Dr. Ames specializes in nonlinear optimization with applications primarily in machine learning and signal processing. In particular, much of Dr. Ames’ research has focused on the development of tractable numerical optimization methods for learning tasks such as clustering and classification, as well as theoretical analysis establishing sufficient conditions guaranteeing when the desired underlying structure can be efficiently recovered from data.
Learn more about his research on Dr. Ames’ personal website, and see his publications on Google Scholar and MathSciNet.
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Dr. Chen’s research focuses on methodology for statistical analysis of big data, with a focus on Bayesian nonparametric models, regression models for mixed response data, survival analysis, and hazards models.
Find Dr. Chen’s publications on MathSciNet.
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The research interests of Dr. Dang Nguyen lie in the fields of applied probability, stochastic processes, dynamical systems, stochastic control, queuing theory and mathematical biology. His primary research focuses on four related areas: persistence and extinction of ecological and epidemiological models, qualitative properties of switching diffusions; long-term optimal strategies for harvesting renewable resources and asymptotic properties of multi-scale stochastic systems.
Learn more about his research on Dr. Nguyen’s personal website, and see his publications on Google Scholar and MathSciNet.
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Dr. Sun’s research focuses on global optimization, multiobjective programming, nonlinear regressions, optimal control, minimax and game, modeling and simulation, with applications in many areas of science and engineering including smart and autonomous home energy managements, water resources management, and magnetic materials research.
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