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Fairfax, VA 22030
Amarda Shehu is a professor in the Department of Computer Science. She holds affiliated appointments in the Department of Bioengineering and the School of Systems Biology. Shehu's research focuses on novel algorithms to bridge between computer and information science, engineering, and the life sciences and emphasizes problem solving in AI, search, planning, and optimization for high-dimensional and non-linear variable spaces, and machine learning for the simulation, analysis, and characterization of complex dynamics systems operating in the presence of constraints. Application domains are diverse and span from bioinformatics and computational biology, to computer-aided engineering design, and human-machine teaming. Shehu's research is supported by various NSF programs, including Intelligent Information Systems, Computing Core Foundations, and Software Infrastructure, as well as state and private research awards.
PhD, Computer Science, Rice University (2008)
MS, Rice University, Computer Science (2004)
BS, Computer Science and Mathematics, Clarkson University (2002)
Faculty Rank: Professor
2018-2021: Collaborative: Guiding Exploration of Protein Structure Spaces with Deep Learning. Funded by National Science Foundation.
2018-2021: Statistical Inference for Molecular Landscapes. Funded by National Science Foundation.
2017-2019. High-dimensional Statistics and Biomolecular Modeling as a Powerful Microscope over Pathogenic Mutations in Proteinopathies. Funded by Thomas F. and Kate Miller Jeffress Memorial Trust Program in Interdisciplinary Research.
2015-2019: Collaborative: A Plug-and-play Software Platform of Robotics-inspired Algorithms for Mapping Protein Structures and Motions. Funded by National Science Foundation.
2013-2015: Probabilistic Search Algorithms: Powerful Novel Tools for Peptide Modeling. Funded by Thomas F. and Kate Miller Jeffress Memorial Trust Program in Interdisciplinary Research.
2013-2015: Molecular Mechanisms Underlying Menthol Cigarette Addiction. Funded by Virginia Youth Tobacco Program.
2014-2017: Novel Stochastic Optimization Algorithms to Advance the Treatment of Dynamic Molecular Systems. Funded by National Science Foundation.
2012-2017: CAREER: Probabilistic Methods for Addressing Complexity and Constraints in Protein Systems. Funded by National Science Foundation.
2010-2014: A Unified Computational Framework to Enhance the Ab-initio Sampling of Native-like Protein Conformations. Funded by National Science Foundation
Artificial Intelligence, Machine Learning, Bioinformatics, Algorithms and Theory of Computation, Robotics, Computational Biophysics, Molecular Modeling
Published on September 3, 2015