Mohammed Qazi
Tuskegee University, AL·Mathematics & Statistics
Artificial intelligence (AI) and data science now shape how scientists model complex systems, analyze data and make decisions. To make sure these methods are reliable, efficient and adaptable, researchers need a solid mathematical foundation.
- Fields
Mathematics & Statistics
Computer Science
- Topics
artificial intelligence
data science
machine learning
- Grants
- 1 known grant, through Aug 2030
What they work on
- Develop the mathematics of diffusion models for generative AI, which create new data by reversing a noising process, using stochastic modeling with applications such as wireless communications.
- Apply topological data analysis, which uses algebraic topology to describe the shape of data, to complex datasets in application areas including medical imaging.
- Build partial differential equation-based machine learning methods for anomaly detection, drawing on inverse problems, with applications such as cybersecurity.
More about the research
This collaborative training program, shared between Auburn and Tuskegee University, prepares students and postdocs to do research where mathematics, AI and data science meet.
Why it matters
Mathematically grounded AI methods could make tools for wireless communications, medical imaging and cybersecurity more reliable. Examples include generating realistic data, finding patterns in complex datasets and detecting anomalies such as network attacks. The program also trains a pipeline of researchers able to build and test such methods.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Jeremy Marzuola
University of North Carolina at Chapel Hill, NC·Mathematics & Statistics
Establish rigorous constructions of fundamental modes and resonances for model operators from condensed matter and topological physics, including light propagating in crystalline structures that contain localized defects.
Konstantina Trivisa
University of Maryland, MD·Mathematics & Statistics
Develop new quantum algorithms and strengthen existing ones using tools from algebra, representation theory and scientific computing, with implications for scientific computing performance.
Jonathan Tamir
University of Texas at Austin, TX·Engineering·Early-career
Develop deep generative physical models that pair known imaging physics with image patterns learned from data, so computational imaging systems can adapt to dynamically changing operating conditions.
The school
Tuskegee University: 4 labs on PI Pages, $30M a year on research, 6 research doctorates a year.
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