SIAM Conference on Mathematics of Data Science (MDS24)
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Deepen your understanding and application of machine learning technologies in this one-day intensive course held prior to MDS24.
About the Conference
This conference is sponsored by the SIAM Activity Group on Data Science.
At the upcoming SIAM Conference on Mathematics of Data Science (MDS24), a diverse mix of professionals from universities, industry, government, and research labs are set to join. The conference will showcase cutting-edge research that advances mathematical, statistical, and computational methods in the context of what we do with data and how to do it better. Presentations will range from foundational theory of data science to diverse applications. A particular focus this year is on the interaction of data science with the broader society in terms of privacy, interpretability, explainability, ethics, and policies. We hope you will consider participating in MDS24 to learn, share, and discuss the latest in data science!
Included Themes
Broad Areas, including:
- Mathematics of artificial intelligence (AI)
- Network science
- Optimization and control
- Randomized algorithms for matrices and data
- Compressive sensing
- Signal processing and information theory
- Statistical learning theory
- Approximation theory
- Data mining
- Machine learning (ML)
- Deep learning
- Topology and data
- Applications of data science (DS), ML, and AI, in all fields (e.g. health, sports, education, astrophysics, chemistry, earth science, materials science, biology, bioinformatics, neuroscience, economics, engineering, banking, finance, security, privacy, materials science, and social science)
Focus Topics, including:
- Generative AI (theory and applications)
- Privacy/interpretability/explainability/ethics/policy of AI, ML, and DS
- Dimensionality reduction and reduced-order models
- Data-driven dynamical systems
- Matrix and tensor decompositions
- Parallel/distributed/scalable optimization
- Inverse problems
- Reinforcement learning
- Graph neural networks
- Data visualization
Organizing Committee Co-Chairs
Eric Chi
Rice University, U.S.
David Gleich
Purdue University, U.S.
Rachel Ward
University of Texas at Austin, U.S.
Organizing Committee
Yuejie Chi
Carnegie Mellon University, U.S.
Karina Montilla Edmonds
SAP, U.S.
Margot Gerritsen
Stanford University and Women in Data Science Worldwide, U.S.
Anna Gilbert
Yale University, U.S.
Nicolas Gillis
University of Mons, Belgium
Jamie Haddock
Harvey Mudd College, U.S.
Gal Mishne
University of California, San Diego, U.S.
Emilie Purvine
Pacific Northwest National Laboratory, U.S.
Justin Romberg
Georgia Institute of Technology, U.S.
Fred Roosta
University of Queensland, Australia
Shashanka Ubaru
IBM Research and University of Texas at Austin, U.S.
Dootika Vats
Indian Institute of Technology Kanpur, India
Talitha Washington
Clark Atlanta University & Atlanta University Center, U.S.
Wotao Yin
Alibaba Group US/DAMO Academy, U.S.
Get Involved
Sponsor, exhibit, or check out past content in our video and presentation archive.
Funding Agency Support
SIAM and the Organizing Committee wish to extend their thanks and appreciation to the U.S. National Science Foundation and DOE Office of Advanced Scientific Computing Research for their support.
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