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Applied Computing for Scientific Discovery

Silvia Crivelli

Silvia Crivelli

I received my Ph.D. in Computer Science from the University of Colorado, Boulder in 1995. I was a postdoctoral fellow at the University of California, Berkeley and the Lawrence Berkeley National Laboratory (LBNL), where I began to work in computational biology. Currently, I am a Program Manager 2 at LBNL and an Associate Researcher at UC Davis.

My research interests are twofold: to bring scientists together, both seasoned and young and from all walks of science, to tackle long-standing, extremely hard, and multidisciplinary biological problems and to develop highly interactive information modeling tools that empower physicians and researchers to explore and understand the mechanisms of health and disease. To this end, I have been collaborating with biologists, computer scientists, and applied mathematicians for twenty years. At the molecular level, I have conducted research in protein structure prediction and protein scoring and I created and lead the WeFold collaborative project ( to tackle some of the roadblocks in the field. At the cell and tissue level, I have worked in the area of bioimaging by developing new methods and technologies that allow biologists to create simplified geometrical models of very complex 3D images of biological systems. These images are too large and too complex to be viewed in their entirety making it necessary to segment them to visualize and analyze them in detail. These technologies will enable the creation of realistic models of biological systems that aggregate relevant information from multiple sources with the capability of zooming in and out, from organs and tissues all the way to the atomic level, to investigate how changes at one level impact the others. Such capability is essential to understand how mutations in a single protein may cause complex diseases that often manifest themselves at the level of cells and tissues and how to develop efficient drugs and assess the effects of different drugs and therapies on a particular disease.

I am a long-standing member of the CASP (Critical Assessment of protein Structure Prediction) community and a pioneer in building and running open collaborative efforts within this community. I have been the PI and co-PI of various projects that tackle the development of protein structure prediction methods, from physics-based and global-optimization-based methods to statistical-based methods. As such, I saw the need for a new kind of highly interactive molecular modeling tool to assist computational biologists to better design protein models and I led efforts to develop two highly innovative molecular modeling tools: ProteinShop and DockingShop (>

I have authored more than 30 papers in a broad range of STEM fields including parallel computing, molecular visualization, optimization, mathematical modeling, and machine learning.