CSE Colloquium: Sampling, learning, and testing Markov random fields

Sampling, learning, and testing probability distributions are fundamental tasks in science and engineering. In this talk, I will consider various algorithmic challenges that arise in the study of these problems in the context of Markov random fields. Markov random fields (or spin systems) are a family of high-dimensional distributions with vast applications in diverse research disciplines, including machine learning, statistical physics, computer vision, computational biology, social network analysis, game theory, and theoretical computer science. I will also explore connections between the computational hardness of these problems and phase transitions, which mark abrupt changes in properties of probability distributions due to a small change of a parameter.
Biography: Antonio Blanca is an assistant professor in the Department of Computer Science and Engineering at Penn State. Before that, he was a postdoctoral fellow in the Algorithms and Randomness Center at Georgia Tech and completed his Ph.D. studies at UC Berkeley in 2016. His research concerns the computational problems that arise in the study of probabilistic models. He is particularly interested in Markov chain Monte Carlo methods, structure learning, testing, the design and analysis of randomized algorithms, and the effects of phase transitions in computation.


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