This award supports research aimed at uncovering the fundamental laws of nature by studying the Higgs boson and searching for new particles at the Large Hadron Collider (LHC) at CERN. By looking for unique signals in the ATLAS experiment's detector, and improving the precision with which we measure current known particles, this work seeks to answer deep questions about the origins of mass, dark matter, and the imbalance between matter and antimatter in the universe. This award will support the training of undergraduate and graduate students as well as post-doctoral scholars. This award will further the computing tools that the field of high energy physics uses for physics analysis and high speed detection of interesting physics. The University of Washington team will analyze data from LHC Run-3 and prepare for the upcoming High-Luminosity LHC. The research focuses on precision measurements of Higgs boson properties, including the self-coupling, and on searches for new physics using novel techniques such as machine-learning-enhanced reconstruction of long-lived particles, and searches for mono-jet signatures. Contributions also include development of upgraded detector components and improvements to simulation and analysis software. A collaborative effort with Tel Aviv University will integrate machine learning into the real-time Level-1 trigger system, boosting sensitivity to unconventional signatures. Together, these activities enhance ATLAS's discovery potential while a