Machine Learning
Developing unsupervised, (semi)supervised, and reinforcement learning models and methods for incorporating domain knowledge and constraints, through projects such as MuMMI and Accelerating HEP Science.
Mathematics and Computer Science
Foundations of Machine Learning, Data Analysis, and Statistics
Exploring the principled and automated learning frontier
Argonne’s Mathematics and Computer Science division is researching fundamental aspects of computer vision, data analysis, machine learning, imaging, statistics, and algorithmic differentiation. Our research enables the extraction of insights and construction of scientifically rigorous predictive models from computational, experimental, and observational data. The results are used for design of experiments in scientific settings such as light source facilities, the nation’s energy and electricity grid, and leadership-class supercomputers.
Related Projects
Data Analysis
Formulating data science approaches and abstractions to facilitate automation of large-scale analysis, through projects such as SDAV, HEP Data Analytics, and Extreme-Scale Distribution-Based Data Analysis.
Statistics
Conducting fundamental research in statistical and stochastic methods, through projects such as MACSER.
Recent Activities
Recent Awards
-
2024 Dr. Sudharar Yalamanchili Award for poster Efficiently Composing and Controlling Hybrid Simulations of PDES and Machine Learning Models, at the 2024 ModSim workshop — Kevin Brown
-
R&D 100 finalist for 2024 — DeepHyper team