Computer Science Synergies/Interests

This page is meant to capture some areas where high-energy experimentalists have built successful collaborations with computer scientists and data scientists. This list is by no means exhaustive or exclusive of areas where a successful collaboration has been or could be established.

• High throughput computing and distributed computing - workflow organization and scheduling; orchestration of compute across physical sites.
• Data management and compression techniques - how do we organize large amounts of data and move it to where it is needed.
• Python ecosystem - novel data analysis and science tools and techniques
• Machine learning / Artificial Intelligence - methods for data analysis, data processing and simulation
• High performance computing
• Interactive computing: Development of LLVM compiler toolchain and interoperability
• Probabilistic programming / Automatic differentiation methods. Developing differentiable pipelines with HEP tools; replacing tranditional tools and techniques with those better suited for optimization techniques
• Software for heterogeneous computing environments (eg, techniques and algorithms appropriate for efficiently using GPUs)