Danilo Bassi

Danilo Bassi, PhD, is an engineer, scientist, innovator, and an entrepreneur. His recent emphasis converges on systems analysis for financial markets. He has been a Professor in the U.S. and in Chile, and has also launched several ventures based on products derived from his scientific research. His developmental contributions are applied by numerous researchers in the fields of Mathematical finance, Robotics and Automation. Dr. Bassi’s autonomous decision models are based on Computational intelligence and feature a general parameterized mission control approach which to date have benefited the world of finance and automation.
Biography
In 2008 Dr. Bassi moved with his family to the U.S. He now resides and works in New York City. He is currently the Principal Quantitative analyst for a Hedge fund group where he develops and evaluates financial robotic technology (e.g. Machine learning, neural networks) for Algorithmic trading, portfolio management and risk control.
Education
Dr. Bassi received an engineering education at the University of Chile (1986). He later moved to the U.S. where he obtained his PhD at the University of Southern California (1990) under the guidance of renowned Professor George A. Bekey. His PhD thesis, “Connectionist Dynamic Control of Robotic Manipulators,” is one of the first studies to consider dynamic control with neural modeling in a practical way for solving the feedback control problem.
Academic Work
His first Professorship was with the Department of Industrial Engineering, University of Chile (1991), where he started to apply advanced modeling techniques into business and financial systems. In 1995 he moved to the University of Santiago, Chile while continuing to refine his orientation on financial modeling for the private sector. This resulted in Automation and Dynamic Systems: Robotic and Control, and Predictive Models. In 2008 he became Visiting Professor and Researcher at Villanova University; during this period he developed novel Mission Control solutions and pursued Artificial intelligence driven risk modeling while applying his innovative approach to examine investor behavior based on Game theory. He also continued researching on predictive control models.
Entrepreneurship
Since the beginning of his academic career, Dr. Bassi has been interested in practical applications of his research. In 1993 he started to work on risk modeling using non-linear paradigms. The success of this project led to the foundation of his company, RedNova, which focused on risk and behavior modeling. In 2000 he started the venture Enacsys to pursue further applications of advanced modeling (behavioral control and automatic trading). In 2003 he went on sabbatical to Codelco, the world’s largest copper producer, to advance the control systems for mining automation; this endeavor motivated the subsequent launch of a company Robotic Mining Consortium (RMC S.A.), which concentrated on the development of robotic mining technology. In 2008 he founded AeroRobot Chile to develop autonomous solutions for unmanned aerial vehicles (UAVs). His research and experience culminated with the formation of SymLogical, which he founded in 2011, for solving the problem of mission control at the recurrent logical level, developing for that a novel and efficient method.
Business and Financial Systems
Dr. Bassi introduced credit risk behavior modeling to the financial industry in 1993. Constructed from real data, this allowed for optimal decision making. The approach is known as knowledge discovery from data (KDD), and the models are based on neural networks and induction systems (like decision trees and rules). He then applied other techniques such as dynamic control, Time series analysis and prediction and undertook more advanced problems: probabilistic model for estimation, anomaly detection, time series analyses with proprietary learning architecture and recursive memory models, clustering and unsupervised learning, predictive systems and automatic decision (trading). At present, he is involved in studies of financial industry specific mathematical models favored by banks and institutional investors for portfolio assurance purposes (Basel Accords), and allowing for implementing broad algorithmic decision strategies for sustainable performance in volatile financial markets.

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