Software Engineer | Google Zurich

Google Zurich
Switzerland

e-mail:

Research Interests

My name is Mathieu Guillame-Bert, and I am working in Google Zurich, Switzerland.

I did my Postdoctoral at CMU (Carnegie Mellon University) in the Auton-Lab (part of Robotic Institute) under the supervision of Artur Dubrawski. I studied the use of rule extraction and decision forest-like techniques for symbolic and numerical time series. Typical applications include Automated and/or assisted Medical Diagnostics, Medical Forecasting, 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.

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 studied 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.

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.

Publications

2018

Article

▪ Exact Distributed Training: Random Forest with Billions of Examples

M. Guillame-Bert and O. Teytaud.

Arxiv

[paper

]

2017

Journals/Conference Proceedings

▪ Classification of Time Sequences using Graphs of Temporal Constraints

M. Guillame-Bert and A. Dubrawski.

Journal of Machine Learning Research 18 (2017)

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

Article

▪ Batched Lazy Decision Trees

ArXiv.org repository.

[arxiv

]

▪ Honey: A dataflow programming language for the processing, featurization and analysis of multivariate, asynchronous and non-uniformly sampled scalar symbolic time sequences

ArXiv.org repository.

[arxiv

]

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

]

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)

▪ 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)

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)

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)

[read

]

▪ 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

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]

[read

]

▪ Bs.C work, I-Terms unification

With the supervision of Nicolas Peltier

ENSIMAG, 2008
Download implementation (C++) [.zip]

[read

]

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Curriculum vitae

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Softwares & Datasets

Click on this link bellow to see the softwares and the data-sets that I created or processed.

Softwares

TITAR Learner
TITAR (Temporal Interval Tree Association Rule Learner) is Temporal Data Mining algorithm able to extract temporal patterns from symbolic time series and time sequences. These patterns can then be used to user interpretation or for automatic forecasting.
Event Viewer
A powerful Time Sequences visualier. Event Viewer has been developed during the study of several real world problems. For that reason Event Viewer has a lot of unique features which allow a powerful understanding of data.
Event Script
Event script is a small but high level flow-oriented declarative programming language designed to facilitate the pre/post processing and the analysis of symbolic and numerical time series and sequences datasets.
Machine Learning Lab.
A small exploratory tool designed to test and experiment easily with several Machine Learning algorithms on several real work and sythetic datasets.

Public datasets

The computer generated Database
A syntetic data-set create to test symbolic time sequence data-mining algorithms.
The Home Data Set
The "Home activities dataset" of Tim van Kasteren et al. converted to be imported in TITARL and Event Viewer.
Forex Dataset: Automated Trading Experiment
A three years record of the USD/EUR Forrex market converted to be imported in TITARL and Event Viewer.

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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.