Mathematics of Data

The "mathematics of data" encompasses a diverse blend of mathematical techniques that are crucial not just for handling vast datasets, but also for extracting meaningful insights from them. Rooted in core areas such as probability, statistical theory, linear algebra, optimization, and combinatorics, this discipline provides the tools needed to navigate the complexities of data. As the fields of artificial intelligence and data analysis continue to expand, they not only lean heavily on these foundational mathematical concepts but also offer fresh perspectives and challenges back to the world of mathematics. This dynamic interplay underscores the mutually enriching relationship between data and math in our evolving digital landscape.

Department Members in This Field

Faculty

• Jörn Dunkel
• Anette Hosoi Fluid Dynamics, Numerical Analysis
• Jonathan Kelner Theoretical Computer Science
• Pablo Parrilo Optimization, Control Theory, Computational Algebraic Geometry, Applied Mathematics
• Philippe Rigollet Statistics, Machine Learning

Instructors & Postdocs

• Chenyi Fei Theoretical Biophysics, Mathematical Modeling
• Anya Katsevich High dimensional statistics, Bayesian inference
• Dan Mikulincer Probability, High-Dimensional Geometry, Functional Inequalities
• Yihui Quek Quantum Computing, Complexity Theory, Quantum Noise and Error Correction

Graduate Students*

• Adam Block Learning Theory, Statistics
• Andrey Bryutkin Mathematics of Data, Statistics, Physical Applied Mathematics
• Max Daniels High-dimensional statistics, optimization, sampling algorithms, machine learning
• Seokmin Ha
• Daniel Lazarev
• Lichen Zhang Theoretical computer science, machine learning and data science

*Only a partial list of graduate students