Sebastien Roch

Sebastien Roch is a Vilas Distinguished Achievement Professor in the Department of Mathematics at the University of Wisconsin-Madison, with affiliate appointments in the Department of Statistics and the Theory of Computing Group in the Department of Computer Sciences. He is a member of the Probability Group and Applied Mathematics Group within the Mathematics Department and can often be found at the Wisconsin Institute for Discovery. Roch serves on the executive committee of the Institute for Foundations of Data Science (IFDS), a collaboration with several major universities funded through the NSF TRIPODS Phase II program.

Roch's research spans the intersection of applied probability, statistics, and theoretical computer science, with a strong emphasis on biological applications, particularly in mathematical phylogenetics and population genetics. His work focuses on developing rigorous mathematical frameworks for analyzing probabilistic and combinatorial structures arising from evolutionary biology. His research has produced significant contributions to understanding species tree estimation, sequence alignment, and phylogenetic methods under various evolutionary models.

His recent publications demonstrate a consistent focus on advancing computational methods in phylogenetics, with particular attention to statistical consistency, sample complexity, and polynomial-time algorithms. Roch has developed innovative approaches to species tree estimation under gene duplication and loss, addressed challenges in respondent-driven sampling, and contributed to understanding phase transitions in phylogenetic inference.

Scientific Awards and Recognition:

• Vilas Distinguished Achievement Professor (2024)
• Fellow of the Institute of Mathematical Statistics (2022)
• Van Vleck Research Professor Award (2023)
• Best Paper Award at RECOMB 2018
• Honorable Mention for Marcus W. Feldman Prize in Theoretical Population Biology

Roch has successfully mentored multiple PhD students to completion, including Max Hill (2023), Yu Sun (2023), and Shuqi Yu (2023). His research is currently supported by NSF grants DMS-2023239 (TRIPODS Phase II) and DMS-2308495, as well as institutional awards. He is actively involved in developing new courses, including "Linear Algebra and Optimization" (MATH 345) and "Graphs and Networks in Data Science" (MATH 444), which bridge traditional mathematics with modern data science applications.