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SIAM Conference on Mathematics of Data Science (MDS24)

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From Machine Learning to Large Language Models - An Introduction

  • A full-day data science mini-course will be held prior to MDS24 on Sunday, October 20. Details will be posted when available.

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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.

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