Dr. Pawel Polak

Pawel Polak is an Assistant Professor in the Department of Applied Mathematics and Statistics (AMS) at Stony Brook University. He also holds positions as an Affiliated Researcher at the Center of Excellence Wireless and Information Technology (CEWIT) and as an Affiliated Faculty at the Institute for Advanced Computational Science (IACS). His research specializes in statistical learning and machine learning methods with applications across engineering, medicine, and quantitative finance. His recent initiatives include developing multimodal Large Language Models for conversational AI, analyzing facial muscle dynamics for medical applications, designing Physics Informed Neural Networks for automated threat detection, creating advanced portfolio optimization methods in asset management, and developing signal processing techniques for high-frequency trading systems. His contributions have been highlighted at major machine learning and computer science conferences, including CVPR'24 and NeurIPS'23 workshops, and published in leading journals such as Quantitative Finance, the Journal of Econometrics, and the Journal of Banking and Finance. Rebellion Research, a global machine learning think tank, artificial intelligence financial advisor, and hedge fund, recognized him as one of the Top 10 Professors in Quantitative Finance in the US in 2023.

Website: https://sites.google.com/view/pawelpolak 
LinkedIn: https://www.linkedin.com/in/pawelpolaknyc/