•   When: Thursday, November 09, 2017 from 11:00 AM to 12:00 PM
  •   Speakers: Amarda Shehu
  •   Location: The HUB, Meeting Room 1 & 2
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 In 1952, Sir Alan Turing published “The chemical basis of morphogenesis,” where he introduced the ingredients of a model-driven investigation into how matter changes form. Decades of scientific enquiry have demonstrated just how fundamental form and changes to form are to function and function modulation, whether in understanding and predicting phase transitions in statistical physics, the evolution and dynamics of complex networks in network science, or structural rearrangements of biological molecules regulating cellular processes in a growing cell or a beating heart.

A primary objective of my research is the design of novel algorithmics for elucidating biomolecular structures and their rearrangements as fundamental to understanding (dys)function, cellular processes, our own biology, disease, and disease treatments. My research advocates for a paradigm shift to address the algorithmic impasse in physics-based simulation. Inspiration comes from a combination of biology and science and engineering fields that model dynamic systems. My research group has proposed and matured sample-based models that are allowing us to conduct in-silico biology at scales previously impossible. These models build increasingly-detailed representations of biomolecular energy landscapes and equilibrium structural dynamics. They are now instigating our design of novel spatial data mining techniques to harness information embedded in biomolecular landscapes. As I will demonstrate, computing and mining landscapes is allowing us to discover and categorize mechanisms via which pathogenic mutations alter protein dynamics and function in human disorders. This research is bringing closer the dawn of machines learning how mutations alter biological activities.

As application-driven basic research, my work has also made important contributions to many domains in computer science. I will show selected advancements in stochastic optimization under the umbrella of evolutionary computation, in robot motion planning, the interplay between the two, and the integration of machine-learned models for effective state space exploration and state-to-state navigation problems posed by complex, modular, intrinsically-dynamic systems operating in the presence of constraints.


Bio: Dr. Amarda Shehu is an Associate Professor in the Department of Computer Science at George Mason University and is also affiliated with the School of Systems Biology and the Department of Bioengineering. Shehu received her B.S. with a dual degree in Computer Science and Mathematics from Clarkson University in Potsdam, NY in 2002 and her Ph.D. in Computer Science from Rice University in Houston, TX in 2008, where she was an NIH fellow of the Nanobiology Training Program of the Gulf Coast Consortia. Shehu’s research is supported by various NSF programs, including Intelligent Information Systems, Computing Core Foundations, and Software Infrastructure. Shehu is also the recipient of an NSF CAREER Award, two Jeffress Memorial Trust Awards, and a Virginia Youth Tobacco Program Award. Shehu is an Associate Editor of IEEE/ACM Transactions in Computational Biology and Bioinformatics. She has served as program committee chair and general chair of the premiere IEEE and ACM bioinformatics conferences and is a frequent editor of special journal collections and issues in PLoS Computational Biology, IEEE/ACM Transactions in Computational Biology and Bioinformatics, BMC Structural Biology, and Journal of Computational Biology. Shehu is also the recipient of the 2014 Mason Emerging Researcher/Scholar/Creator Award and the 2013 Mason OSCAR Undergraduate Mentor Excellence Award.

Posted 4 weeks, 1 day ago