Parisa Karimi

Parisa Karimi is a Quantitative Researcher at Capital Fund Management (CFM) in New York, where she develops and implements systematic trading strategies using advanced quantitative methods. Her current responsibilities include designing predictive models and leveraging statistical inference, Bayesian estimation, and machine learning techniques to uncover inefficiencies and generate alpha across global financial markets. At CFM, her work primarily focuses on liquid asset classes, including equities, futures, and derivatives, where robust data-driven insights are crucial for active portfolio management.

Before joining CFM, Parisa gained substantial experience as a Graduate Research Assistant at the University of Illinois Urbana-Champaign, where she focused on statistical signal processing, optimization, and computational methods, including applications in high-dimensional data analysis. Her research contributions have advanced knowledge in statistical inference and machine learning, particularly within the context of complex and high-frequency data. She also completed an impactful research stint at the Simons Foundation, developing biologically plausible control learning rules.

Parisa holds a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign, a Master's degree in Electrical Engineering from Sharif University of Technology, and a Bachelor's degree in Electrical and Computer Engineering from Iran University of Science and Technology. Her expertise in quantitative research, machine learning, and statistical modeling is supported by a strong academic record and a track record of innovative research, positioning her at the forefront of data-driven investment strategies for liquid markets.