Laura Simonsen Leal
Vice President in AI/ML Strats at Goldman Sachs
Princeton, New Jersey, United States
Summary
Laura Simonsen Leal is a seasoned quantitative finance professional with a strong academic background, holding a Ph.D. in Operations Research and Financial Engineering from Princeton University. Her doctoral research focused on high-frequency optimal execution and microstructure, showcasing expertise in advanced quantitative methods. mathgenealogy+1
Her professional career is deeply rooted in algorithmic and quantitative strategies, particularly within Goldman Sachs, where she has progressively advanced through roles in Quantitative Investment Strategies (QIS) and currently serves as Vice President in AWM AI/ML Strats. This demonstrates a clear specialization in applying artificial intelligence and machine learning to financial markets. google
She possesses strong research capabilities, evidenced by her Master's thesis on hedge fund tail risk from FGV EPGE and participation in various academic seminars and workshops. Her research interests span high-frequency finance, machine learning, deep neural networks, optimization, and statistical and econometric methods for analyzing high-frequency trading data. google+2
Work
Education
Writing
Topics in High-Frequency Optimal Execution and Microstructure of Product Repricings
January 1, 2022Her PhD dissertation focusing on optimal execution problems, including the presence of a Brownian component in inventory and wealth processes, and a solution to stochastic optimization using an explainable neural network optimizer to adapt to risk preferences and learn from intraday seasonality.
An SDF Approach to Hedge Funds' Tail Risk: Evidence from Brazilian Funds
January 1, 2017Proposes a methodology to obtain a hedge fund tail risk measure, building on existing methods and using a minimum Hellinger risk-neutral measure to price observed hedge fund returns. It extracts a Tail risk hedge fund factor for Brazilian funds and relates it to market volatility.