- Tingting Liu, Jan Lemeire and Lixin Yang, Proper
Initialization of Hidden Markov Models for Industrial
Applications, in Procs. of ChinaSIP, 2014.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Tingting Liu, Jan Lemeire, Francesco Cartella, Stijn
Meganck, An
improved segmentation-based HMM learning method for
Condition-based Maintenance, Proceedings of COMADEM
2012.
- 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.
- 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.
- Jan G. Cornelis, Jan Lemeire. Benchmarks
Based on Anti-Parallel Patterns for the Evaluation of GPUs,
Parallel Computing
Conference (ParCo), Ghent, Belgium, September 2011.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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)
- 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.
- 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.
- 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.
- A
Refinement Strategy for a User-Oriented Performance
Analysis. (Euro-Pvm
2004, slides
of presentation)
- Causal
Models for Parallel Performance Analysis, Fourth
PA3CT-Symposium, Edegem, Belgium, September 2004.
- Lookahead
Accumulation in Conservative Parallel Discrete Event
Simulation. The 2004
High
Performance Computing & Simulation (HPC&S)
Conference.
- Exploiting
Symmetrical Properties for Partitioning of Models in
Parallel Discrete Event Simulation. (PADS
2004, Kufstein, Austria)
- Complexity-Preserving
Functions (DIMACS
Workshop on Complexity and Inference 2003, presentation
'Complexity and Symmetry' )
- Automated
Experimental Parallel Performance Analysis (2002) (2nd PACT Symposium
2002)
- Adaptive
Load Balancing of Parallel Applications with Reinforcement
Learning on Heterogenous Networks (2002) (DCABES 2002)
- Performance
Factors in PDES (2001) (ESM
conference 2001)
Unpublished
- Causal
Inference on Data Containing Deterministic Relations,
February 2008.
- Causal
Models as Minimal Descriptions of Multivariate Systems,
2006.
- Talk
'Practical Parallel Processing' at Royal Military Academy,
May 2004, Brussels.
- Causes
of Blocking Overhead in Message-Passing Programs (2003)
- Towards
a Generalised Performance Analysis of Parallel Processing
(2003)
- Natuurlijke
Taal in de Formele Wereld van de Informatica (2001)
- Neural
Networks:
What's
Inside. The Explicitness Hypothesis (2001)
Projects
- 2021-2022: Classification Algorithms for Crime Linkage
(together with University of Birmingham & Leicester)
- 2018: Development of parameteric strategic assessment
tool for the evaluation of MEMS sensors (with
Verhaert)
- 2017: Imec-Hi2 initiative on scheduling &
parallelization
- 2016-2017: Accordion Fringe Interferometer on GPU
(together with Nobel Biocare)
- 2015 – 2016. Bahamas:
Big dAta and High-throughput Analysis in life and
MAterials Sciences.
- 2014 - 2016. Parallel Historical Data Search.
Feasibility Study with D Square.
- 2013 - 2016. MACH:
MAssive Calculations on Hybrid systems.
- 2013 - 2014. MMIQQA:
Multimodal Microscopic Imaging: Quality, Quantification
and Acceleration.
- 2011 - 2013.
The GUDI Project: A
combined GP-GPU/FPGA desktop system for accelerating image
processing applications.
- Responsibility: Acquire the knowhow
about GPUs for lowering the threshold for companies
(target: medical sector, image processing).
- 2010 - 2013.
The Exascience Project: Solar Flare Prediction on
Intel’s Future Exascale Supercomputer.
- Responsibility: Performant
visualization of fine-grain massively parallel simulation.
- 2009 - 2013.
The use of dynamic models
for Prognostics for Optimal
Maintenance (POM).
- Responsibility:
learning dynamic graphical probabilistic models (such as
dynamic Bayesian networks, graphical duration models or
hidden Markov models) from data about the degradation
process of industrial machines.
- 2008 - 2009. DMOBISA:
on Distributed Mosaicing of Medical Image Sequences.
- Responsibility: a transparaent
solution for parallelizing the image processing based on clusters,
multicores and GPUs.
PhD
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)
Talks
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)
Education
I am responsible for the following courses:
I'm the mentor of several students doing projects or
their final thesis.
