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Optimization and Deep learning for Data Analysis.

Our research interest includes modeling, optimization techniques and theories, and deep learning architectures for high dimensional data analysis. Current ongoing projects are

  • Deep learning architectures inspired by optimization method: An integration of variational method and deep neural network (DNN) approach for data analysis;
  • Variational DNN for medical image processing;
  • Develop randomized incremental primal dual algorithms for decentralized consensus problems to reduce communication cost for fully connected networks;
  • Develop accelerated level bundle methods for functional constrained convex optimization.
  • Develop sparsity-induced stochastic ADMM algorithm for non-convex stochastic optimization to achieve better iteration complexity and sample complexity (joint with Dr. Liu, ISE, UF)

Leading figure shows Prof. Yunmei Chen and her students.