معرفی
Pawel Polak is an **Assistant Professor** in the Department of Applied Mathematics and Statistics at Stony Brook University, and an Affiliated Faculty in the Institute for Advanced Computational Science. His research focuses on statistical learning, machine learning, and their applications in finance, medicine, and engineering. He has held academic positions at Columbia University (2015-2019) and Stevens Institute of Technology (2019-2020).
**Education**: PhD (summa cum laude) from the Swiss Finance Institute and University of Zurich (2014), Postgraduate Diploma in Economics (summa cum laude) from the Institute for Advanced Studies (2009), and dual degrees in Mathematics and Computer Science from the University of Warsaw and Cardinal Wyszyński University (2007).
**Research Interests**: Machine learning in quantitative finance, risk management, high-frequency trading strategies, image analysis for medical diagnostics (e.g., facial motion analysis), and non-Gaussian statistical models. He has developed the COMFORT framework for financial modeling and pioneered applications like real-time emotion detection via facial muscle tracking.
**Awards & Grants**: Top 10 US Quant & Finance Professors (2023), Swiss National Science Foundation Research Grant (2014-2015), and grants from the Department of Energy, Air Force, and Bloomberg. Active in organizing workshops like Alphathon and conferences on quantitative finance and machine learning.
**Teaching**: Courses in financial derivatives, quantitative risk management, machine learning in finance, and time series analysis at Stony Brook, Columbia, and Stevens Institute. Known for integrating advanced computational tools like Python and R into curricula.
