Instead of focusing on specific physics processes to choose what is “interesting”, this project will investigate a more generic approach – what are the bottlenecks in our trigger algorithms, what prevents us from recording the events we want and how can we do more interesting physics with the same or even fewer resources. The first objective of the student working on this project will be to simplify, streamline and optimize the process of testing and benchmarking the LHCb real-time analysis software which decides which events are sufficiently interesting to be studied further, and which are discarded, by creating tools that will analyze the performance of each piece of the decision software, the resources used for each individual decision, and the commonalities amongst decisions. The student will also study how to reduce the resources required by using ML algorithms. While this project will focus on LHCb, it will also keep in mind the ATLAS use case during a stay at CERN in order to collaborate with ATLAS colleagues. Compared to ATLAS, LHCb generates an order of magnitude less data per collision, but at an order of magnitude larger rate. The requirement that the toolkit must work optimally for both experiments implies that it must be sufficiently generic and adaptable. As a result, it will also have applications beyond these two experiments. Furthermore, two collaborations (one academic, one industrial) with Dortmund will allow the PhD student employed in this project to receive expert training in the design of Lepton Flavor Violating analyzes and collaborate with the student there. This will happen in tandem with an industrial internship at Point 8, an industrial data science company, where optimization and benchmarking techniques will be used for monitoring and decision-making in applications of data-driven use cases in the German industry. The physics analysis focus of this thesis project will be the search for lepton flavor violation in the τ → μγ decay in LHCb, which currently does not have a dedicated trigger chain at LHCb. The high data rate of the upgraded LHCb experiment, in combination with the optimization of the trigger algorithms, will allow us to collect this kind of events and study a process that was previously thought to require dedicated experiments.