Dr. Peter Martey Addo

What I do

I can help you generate new ideas, innovation and improve growth.

Foresight & Strategy

Provide understanding of the potential of unleashing data & emerging technologies to improve growth.

Lead Interdisciplinary Teams

Experimentations, Research & Innovation.

Partnerships

Establishing the right partners to address key challenges.

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Peter Martey Addo

Senior Data Scientist

Agence Française de Développement (AFD)

Biography

I’m an experienced data scientist, and serve as the head of DataLab at the French Development Agency (AFD), Paris, France. I hold a double PhD in Applied Mathematics and Economics. I worked at the French National Center for Scientific research (CNRS) as a researcher, and at the French National railway company (SNCF) as the Lead Data Scientist.

My research interests include machine learning, systemic risks, information retrieval, time series analysis, business cycles, and nonlinear system dynamics.

I am keen on ways we can leverage data and emerging technologies to improve growth, and people’s lives.

Recent Posts

Découvrez en vidéo le métier de Data Scientist à l’ AFD.

L’intelligence artificielle (IA) en Afrique - Entretien avec Next Einstein Forum

« Je vois la nécessité d’élargir notre connaissance et notre compréhension de la pertinence de l’intelligence artificielle pour l'emploi et la croissance en Afrique,» selon @PMarteyAddo. De quelle manière? Lisez son entretien sur le blog du #NEF2020!

Recent & Upcoming Talks

Invited Speaker - 'Beyond Slogans - Building inclusive AI Solutions' at NEF2020 (**Postponed**)

Plenary Session - ‘Beyond Slogans - Building inclusive AI Solutions’

Projects Opportunities

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Open research collaboration

Outline of some of the research interest at the AFD Datalab that might interest you.

Opportunities - Research Assistant, Internships, Postdocs, CIFRE PhD

Research opportunities at AFD on data & emerging technologies (AI, Blockchain, …)

Potential Partnership with AFD

Seeking for an institutional partner to support initiatives that improve people’s life and leverage on emerging technology? Get in touch.

Recent Publications

Quickly discover relevant content by filtering publications.

Credit Risk Analysis Using Machine and Deep Learning Models

Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency.

Insights to the European debt crisis using recurrence quantification and network analysis

The turmoil in the sovereign debt markets in Europe has raised concerns on the usefulness of sovereign credit default swaps and government bond yields in periods of distress. In addressing this issue, we introduce a novel nonlinear approach for the analysis of non-stationary multivariate data based on complex networks and recurrence analysis. We show the relevance of the approach in studying joint risk connections, extracting hidden spatial information, time dependence, detection of regime changes and providing early warning indicators. The feasibility and relevance of the approach in studying systemic risk is discussed. Finally, we share more light on possible extensions and applications of the approach to systemic risk.

Coupling direction of the European Banking and Insurance sectors using inter-system recurrence networks

Modern financial systems exhibit a high degree of interdependence making it difficult in predicting. This has raise concerns on the correct identification of coupling direction in financial sectors of the economy. This study explores a “two-way” risk connection between the European banking and insurance sector based on geometrical closeness of observations. Specifically, the study looks at the inter-system recurrence networks in tracing dynamical transitions and detecting coupling direction between these sectors. The overall results shows that the banking sector is central in risk transmission compared to the insurance sector. A comprehensive discussion of the feasibility and relevance of the approach in studying systemic risk is provided.

Multivariate Self–Exciting Threshold Autoregressive Models with eXogenous Input

This study defines a multivariate Self-Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The conditions for stationarity of the nonlinear MSETARX models is provided. In particular, the efficiency of an adaptive parameter estimation algorithm and LSE (least squares estimate) algorithm for this class of models is then provided via simulations.

Nonlinear Dynamics and Wavelets for Business Cycle Analysis

We provide a signal modality analysis to characterize and detect nonlinearity schemes in the US Industrial Production Index time series. A complex Morlet wavelet is employed to detect and characterize the US business cycle. A comprehensive analysis of the feasibility of this approach is provided. Our results coincide with the business cycles peaks and troughs dates published by the National Bureau of Economic Research (NBER).

Contact

  • +33638308228
  • 15 rue Traversiere, Paris, Ile de France 75012
  • Enter Building and take the stairs to Office T02 220 on Floor 2