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The IMA Welcomes Its Class of 2011–2012 Postdoctoral Fellows

The IMA would like to welcome the following eight postdoctoral fellows and two industrial postdocs who have joined the IMA for the 2011–2012 thematic program on the "Mathematics of Information." These postdocs were chosen from a pool of more than 200 applicants from around the world. During their stay, the fellows will have opportunities for collaboration, engagement, research, and exploration. The IMA provides a stimulating, scientific atmosphere built around the Annual Thematic Program, which allows for the fellows to become truly immersed in one broad field of interdisciplinary mathematics. The IMA also dedicates a great deal of resources to mentoring, supporting, and nurturing its postdoctoral fellows.

It is with great pleasure that the institute welcomes the new class of 2011–2012.

Brendan P. W. Ames, Department of Combinatorics and Optimization, University of Waterloo, is interested in convex analysis, matrix analysis, and semidefinite programming and their applications to nonlinear optimization. In particular, his current focus is on matrix rank minimization and its application to information retrieval and data mining.

Paolo Codenotti, University of Chicago, is broadly interested in algorithms. He's completed research on approximation algorithms, algorithms relating to vision, and isomorphism problems. He's especially interested in algorithms that exploit the combinatorial and algebraic structures that might arise.

Junshan Lin, Michigan State University, centers her research on wave propagation, near-field imaging, inverse problems and PDE-constrained optimizations, numerical analysis, and scientific computations.

Xin Liu, University of North Carolina, concentrates his research on diffusion approximations for stochastic networks, stochastic analysis, stochastic stability and control, and applications of probability and statistics to communications, manufacturing, biological, sensor, and social networks.

Shiqian Ma, Columbia University, is currently focusing on theory and algorithms for large-scale optimization and applications in medical imaging, compressed sensing, matrix completion, machine learning, data mining, computer vision, and finance.

Gabriel Martinez, Stevens Institute of Technology, is interested in numerical optimization, risk measures, and stochastic optimization and its applications. In particular, her research focuses on developing numerical methods for optimization problems with probabilistic constraints.

Caroline Uhler, University of California-Berkeley, focuses her research on algebraic statistics, theoretical statistics (i.e. graphical models, multivariate statistics, maximum likelihood estimation, parameter identifiability, Markov chain Monte Carlo, hidden Markov models), applied algebraic geometry, convex optimization, and computational biology.

Arthur Szlam, Department of Mathematics, New York University, studies computational harmonic analysis, specifically the relationships between smoothness, frequency, and scale on graphs and data clouds and applications to signal processing and machine learning.

Divyanshu Vats, Carnegie Mellon University, is interested in statistical signal processing, information theory, and machine learning. He is currently working on algorithms for learning statistical models on graphs, which has applications in image processing, computational biology, speech processing, and many other fields.

Teng Zhang, University of Minnesota, is interested in data analysis and multi-manifold modeling.

Read more about the IMA’s Postdoctoral Fellowship Program at www.ima.umn.edu/postdocs.

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