Tim Hoheisel

Tim Hoheisel is an Associate Professor at McGill University's Department of Mathematics and Statistics. His research focuses on nonsmooth optimization, variational analysis, and their applications in continuous optimization and machine learning. He is the director of the CRM Applied Math Laboratory and co-chair of the CRM thematic program on Mathematical Foundations of Data Science (2025). He serves as an Associate Editor for *Mathematics of Operations Research* and *Open Journal of Mathematical Optimization*. Hoheisel has organized conferences and seminars, including the CRM Applied Math Seminar in Montreal. He has advised numerous PhD and MSc students and postdocs, collaborating on topics like regularization methods, inverse problems, and convex analysis.

• Education: PhD in Mathematics from University of Würzburg (2009).
• Committee Roles: Advisor for Honours Applied Mathematics (B.Sc.) Program, elected member of the Chair's Advisory Committee.

His research integrates theoretical analysis with applications, bridging applied and pure mathematics. Recent work includes contributions to the Maximum Entropy on the Mean method for inverse problems, Lipschitz stability in LASSO, and regularization techniques. Hoheisel is a member of SIAM and the MTL MLOpt group, focusing on optimization-machine learning interfaces.

Recent presentations include a semi-plenary talk at the 2025 International Conference on Continuous Optimization and lectures on regularized least-squares at the University of Trier. His publications span optimization theory, convex analysis, and applications in image processing and quantum Monte Carlo data analysis.

• Awards: None explicitly mentioned.
• Grants/Funding: Not detailed; inferred from editorial and leadership roles.

He leads the CRM Applied Math Lab and collaborates with institutions like Concordia University and the University of Ottawa through INTER-MATH-AI. His work emphasizes interdisciplinary approaches, particularly in data science and AI integration with mathematical optimization.