Conformance checking techniques asses the suitability of a process model in representing an underlying process, observed through a collection of real executions. An important problem for conformance checking is to align a log trace with a model, that is, to find the minimal changes necessary to correct a new observation to conform to a process model. The subject of this work is to study conformance checking for timed models, that is, process models that consider both the sequence of events in a process as well as the timestamps at which each event is recorded. We finally extend to the case of timed stochastic models.
Large Language Models (LLMs) have become impossible to ignore regardless of one's field or role. As an early adopter, I have explored their use in teaching, research, and development. This talk will explore practical applications including creating teaching materials (very helpful), brainstorming research ideas (good availability to skill ratio), writing code (choose your own superlative), and crafting formal documents like precise mathematical definitions (which is not their forte). Through interactive discussions and hands-on examples, we will explore the benefits and challenges of integrating LLMs into these academic functions. We hope this presentation will spark discussion in our community and provide you with new perspectives on leveraging LLMs in your work, whether you are just starting out or looking to deepen your existing practice.