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In this talk, I will discuss one of my research themes on inferring microscopic mechanisms that give rise to macroscopic behaviors of the systems. This theme is a series of joint works with different collaborators, but I will focus on my most recent work with Ethan Levien. Motivated by the applications of pattern recognition, image classification, network reconstruction, etc., we consider a modern Hopfield network (a spin glass model) whose configuration space is R^N. We randomly select M = exp(alpha*N) configurations that are independent and identically distributed according to the standard Gaussian distribution. We call these configurations “patterns”. Given a vector x^0 that is sufficiently close to a typical pattern, say xi^1, we analyze the convergence in probability of x^0 to xi^1. We will also discuss the local minima behavior of the corresponding energy landscape.