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Energy correlators have recently come to the forefront of physics for studying high energy particle collisions. Due to their ability to naturally separate physics at varying scales and suppression of soft radiation effects, they have attracted a lot of attention for studying a wide range of physics phenomena ranging from imaging the non-perturbative transition, precision determination of the top-quark mass as well as studying medium modifications to jets traversing a quark-gluon plasma medium. Moreover, the N-point projected energy correlators exhibit simple scaling properties governed by the anomalous dimensions. While these N-point correlators are of interest, their evaluation time scales as M^N for a jet with M particles and this computational scaling is far more restrictive for the analytic continuation of these observables to non-integer N values. In a recent series of works, we introduced a new method based on recursion and a dynamical resolution parameter, enabling efficient estimation of arbitrary NN-point correlators over realistic LHC jet samples. In a more recent work, we further introduced a novel paramterization for energy correlators that not only improves the computational time drastically, bringing it down to M^2 log(M) for any N value, but also provides new avenues for visualizing energy correlators when more particles in the jet are resolved. I will discuss all these recent developments in my talk.