The "data factory"

Bruno Chaves

Bruno Chaves
Coordinator of the project

A double background in computer sciences and economics / econometrics

  • CNRS Engineer in information technology, data engineer and project manager since more than 10 years.
    (Nanterre University, Ecole Normale Supérieure and Dauphine University)
  • Previously : Research and teaching assistant in econometrics / statistics (Sorbonne University)

Coordinator for several international research projects :

  • SIOE - Society for Institutional & Organizational Economics (coordinator since 2007)
  • IOEA - Institutional & Organizational Economics Academy (co-coordinator since 2002)
  • DIME - Dynamics of Institutions and Markets in Europe (IT manager 2005-2011)

Methodologies and tools

  • Project management, object and test driven development, continuous integration methodologies.
  • Data engineer (Python, SQL and NoSQL databases), IT systems administration, full stack web development (JavaScript, CSS, PHP, HTML5).

Yifei Fan

Yifei Fan
Data Scientist

Training and Experience

  • Engineering degree from the Paris Institute of Technology for Life, Food and Environmental Sciences(AgroParisTech) with a double Master’s degree from the Paris-Dauphine University in Data Science.
  • Internship in a consulting firm, from March till September 2018, working on a project consisting of establishing Machine Learning models for predicting the probability of default of French companies according to their annual reports.

Methodologies

  • Data Analysis: classification, clustering
  • Neural Computing and Forecasting: neural networks, deep learning
  • Semantic web

Programming Languages and databases

  • Programming languages : Python, R(Tensorflow, shiny)
  • Databases : SQL-> PostgreSQL

Svitlana Galeshchuk

Svitlana Galeshchuk
Post-doctoral fellow in data science

Training

PhD in Economics (Ukraine, 2015): Accounting and Analysis of Foreign-Exchange Operations

Publications / Award

Awards

  • 2017 : Visiting Researcher at the National Bank of Poland (May 15-26/17)
  • 2017 : Invited Professorship at Université Grenoble-Alpes, France (01/17-07/17)
  • 2015 : Fulbright Faculty Development Research Scholarship, IIE, USA
  • 2013 : Eiffel excellence scholarship, French Ministry of Foreign Affairs, France

Research Fields

Financial Markets, Monetary Policy, Economic Forecasting, Data Analysis, Neural Computing

Methodologies and tools

  • Data Analysis: high-dimensional problems, clustering, genetic algorithms
  • Neural Computing and Forecasting: neural networks, deep learning
  • Software: Python, R, Tensorflow

Mohammad M. Habibpour

Mohammad M. Habibpour
Post-doctoral fellow in economics

Training

Ph.D. in Economics from University of Brussels (VUB), Belgium
Doctoral fellows program, Center for Social Impact Strategy, University of Pennsylvania, USA
Master of Science in Economics from University of Siegen, Germany

Publications / Award

  • How giving affects giving: a long-term analysis of donations. Applied Economics (2017, 66, 35-42 ). Coauthor with Peiffer, M., Jegers, M. & Pepermans R.
  • Resource rents distribution, income inequality and poverty in Iran. Energy Economics (2017, 50:21, 2402-2413). Coauthor with Farzanegan M.
  • The importance of sector stereotypical images in relation to job pursuit intentions. Nonprofit Manage-ment and Leadership (2018, DOI: 10.1002/nml.21304).
  • Coauthor with Peiffer, M., Pepermans R., & Jegers, M.
  • Effects of Releasing Subsidies on the Wage Rates and the Gender Wage Inequality. In: Jon Strand (Editor): The Politics and Political Economy of Energy Subsidies. Cambridge, MA: MIT Press. Forth-coming, 2016. Coauthor with Seiban N.
  • Iran economy. In: The Europa Regional Surveys of the World. The Middle East and North Africa. 2016 Routledge. Taylor & Francis Group. Coauthor with Farzanegan M.

Research Fields

Economic Developments (Resource rents, Inequality, Poverty)
Economics of NPOs (non-profit organizations), (Donations, Social networks, volunteering)
Econometrics

Methodologies and tools

  • Timeseries analysis, Bayesian estimations, structural equation modeling, spatial analysis.

Abir Jaza

Abir Jaza
Data Scientist

Training and Experience

  • Engineering degree from the National School of Computer Studies ENSI of Tunisia in Computer Science with the passion for Data Analysis.
  • DISP Laboratory Internship, from February till July 2018, working on a task in a European Project, consisting of implementing a solution for managing and treating enterprises’s documents and data.

Methodologies

  • Textual analysis
  • Web Scrapping
  • Web Development
  • Sentiment analysis
  • Modeling

Programming Languages and databases

  • Programming languages : C, C++, JAVA, JavaScript, PHP, Pascal, Python, R
  • Databases : SQL-> MySQL, NoSQL-> MongoBD, Hadoop

Ju Qiu

Ju Qiu
Post-doctoral fellow in economics

Training

PhD in Economics,
European Diploma in Advanced Quantitative Economics,
Toulouse School of Economics

Working papers

  • Social Media and Firm Value: An Exploratory Study of the Dieselgate. Dec. 2018. Co-authored with Julien Jourdan and Svitlana Galeshchuk.
  • Risk Heterogeneity, Kinship Networks and Risk Sharing: Evidence from Thai Villages. Dec. 2018.submitted
  • Quality Adverse Selection in Online Marketplaces: Evidence from Etsy. Jan 2019. Co-authored with Bruno Chaves. submitted
  • Nonparametric Decomposition of the Distributional Change in Body Mass Index in China. July 2017. Co-authored with Pierre Dubois
  • Migration, Consumption Smoothing and Household Income: Evidence from Thai Villages. Nov 2016. Co-authored with Yaping Wu 
  • Estimate Income Gains from Internal Migration in China using Propensity Score Matching. Oct 2016. Co-authored with L. Alan Winters.

Awards

  • 2008-2011: Doctoral Research Grant, French Ministry of Education, France.
  • 2007-2008: Eiffel Scholarship, French Ministry of Education, France.

Research Fields

Applied Micro-econometrics, Social and Economic Networks

Methodologies and tools

  • Panel Data Analysis, Network Analysis, Program Evaluation, Non-parametric Decomposition, Discrete Choice, etc.
  • Stata, R, Gephi, Matlab, Python