| Add initial integration of Clad with Enzyme |
| Add numerical differentiation support in clad |
| Add support for functor objects in clad |
| Add support for in-browser interactive averaging of physics results |
| Add support to resolve symbols from a static library to the Cling C++ interpreter |
| Allow redefinition of CUDA functions in Cling |
| Automatic conversion of data stored in TTree form to RNTuple |
| CernVM-FS preload capability |
| Deep autoencoders for ATLAS data compression |
| Developing C++ modules support in CMSSW and Boost |
| Implementation of physical shape function |
| Implementing an application for visualizing the LHCb DAQ network |
| Improve Cling’s Development Lifecycle |
| Improve the job submission and handling in the Ganga User interface |
| Improving Cling Reflection for Scripting Languages |
| MCnet/LHAPDF - Accuracy and parallel computation in parton density calculation |
| MCnet/Rivet - Modern plotting machinery for the LHC’s MC event analysis tool |
| MCnet/YODA - Add parallel weight streams to a statistical analysis toolkit |
| New protocols for exascale data management with Rucio |
| PODIO serialization back-end for ROOT RNTuple |
| PRMON - Develop Logging and Unit Test Infrastructure For PRMON |
| Partitioning GPUs for graphical and computing applications under Linux KVM |
| Phoenix, interactive data visualization - Development of an experiment independent javascript event display framework and data format |
| Portability for the Patatrack Pixel Track Reconstruction with Alpaka |
| ROOT Storage of Deep Learning models in TMVA |
| RooFit Developmnt - Intuitive Python bindings for RooFit |
| RooUnfold - Efficient deconvolution using state of the art algorithms |
| Rucio and CS3API to enable data management for the ScienceMesh cloud |
| Runtime plugin ecosystem support for OCIS |
| Scientific Notebook support in ownCloud Infinite Scale |
| Single-precision floating-point support for GPU acceleration in VecGeom |
| TMVA Deep Learning Developments - 3D Convolutions for GPU |
| TMVA Deep Learning Developments - Inference Code Generation for Batch Normalization |
| TMVA Deep Learning Developments - Inference Code Generation for Recurrent Neural Networks |
| Upgrading the Ganga graphical user interface |
| Utilize second order derivatives from Clad in ROOT |