Despite the increase in computational power over the last few decades, the modeling of complex reaction networks for realistic applications remains a daunting task. In response to that, Prof. Violi and Prof. Hero’s (U-M EECS) groups have recently joined forces and developed a data-driven sparse learning approach to model reduction in chemical reaction networks. Different from other methods presented in literature and studies, the proposed method does not require any understanding of the underlying process. Instead, it learns the model from data generated from the chemical process. And, the method requires tuning of only one parameter, the tolerance on the distance. Because of the data-driven nature of this approach, it can be implemented on any reaction mechanism. This work has been recently published in the Proceedings of NIPS 2017 Workshop on Advances in Modeling and Learning Interactions from Complex Data, and is receiving a lot of attention. https://arxiv.org/abs/1712.04493.