Johannes Wiesel

Johannes Wiesel is an Assistant Professor in the Department of Statistics at Columbia University's Faculty of Arts and Sciences, where he conducts research at the intersection of mathematical statistics and quantitative finance. He is affiliated with the Center for Financial and Business Analytics and the Foundations of Data Science initiative, reflecting his commitment to interdisciplinary data-driven research.

His research focuses on statistical optimal transport, a field that bridges probability, statistics, and optimization, with applications in finance and machine learning. He develops robust methodologies for financial modeling without relying on a priori assumptions, instead leveraging market information to construct time-consistent trading strategies and risk measures. His work establishes novel connections between optimal transport theory, robust statistics, and modern machine learning techniques, aiming to build a universal framework for robust decision-making in finance.

The trends in his research, though not detailed in specific publications here, suggest a strong emphasis on theoretical rigor combined with practical implementation in financial and data science contexts. His affiliations with analytics and data science centers indicate a focus on scalable and implementable statistical tools.

• PhD in Mathematical Sciences, University of Oxford (2020), advised by Jan Obloj

Johannes Wiesel has established key collaborations through his center affiliations at Columbia, particularly in financial analytics and data science. While no formal advisees or grants are listed in the provided text, his role as an assistant professor and active researcher suggests ongoing mentorship and potential grant involvement in statistical and financial research initiatives.

He is part of research teams associated with the Financial and Business Analytics and Foundations of Data Science centers at Columbia University, where he contributes to advancing robust statistical methodologies in real-world applications.