Mathieu Guillame-Bert
Postdoctoral Fellow | Carnegie Mellon University
Carnegie Mellon University
Robotics Institute
5000 Forbes Ave
Pittsburgh, PA 15213
e-mail:
Phone:

Research Interests

My name is Mathieu Guillame-Bert, and I am working at CMU (Carnegie Mellon University) since 2013 as a Postdoctoral Fellow for the Auton-Lab (Robotic Institute). I defended my PhD in France in 2012 at the INRIA Research Lab in the PRIMA team under the supervision of James L. Crowley. I graduate from the Master of Advance Computing of the Imperial College of London, and from the "French Grande Ecole" ENSIMAG (Ecole Nationale Supérieure d'Informatique et de Mathématiques Appliquées) in 2009.

I am studying the automated extraction of patterns from large temporal datasets, as well as the use of those models for automated prediction, user interpretation and automated reasoning. Application domains I have worked on include Medical Diagnostics, Medical Forecastion, Human Activity Recognition and Forecasting, Banking Activity Patterns, Fault Detection and Prediction on complex systems, automatic Foreign exchange market trading, and a bit of Robotics.

Publications

2016

Journals/Conference Proceedings

▪ Learning Temporal Rules to Forecast Instability in Continuously Monitored Patients
Mathieu Guillame-Bert, Artur Dubrawski, Donghan Wang, Marilyn Hravnak, Gilles Clermont, Michael R. Pinsky
Journal of the American Medical Informatics Association (JAMIA), 2016

Tutorial

▪ Honey programming language tutorial
Introduction to the Honey programming language which is designed to efficiently process time structured datasets.
[html ]
▪ Event Viewer video tutorial
Introduction to the Event Viewer software which is designed to visualise large time structured datasets.
[html ]

Article

▪ Batched Lazy Decision Trees
Mathieu Guillame-Bert and Artur Dubrawski
ArXiv.org repository
[pdf ][arXiv ]

2015

Presentation

▪ Forecasting escalation of cardio-respiratory instability using noninvasive vital sign monitoring data
M. Guillame-Bert, A. Dubrawski, L. Chen, MT. Hravnak, G. Clermont, MR. Pinsky
ESICM 2015 (European Society of Intensive Care Medicine)
[pdf ]
▪ Detection of hemorrhage by analyzing shapes of the arterial blood pressure waveforms
S. Romero Zambrano, M. Guillame-Bert, A. Dubrawski, G. Clermont, MR. Pinsky
ESICM 2015 (European Society of Intensive Care Medicine)
[pdf ]

2014

Workshop

▪ Learning Temporal Rules to Forecast Events in Multivariate Time Sequences
Mathieu Guillame-Bert and Artur Dubrawski
NIPS Workshop 2014 (Neural Information Processing Systems Foundation)
[pdf ]

Presentation

▪ Utility of Empirical Models of Hemorrhage in Detecting and Quantifying Bleeding
Mathieu Guillame-Bert, Artur Dubrawski, Karen Chen, Andre Holder, Gilles Clermont, Marilyn Hravnak, and Michael Pinsky
ESICM 2014 (European Society of Intensive Care Medicine)

2013

Presentation

▪ Learning Temporal Rules to Forecast Instability in Intensive Care Patients
Mathieu Guillame-Bert, Artur Dubrawski, Karen Chen, Andre Holder, Gilles Clermont, Marilyn Hravnak, and Michael Pinsky
ESICM 2013 (European Society of Intensive Care Medicine)
[pdf ]
▪ Learning Temporal Rules to Forecast Instability in Intensive Care Patients
Mathieu Guillame-Bert, Artur Dubrawski, Karen Chen, Andre Holder, Gilles Clermont, Marilyn Hravnak, and Michael Pinsky
INFORMS Healthcare 2013
▪ Artifact patterns in continuous noninvasive monitoring of patients
Hravnak M., Chen L., Bose E., Fiterau M., Guillame-Bert M., Dubrawski A., Clermont G., Pinsky MR.
INFORMS Healthcare 2013
▪ Is there an information hierarchy among hemodynamic variables for early identification of occult hemorrhage?
Andre Holder, Mathieu Guillame-Bert, Karen Chen, Peter Huggins, Artur Dubrawski, Marilyn Hravnak, Gilles Clermont, Michael Pinsky.
Journal of Critical Care vol 6
▪ Does advanced treatment of existing physiologic data allow for earlier detection of occult hemorrhage?
Holder A., Guillame-Bert M., Chen K., Huggins P., Dubrawski A., Hravnak M., Clermont G.
Journal of Critical Care vol 6

Tutorial

▪ The TITARL algorithm
An interactive tutorial to understand the TITARL algorithm (Data-Mining algorithm on symbolic time sequences).
[html ]

