The Computational Biology & Bioinformatics (CBB) Program conducts research and offers services and training in the management and analysis of biological and biomedical data.
The CBB faculty and staff are a diverse group of interdisciplinary specialists with research interests that span the life sciences, and Computational Biology & Bioinformatics expertise areas in building infrastructure, developing algorithms, and designing and implementing analytical approaches. The data CBB handles ranges from species ecology through to genomic medicine. Particular focus areas are illustrated on our Research page.
The CBB Program brings computer scientists and engineers together with clinicians and medical research scientists to develop the methods and tools needed for the analysis of complex high-dimensional data sets.
Office: Room 600K, Gables One Tower
Enrico Capobianco holds a Doctorate in Statistical Sciences from the University of Padua. After conducting graduate studies at LSE (The London School of Economics and Political Science, UK), Northwestern University, and UC Berkeley, he pursued research in computational fields at Stanford University (US) (1994-1998). He received a NATO-CNR grant in Denmark (Niels Bohr Institute and Danish Technical University) and later (2001-2002) became an ERCIM fellow at CWI (Center for Mathematics and Computer Science/Centrum Wiskunde & Informatica) in Amsterdam (NL). He returned to US in 2003 working for as a Staff Scientist at the Mathematical Sciences Research Institute in Berkeley, and then as a Senior Scientist at Boston University, Biomedical Engineering (2004-2005). In 2005, he was appointed Head of Methods at Serono (Evry, FR), and then in 2006, he joined the Center for Advanced Studies, Research and Development (aka CRS4) in Sardinia at the Polaris Science and Tech Park leading a quantitative systems biology group. Dr. Capobianco’s core expertise is in statistical methods for bioinformatics, bio-network inference and analysis, machine learning and signal/image processing applied to biomedical studies. His background includes participation in academic and scientific activities at:
- SAMSI (Statistical and Applied Mathematics Sciences Institute) (North Carolina),
- IMA (Institute for Mathematics and its Applications) (Minnesota),
- MSRI (Mathematical Sciences Research Institute) (Berkeley),
- IPAM (Institute for Pure & Applied Mathematics) (UCLA),
- CAS (Chinese Academy of Sciences) in China,
- ICTP (International Centre for Theorectical Physics) in Italy,
- Fiocruz (Fundacao Oswaldo Cruz) in Brazil (visiting professor 2008-2010 within the Program, Capes – FIOCRUZ), and the
- Institut des Hautes Études Scientifiques (IHES) in France (Visiting scientist 2010)
Most recent published work:
Dominietto Marco D., Capobianco Enrico. Expected Impacts of Connected Multimodal Imaging in Precision Oncology. Frontiers in Pharmacology 29 November 2016 , Vol. 7, DOI 10.3389/fphar.2016.00451, ISBN 1663-9812.
Raju HR, Tsinoremas N, Capobianco E. Emerging non-coding RNA evidences linked to neuropathic pain. Front Neurol 18 (Neurogenomics).
Capobianco E. (First Online August 2016) Born to be Big: data and graphs. On their entangled complexity. Big Data and Information Analytics. American Institute of Mathematical Sciences. Vol. 1, No.2, pp. 2-2, issn 2380-6966, doi 10.3934/bdia.2016002.
Click here for complete list.
Office: Room 600.23, Gables One Tower
Dr. Zhijie Jiang received his PhD in Evolutionary Biology from Louisiana State University in 2006, and then spent one year working on genome annotation projects at Scripps, Florida (The Scripps Research Institute). His main projects at CCS can be divided into two categories: The first is genome assembling using next generation sequencing (NGS) data and genome annotation, his expertise includes both prokaryotic and eukaryotic genomes, run on two different NGS platforms; the second focus is gene expression analysis, working with microarray data as well as NGS data. This latter focus allows him to maintain an active interest in gene regulatory networks and promoter prediction.