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Events During the Week of May 5th through May 12th, 2024
Sunday, May 5th, 2024: Final Exams; Abstract: *Note: actual end time may vary.* URL:; Add this event to your calendar
Monday, May 6th, 2024: Final Exams; Abstract: *Note: actual end time may vary.* URL:; Add this event to your calendar
Tuesday, May 7th, 2024: Final Exams; Abstract: *Note: actual end time may vary.* URL:; Add this event to your calendar; Personal Finance Workshop for Grad Students; Time: 12:00 pm - 1:00 pm; Place: 5280 CH; Speaker: Carol Hulland; Abstract: Several of you have asked me about arranging for someone to come speak about personal finance for graduate students. Questions about saving and budgets and generally managing funds are challenging. Carol Hulland is willing to present a workshop for you on starting early and continuing throughout one’s work life to take steps that build financial resilience.; Host: Sharon Kahn; Add this event to your calendar
Wednesday, May 8th, 2024: Final Exams; Abstract: *Note: actual end time may vary.* URL:; Add this event to your calendar
Thursday, May 9th, 2024: Final Exams; Abstract: *Note: actual end time may vary.* URL:; Add this event to your calendar
Friday, May 10th, 2024: Final Exams; Abstract: *Note: actual end time may vary.* URL:; Add this event to your calendar; Application of geometric deeping learning in charged-particle track reconstruction in the ATLAS ITk; Time: 9:00 am - 11:00 am; Place:; Speaker: Tuan Pham, Physics Graduate Student; Abstract: The reconstruction of charged-particle trajectories, ranking amongst the most computationally demanding tasks in particle collider experiments, such as the ATLAS experiment at CERN, plays an essential role in any High-Energy Physics program, as it determines the quality of particle identification, kinematic measurement, vertex finding, lepton reconstruction, jet flavor tagging, and other downstream tasks. The upcoming High Luminosity phase of the Large Hadron Collider (HL-LHC) represents a steep increase in the average number of proton-proton interactions and hence in the computing resources required for offline track reconstruction of the ATLAS Inner Tracker (ITk). As such, track pattern recognition algorithms based on Graph Neural Networks (GNNs) have been demonstrated as a promising approach to these challenges. We present a novel algorithm developed for track reconstruction in silicon detectors based on a number of deep learning techniques including GNN architectures. Using detector simulation of collision events associated with the production of a top quark pair on the latest version of ITk geometry under HL-LHC conditions, we demonstrate the performance of our algorithm, and compare to that of the tracking algorithm currently used in ATLAS on a range of important physics metrics, including reconstruction efficiency, and track parameter resolution. Finally, we discuss the algorithm's computational performance and optimisations that reduce computing costs, as well as our effort to integrate into the ATLAS analysis software for full-chain testing and production.; Host: Sau Lan Wu; Add this event to your calendar; Commencement: Doctoral & Medical Professional; Time: 5:30 pm - 12:00 am; Abstract: URL:; Add this event to your calendar
Saturday, May 11th, 2024: Commencement: Bachelor's, Master's, Law; Time: 12:00 pm - 1:00 pm; Add this event to your calendar
Sunday, May 12th, 2024: Graduate School Spring 2024: Doctoral degree deadline; Time: 11:55 pm - 12:55 am; Abstract: Degree candidates must complete all steps: CONTACT: 262-2433, gsacserv@grad.wisc.edu; Add this event to your calendar

Events During the Week of May 5th through May 12th, 2024