Samuel Edet is a doctoral student in Economics, Networks and Business Analytics at IMT School for Advanced Studies, Italy. He holds a masters degree (distinction) in Mathematical Sciences from the University of Cape Town, South Africa (through the African Institute for Mathematical Sciences Post graduate scholarship), where he worked on Recurrent Neural Networks for financial market forecast. This project made the final nomination for the 2017 Thomson Reuters Excellence Award (student research in data science). Also, he has a bachelors degree (first class) in Mathematics from the University of Ibadan, Nigeria.
Professionally, he worked with Siemens as a business developer. He has a diploma in finance and investment from London Institute of Management and a business management certification from European School for Management and Technology (ESMT) business school.
His research interest spans economics of innovation, industrial organization, business strategy and machine learning in finance.
1. Edet, Samuel, Recurrent Neural Networks in Forecasting S&P 500 Index (July 12, 2017). Available at SSRN: https://ssrn.com/abstract=3001046 or http://dx.doi.org/10.2139/ssrn.3001046