University of Washington
Gordon Watts is a professor in the Elementary Partciles Experiment group in the physics department at the University of Washington’s Seattle campus. He is a member of the ATLAS collaboration at the LHC at CERN and also the MATHUSLA collaboration. His primary physics interested there are searches for long-lived particles decaying in the calorimeter (this recent paper, for example), where heavy use of Machine Learning techniques are employed. He is also deputy executive director of the IRIS-HEP software institute, a software institute bring together over 20 institutions accross the USA to help build software and facilities to extract as much physics as possible from the HL-LHC experiments. His R&D work in IRIS_HEP concentrtes on the Analysis Systems focus area. Prior to ATLAS, he was a member of the DZERO collaboration at the Tevatron accelerator at Fermilab in Illinois, the CDF experiment at the Tevatron, and before that the AMY experiment on the Tristan accelerator at KEK in Japan. He has lead the flavor tagging groups in ATLAS and DZERO and worked on the top quark discovery, the single-top discovery, and higgs searches.
G. Watts has a broad range of interests and projects that are suitable for collaboration. Some of the projects that are directly connected with IRIS-HEP are:
- ServiceX - High speed delivery of data using a distributed cloud-native architecture. The data is translated from experiment’s custom formats into columnar data suitable for
awkwardarray consumption or Root Data Frame consumption. This work involves cloud native tools, like Kubernetes, as well as python, and C++.
- func_adl is an embeded SQL-like language in python modeled after C#’s LINQ. The complete tool line can translate from the python to C++ or python
awkwardarray manipulations to select the data. Used in the backend of ServiceX to select the data to be distributed.
- Other Analysis Projects - a lot of exploratory work in using python infrastrucure and the language to enable a physicsist to quickly analyze the PB of data expected in the HL-LHC era. There are many challenges and a lot of room for innovation in this space.