Jonas Almeida


Jonas Almeida     
Professor, Chief Technology Officer, Graduate Program Director
Department of Biomedical Informatics
HSC 3-045C
Stony Brook, NY 11794
Phone:  (631)638-1325
Website (URL):  Personal Page

Bioinformatics, Web Computing, Big Data, Big Coding, Machine Learning, Integrative Informatics, Precision Medicine, Computational Pathology, Cancer Genomics, Precision Medicine


In January 2015 Dr. Almeida took the position as Professor and Chief Technology Officer at the Biomedical Informatics Department of Stony Brook University. This follows 4 years as the inaugural director of a new Division in Informatics in the Dept of Pathology of the Univ Alabama at Birmingham (UAB), and 5 years as Professor of Bioinformatics in the Division of Applied Mathematics of the University of Texas MDAnderson Cancer Center (2005-2010). Dr. Almeida received his Ph.D. in Biological Engineering from University Nova of Lisbon, Portugal, and his B.S. in Plant Biology in University of Lisbon, Portugal. He was a PostDoc at University of Tennessee and ORNL after his Ph.D.

The intersection of Computational Biology and Data Sciences has become a new frontier for the engineering of software ecosystems for Precision Medicine. Accordingly, identifying and delivering consumer-facing architectures involving Cloud Computing, Web Applications, and Machine Learning define the focus of Dr. Almeida research. The new intersection between cloud computing and web computing is also where a new quantitative framework is emerging for data-intensive analytical applications to the life sciences. Dr. Almeida's explores this new computational ecosystem, with the development of portable software solutions that can migrate between data sources – from consumer genomics to wearable sensing, and between different contexts of application from patients to caregivers. The trans-disciplinary contextualization and the quantitative methodologies needed to traverse them are defining challenges of data science. Within that field, Dr. Almeida's research draws an arc between systems biology, computational statistics and software engineering.  For an example, see, part of the work developing tools and methods to support radiology-based and pathology-based visual analytics.

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