2012

Journals/Conference Proceedings

▪ Learning Temporal Association Rules on Symbolic Time Sequences
Mathieu Guillame-Bert and James L. Crowley
In Proceedings of the 2012 4th Asian Conference on Machine Learning, Singapore, 2012
[pdf ]
▪ Planning with Inaccurate Temporal Rules
Mathieu Guillame-Bert and James L. Crowley
In Proceedings of the 2012 IEEE 24rd International Conference on Tools with Artificial Intelligence, Athens, Greece, 2012
[pdf ]

Presentation

Report

▪ PhD's Thesis, Learning Temporal Association Rules on Symbolic Time Sequences
Under the supervision of Pr. James L. CROWLEY
PRIMA Team – INRIA Lab. – Grenoble - France, 2012
Committee: Pr. Malik Gha llab, Pr. Paul Lukowicz, Dr. Artur Dubrawski, Pr. Augustin Lux
[pdf ]

2011

Journals/Conference Proceedings

▪ New Approach on Temporal Data Mining for Symbolic Time Sequences: Temporal Tree Associate Rules
Mathieu Guillame-Bert and James L. Crowley
In Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
[pdf ]
▪ Predicting Home Service Demands from Appliance Usage Data
Kaustav Basu, Mathieu Guillame-Bert, Hussein Joumaa, Stephane Ploix and James Crowley
In Proceedings of the 3rd International Conference on Information and Communication Technologies and Applications ICTA 2011
[pdf ]

2010

Journals/Conference Proceedings

▪ First-order Logic Learningin Artificial Neural Networks
Mathieu Guillame-Bert, Krysia Broda and Artur d'Avila Garcez
In Proceedings of 23rd International Joint Conference on Neural Networks IJCNN 2010
[pdf ]

Prior to 2010

Report

▪ Master's Thesis, Connectionist Artificial Neural Network
With the supervision of Krysia Broda
Imperial College of London, 2009
Distinguished MSc project [page]
[pdf ]
▪ Bs.C work, I-Terms unification
With the supervision of Nicolas Peltier
ENSIMAG, 2008
Download implementation (C++) [.zip]
[pdf ]
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Curriculum vitae

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Softwares & Datasets

Softwares

Since the 28 Nov. 2015, all the softwares except the Machine Learning Lab (along with their documentation) are hosted on the MGBFramework website. All softwares are publicly available and free to use.

  • TITARL (Temporal Interval Tree Association Rule Learner) is a Temporal Data Mining algorithm able to extract temporal rules from symbolic time series and time sequences (SSTS). The rules can be interpreted, used for predictions, or used for further analysis.
  • Event Viewer is a powerful visualizing tool for time series, time sequences and other symbol or scalar temporal datasets. Event Viewer has a lot of unique features which allow a powerful understanding of data. Event Viewer can be used to study static data and real time data flows. Event Viewer can interact seemingly with Honey.
  • Honey is a compact and high level flow-oriented programming language designed to facilitate the pre/post processing and the analysis of symbolic and numerical time series and sequences datasets. Honey can seemingly be applied on static dataset and real time data streams.
  • Machine Learning Lab is a small exploratory tool designed to test and experiment easily with several Machine Learning algorithms on several real work and sythetic datasets.

Public datasets

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Gallery

This sections shows some of the nice pictures that I have generated through my research work. You can click on the picture to get the full size versions.

You can see more of those pictures at the gallery page.

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Personal area

I enjoy skiing and salsa dancing.

Robot made of Legos
The core of a robot made of legos
when I was a kid.

Apart from that, I enjoy DIY as well as making video games (which are related). You can find some old video games and old DIY projects that I made in my youth on my old personal website at http://hokage.no.jutsu.free.fr/Achoum/.


A screen-shot of Build & Defend
In-game screen-shot of Build & Defend

I created a game called Build & Defend. This is a multi-player survival cooperative game in a randomly generated and destructible world. This game is an experiment of some game-play concepts that I wanted to try for a long time. Also, I rely of lot on player feed backs as a direction to help developing the game. You can try this game at http://buildanddefend.com/. You can see the Indie DB web-page of this game at http://www.indiedb.com/games/build-defend. You can also follow me on twitter at @Achoum_GameDev.


Definition of the "for" function in SMALL.
Definition of the "for" function in SMALL.

I also enjoy hacking a bit with computer. I have created this little blog where I talk about some of my hacks.


The logo of Nac-sitter.com
The logo of Nac-sitter.com

I am also co-creator of the website http://nac-sitter.com/. Nac-sitter.com helps people to find somebody to keep their pets during holidays. This web-site focus mainly on NACs (Nouveau animaux de compagnie - "New pets" in english) such as rats, hamsters, rabbits, fishs, pogonas, etc.


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