Bin Yu is currently working on statistical machine learning theory, methodologies, and algorithms for solving high-dimensional data problems. The current research topics of her group cover sparse modeling (e.g. Lasso), structured sparsity (e.g. hierarchical and group and graph path), and analysis and methods for spectral clustering for undirected and directed graphs. Her data problems come from diverse interdisciplinary areas, including remote sensing, neuroscience, document summarization, and social networks. Her past research areas have included empirical processes, Markov Chain Monte Carlo, signal processing, the minimum description length principle (MDL), and information theory.
- Summer Cluster: Interpretable Machine Learning, Summer 2022. Visiting Scientist and Workshop Organizer.
- Computational Complexity of Statistical Inference, Fall 2021. Visiting Scientist.
- Geometric Methods in Optimization and Sampling, Fall 2021. Visiting Scientist.
- Foundations of Deep Learning, Summer 2019. Visiting Scientist.
- Foundations of Data Science, Fall 2018. Visiting Scientist.
- The Brain and Computation, Spring 2018. Visiting Scientist.
- Information Theory, Spring 2015. Visiting Scientist.
- Theoretical Foundations of Big Data Analysis, Fall 2013. Visiting Scientist.