In 2019, the University of Texas at Austin established a new institute on the foundations of data science with funding from the NSF TRIPODS program. The institute will coordinate foundational research in AI and data science across several university departments, launch a large-scale workshop and signature seminar series, and provide seed funding for a number of graduate and post-doctoral fellowships in artificial intelligence and machine learning.
TRIPODS unites researchers in computer science, electrical engineering, mathematics and statistics towards advancing our understanding of foundational issues in data science and machine learning. The institute will build a next-generation suite of mathematical tools for analyzing core algorithms, models, and applications.
Research activities center around three main research thrusts, all of which were developed to maximize impact and foster collaboration across campus. The institute serves as a center of gravity for theoretical aspects of data science on campus and will develop online curriculum for foundational coursework in machine learning.
Algorithmic Theory of Machine Learning
- Learning Neural Networks
- Hyperparameter Optimization
- Deep Confidence Intervals
Making Machine Learning Robust
- Robust Regression
- Deep Generative Models
- Adversarial Examples
- Subgraph Counting
- Graph Stats for Biological Networks
- Map Synchronization