Witold Czubala, Ph.D.

Witold Czubala is a finance and AI leader with over 15 years of experience at leading global financial institutions, including UBS, Morgan Stanley, Bank of America, and Moody’s. His work focuses on applying artificial intelligence, machine learning, large language models, agentic systems, reinforcement learning, and quantitative methods to investment, product, and risk management challenges.

At UBS Global Wealth Management, Witold leads teams of data scientists and engineers building advanced AI and data-driven solutions for product innovation, portfolio optimization, and strategic decision-making. His work spans the development of machine learning and statistical models, as well as the application of emerging AI approaches such as LLM-powered workflows, autonomous analytical systems, and next-generation decision frameworks. He has extensive experience leading cross-functional teams that translate advanced research into practical tools and scalable business applications.

In previous roles at UBS Evidence Lab and UBS Investment Bank Research, Witold worked on modeling and forecasting using alternative data, combining econometric rigor with modern data science techniques to generate differentiated market insights. Earlier in his career, he held leadership and quantitative roles in Market Risk and Risk Analytics at Morgan Stanley, the Securitized Products Ratings group at Moody’s, and the Chief Investment Officer group at Bank of America, where he applied quantitative modeling, analytics, and risk frameworks across a wide range of financial applications.

Witold holds a Ph.D. in Economics from Johns Hopkins University, with a focus on time series econometrics applied to economic and financial problems. He also holds an M.S. in Mathematical Finance from the Courant Institute of Mathematical Sciences and an M.A. from the Warsaw School of Economics.