Arya Mazumdar, Associate Professor, UC San Diego Abstract: The problems of heterogeneity pose major challenges in extracting meaningful information from data as well as in the subsequent decision making or prediction tasks. Heterogeneity brings forward some very fundamental theoretical questions of machine learning. For unsupervised learning, a standard technique is the use of mixture models for […]

David Pan, Professor, The University of Texas at Austin Abstract: The recent artificial intelligence (AI) boom has been primarily driven by three confluence forces: algorithms, big-data, and computing power enabled by modern integrated circuits (ICs), including specialized AI accelerators. This talk will present a closed-loop perspective for synergistic AI and agile IC design with two […]

Shirin Saeedi Bidokhti, Assistant Professor, University of Pennsylvania Abstract: The Internet of Things (IoT) and social networks have provided unprecedented information platforms. The information is often governed by processes that evolve over time and/or space (e.g., on an underlying graph) and they may not be stationary or stable. We seek to devise efficient strategies to collect […]

Yian Ma, Assistant Professor, UC San Diego Abstract: Professor Ma will introduce some recent progress towards understanding the scalability of Markov chain Monte Carlo (MCMC) methods and their comparative advantage with respect to variational inference. Further, he will discuss an optimization perspective on the infinite dimensional probability space, where MCMC leverages stochastic sample paths while variational […]

Melvin Leok, Department of Mathematics, UC San Diego Abstract: Geometric mechanics describes Lagrangian and Hamiltonian mechanics geometrically, and information geometry formulates statistical estimation, inference, and machine learning in terms of geometry. A divergence function is an asymmetric distance between two probability densities that induces differential geometric structures and yields efficient machine learning algorithms that minimize […]

Hamed Hassani, Assistant Professor, University of Pennsylvania Abstract: In this talk, we will focus on the emerging field of (adversarially) robust machine learning. The talk will be self-contained and no particular background on robust learning will be needed. Recent progress in this field has been accelerated by the observation that despite unprecedented performance on clean […]