Tony Menzo will give a talk titled "Data-driven models of hadronization" at the New High Energy Theory Center (HET) Seminars hosted by the NHETC at Rutgers University, New Brunswick, New Jersey, United States.

Manuel Szewc will give a talk titled "Data-driven hadronization models" at ML4Jets2024, a conference taking place at LPHNE, Paris, France.

Jure Zupan will give a talk titled "Solving the inverse problem for hadronization" at BRDA 2024: Long life to the Standard Model workshop in Belica, Slovenia.

Manuel Szewc and Michael Wilkinson will present a poster titled "Modeling Hadronization with Machine Learning" at the NSF CSSI/CyberTraining/SCIPE PI Meeting in Charlotte, North Carolina, United States.

Manuel Szewc will give a talk titled "Modeling Hadronization with Machine Learning" at the weekly seminar hosted by the High Energy Group of the F1 Department of Theoretical Physics at the Jozef Stefan Institute in Ljubljana, Slovenia.

Manuel Szewc will give a talk titled "Machine Learning for Hadronization" at the "Fundamental Physics in the Era of Big Data and Machine Learning" workshop hosted by the Aspen Center for Physics in Aspen, Colorado, United States.

Michael Wilkinson will present a poster titled "Monte Carlo reweighting for efficient hadronization parameter uncertainty estimation" at the 12th Edition of the Large Hadron Collider Physics Conference hosted by Northeastern University in Boston, Massachussets, United States.

Ahmed Youssef will give a talk titled "Towards a data-driven model of hadronization using normalizing flows" at the 2024 edition of the APS Division of Particles & Fields (DPF) Meeting + 2024 Phenomenology Symposium hosted by the University of Pittsburgh and Carnegie Mellon University in Pittsburgh, Pennsylvania, United States.

Manuel Szewc will give a talk titled "Modeling Hadronization with Machine Learning" at the weekly seminar hosted by the Boston University Theoretical Particle Physics Group in Boston, Massachusets, United States.

Ahmed Youssef will present a poster titled "Towards data-driven models of hadronization", with an associated paper and video, at the Machine Learning and the Physical Science Workshop at the 37th conference on Neural Information Processing Systems New Orleans, Louisiana, United States.