My research is focused on designing methods, algorithms, and systems that assist the users in complex and necessary tasks for data intelligence and critical decision-making. These tasks combine core data management techniques (including data integration, fusion, cleaning, and preparation) with machine learning methods. The final goal is to let the users focus exclusively on the logic of their application, without being concerned by the underlying models or the execution details of data, feature, and ML model engineering. Ultimately, I design and code techniques and end-to-end analytical pipelines in Python and R on the following key aspects of Data Science and AI:
Partial lists can be found in: [DBLP] [ResearchGate] [GoogleScholar] [LinkedIn]
|
2020
|
|
2019
|
|
2018
|
|
2017
|
|
2016
|
|
2015
|
|
2014
|
|
2013
|
|
2011
|
|
2010
|
|
2009
|
|
2008
|
|
2007
|
|
2006
|
|
2005
|
|
2004
|
|
2003
|
|
2002
|
|
2001
|
|
≤ 2000
|
As an Associate Professor (with tenure) from 2000 to 2010 and a Full Professor from 2017 to 2018, I taught (lessons and practical work) from 192 to 300 hours per year at the Computer Science departments of several universities and engineering schools: Aix-Marseille University (PolyTech'Marseille), University of Montpellier 2, University of Toulon, University of Avignon, and University of Rennes 1, INSA Rennes, ENST Brest in France as well as at University of Cape Coast in Ghana and University of Yaoundé in Cameroon, Africa in the volunteering initiative of AHED (Academics for Higher Education & Development)). I’ve developed numerous lecture notes, slides, and other pedagogical materials for undergraduate and graduate students on various topics of Data Science:
Laure Berti-Équille is a Research Director at IRD, the French research institute for sustainable development. Before, she was a full professor In Computer Science at Aix-Marseille University in France, a senior scientist at Qatar Computing Research Institute, an associate professor at University of Rennes 1 in France, and a 2-years visiting researcher at AT&T Labs Research in New Jersey, as a recipient of the prestigious European Marie Curie Outgoing Fellowship. Her work is at the intersection of data management and machine learning with a focus on data cleaning and data preparation. She has more than 100 publications in major conferences and journals and three monographs. She co-organized the first workshops on information and data quality in information systems (IQIS’05) and quality in databases (QDB’09,’16) in conjunction with SIGMOD and VLDB respectively. She has also given several tutorials and keynote talks on data curation for machine learning (ICDE 2018, The WebConf/MePDAW 2019), truth discovery (CIKM 2015, ICDE2016), and data quality-aware analytics (KDD'09, ICDM'09). She is on the editorial boards of the VLDB Journal and the ACM Information and Data Quality Journal. She has received various grants from the French National Agency of Research (ANR), the French National Research Council (CNRS), and the European Union and she is a IEEE senior member.
Laboratoire d'Informatique et Systèmes (LIS)
Domaine Universitaire de Saint-Jérôme
Avenue Escadrille Normandie-Niemen
13397 Marseille Cedex 20, France
Laure Berti-Équille
laure DOT berti AT ird DOT fr
Here are some quotes, pieces of advice, and thoughts I have been collecting through the years in the academia (anonymity preserved):