Eugene Neduv is an expert in graph analytics and data science with background in finance, economics and risk management. During his 15 years in the industry and academia his interests included systemic risk, geopolitical scenario modelling, portfolio optimization, volatility and correlation trading, natural language processing, recommendations systems and unsupervised learning.
Eugene Neduv graduated from Columbia University in 2002 with a PhD degree in Mathematics. After transitioning into financial technology and risk management he started his own consulting practice advising financial institutions in New York and Sao Paulo. His current projects are focused on AI applications to news analysis, Bayesian learning for geopolitical risk, shock propagation in interconnected economies and anomaly detection in complex systems.
Eugene Neduv is currently a Research Scientist in the Department of Industrial Engineering and Operations Research.