Faculty


photo not avail 2David Green, Associate Professor, Ph.D., 2000, MIT, Computational biology; protein interactions and networks
David Green's research is focused on computational studies of protein interactions.  Key areas include: Understanding the determinants of specificity in protein interactions through biomolecular simulation; development and application of algorithms for the design of binding interfaces; and development of tools for the study of protein-carbohydrate interactions, with a focus on the glycobiology of HIV-1 infection.  His research combines techniques from applied mathematics and models from biophysical chemistry to solve problems in biology and medicine.
http://www.ams.sunysb.edu/~dfgreen  Office: Math Tower P-137,  Phone: 631-632-9344
    

photo not avail 2

Tom MacCarthy, Assistant Professor, Ph.D. University College London, 2005, Computational Biology;
Tom MacCarthy’s research uses computational modeling to study fundamental biological questions in both immunology and evolutionary biology. In Computational Immunology he uses computational and statistical methods to better understand the mechanisms underlying the generation of antibody diversity in response to infection and disease. In Evolutionary Systems Biology he uses modeling to study the evolution of gene regulatory networks with the aim of revealing the forces driving changes in developmental networks and the causes underlying the evolution of robustness, or tolerance to failure, in these networks.
http://www.ams.sunysb.edu/~maccarth/people.shtml Office: Math Tower 1-101 ,  Phone: TBA  
    

photo not avail 2Robert Rizzo, Associate Professor, Ph.D., 2001, Yale University: Computational biology; drug design
 Rob Rizzo works in Computational Structural Biology. His research group seeks to understand the atomic basis for molecular recognition for specific biological systems involved in human disease such as HIV/AIDS, cancer, and influenza with the ultimate goal of developing new and improved drugs.  Computational methods are used to model how molecules interact at the atomic level with a given drug target.  The resultant 3D structural and energetic information is used to quantify and rationalize drug-binding for known systems and to make new predictions.
http://www.ams.sunysb.edu/~rizzo/ Office: Math Tower 1-111, Phone: 631-632-9340