Guang Cheng, assistant professor of statistics, for "Bootstrap M-estimation in Semi-Nonparametric Models."
Cheng will further the use of semi-nonparametric models in analyzing modern complex data by developming a series of innovative and valid bootstrap inferential tools. Statistical
science could benefit from the increasing number of researchers trained in semi-nonparametric modeling both from statistical and scientific viewpoints.
Jose Figueroa-Lopez, assistant professor of statistics and mathematics, for identifying
some key open problems
of Levy processes in
statistical analysis and
connecting them to parametric estimation and
change-point detection
for Levy models.
David Gleich, assistant professor of computer science, for "Modern Numerical Matrix Methos for Network and Graph Computations".
Gleich plans to design new algorithms for the matrix exponential and other functions of matrices in the local computations paradigm. The new algorithms will be able to operate on the world's
largest networks quicklly, and help application specialists study their data in new ways. Three driving applications will be ranking and voting, link prediction and brain networks. The investigation will also
include the study of higher-order connections in networks that give rise to three or four-dimensional matrices, commonly called tensors.
Jennifer Neville, assistant professor of computer science, for "Machine Learning Methods to support Computational Social Science."
Neville’s research will enhance our understanding of the mechanisms that influence the performance of network analysis
methods and drive the development novel methods for complex network domains. Expanding the applicability of
machine learning techniques for single network domains could have a transformational impact across a broad range of areas
(e.g., psychology, communications, education, political science) where current methods limit research to the investigation
of processes in dyad or small group settings.
Xavier Trioche, assistant professor of chemistry, for "Efficient Structural Analysis of Multivariate Fields for Scalable Visualization."
Tricoche’s research will benefit the scientific community by contributing a rigorous and scalable framework for the effective analysis and visualization of computational or measured datasets across a broad range of scientific problems.
Adam Wasserman,assistant professor of chemistry, for "Extending the Range of Applicability of Density-Functional Methods."
Wasserman strives to develop and apply electronic-structure methods that extend the reach of quantum-chemical approaches based on Density Functional Theory.
The methods being investigated provide a new framework for developing and testing approximate density functionals for
accurate calculations of ground and excited electronic states of molecules, including metastable ones. Applications include molecular dissociation, long-range charge-transfer excitations, and conductance through molecular wires. The time-dependent extension will facilitate the understanding and teaching of modern spectroscopy and molecular electronics.
