Ph.D. in Biomedical Informatics
Programme overview
Main Subject
Engineering - General
Degree
PhD
Study Level
PHD
Study Mode
On Campus
BMI conducts research on techniques, tools, and systems that synthesize information to obtain a deep understanding of mechanisms underlying health and disease. BMI faculty engage in cutting-edge projects designed to contribute to integrative biomedical and translational research to enhance healthcare. These projects are carried out through synergistic activities with biomedical researchers and healthcare teams at Stony Brook and elsewhere in the U.S. and abroad. Research Areas include:
Computational and Systems Biology
This research area takes an interdisciplinary approach to the study of computation and systems-level analysis to resolve critical biological challenges. Researchers in this area focus on several computational and biologic components involving network analysis and reconstruction, modeling, algorithm development and genomic influence.
Faculty: Ramana V Davuluri
Bioinformatics
Faculty in this area develop methods and tools for the analysis, visualization, and translation of genomic data. Projects range from developing predictive models of response to therapy in human cancer to understanding gene regulation in model organisms.
Faculty: Ramana V Davuluri
Clinical Informatics
The Electronic Health Record (EHR) is the primary means for managing and storing a patient’s data. There has been a growing push to utilize data collected from EHR systems for secondary use, including automated computation of clinical phenotypes to improve accuracy of records, generation of real-time alerts to improve patient care, and analysis of records to advance our understanding of disease and healthcare and to improve the quality of care.
Faculty: Janos Hajagos, Fusheng Wang, Mary Saltz, Joel Saltz
Data Science
With the ever rise of available information often denoted as “big data”, this research takes a mathematical approach to provide actionable insights and our faculty work on the intersection of statistics, mathematics, and computer science. Another focus of our research is to develop and evaluate a suite of novel data and processing abstractions and optimizations for analysis of extremely large low-dimensional spatio-temporal data for scientific, biomedical, and clinical research. We develop and use quantitative models and methods to assist related research areas that are cross-platform and used on a variety applications.
Faculty: Chao Chen, Zhaozheng Yin, Tahsin Kurc, Joel Saltz, Prateek Prasanna, Ramana V Davuluri
Imaging Informatics
Imaging data is employed in care guidelines and clinical settings for virtually all cancer disease sites. With widespread adoption of digitizing microscope platforms, tissue images obtained from whole slide tissues or tissue microarrays are increasingly employed in clinical and research settings. Specific areas of research and development include deep learning methods for image classification and segmentation, image segmentation and feature computation pipelines, software for management and interrogation of image data and imaging features, high performance and Cloud computing methods and tools for image analysis and data management.
Faculty: Tahsin Kurc, Chao Chen, Zhaozheng Yin, Fusheng Wang, Joel Saltz, Prateek Prasanna
Population Healthcare Informatics
We are developing an integrated data gathering, integration, predictive-modeling and information-dissemination infrastructure for large scale population based studies, with the goals to reduce the risk of undesirable events like potentially avoidable emergency department visits and hospital-acquired infections, and to improve patient quality indicators. We also work on large scale integrative big spatial analytics for public health studies, by using healthcare data from open government initiatives, social media, and public data sources, with a goal for community or neighborhood level research. In particular, we work on big data driven studies for opioid epidemic.
Faculty: Mary Saltz, Janos Hajagos, Fusheng Wang, Joel Saltz
Computational and Systems Biology
This research area takes an interdisciplinary approach to the study of computation and systems-level analysis to resolve critical biological challenges. Researchers in this area focus on several computational and biologic components involving network analysis and reconstruction, modeling, algorithm development and genomic influence.
Faculty: Ramana V Davuluri
Bioinformatics
Faculty in this area develop methods and tools for the analysis, visualization, and translation of genomic data. Projects range from developing predictive models of response to therapy in human cancer to understanding gene regulation in model organisms.
Faculty: Ramana V Davuluri
Clinical Informatics
The Electronic Health Record (EHR) is the primary means for managing and storing a patient’s data. There has been a growing push to utilize data collected from EHR systems for secondary use, including automated computation of clinical phenotypes to improve accuracy of records, generation of real-time alerts to improve patient care, and analysis of records to advance our understanding of disease and healthcare and to improve the quality of care.
Faculty: Janos Hajagos, Fusheng Wang, Mary Saltz, Joel Saltz
Data Science
With the ever rise of available information often denoted as “big data”, this research takes a mathematical approach to provide actionable insights and our faculty work on the intersection of statistics, mathematics, and computer science. Another focus of our research is to develop and evaluate a suite of novel data and processing abstractions and optimizations for analysis of extremely large low-dimensional spatio-temporal data for scientific, biomedical, and clinical research. We develop and use quantitative models and methods to assist related research areas that are cross-platform and used on a variety applications.
Faculty: Chao Chen, Zhaozheng Yin, Tahsin Kurc, Joel Saltz, Prateek Prasanna, Ramana V Davuluri
Imaging Informatics
Imaging data is employed in care guidelines and clinical settings for virtually all cancer disease sites. With widespread adoption of digitizing microscope platforms, tissue images obtained from whole slide tissues or tissue microarrays are increasingly employed in clinical and research settings. Specific areas of research and development include deep learning methods for image classification and segmentation, image segmentation and feature computation pipelines, software for management and interrogation of image data and imaging features, high performance and Cloud computing methods and tools for image analysis and data management.
Faculty: Tahsin Kurc, Chao Chen, Zhaozheng Yin, Fusheng Wang, Joel Saltz, Prateek Prasanna
Population Healthcare Informatics
We are developing an integrated data gathering, integration, predictive-modeling and information-dissemination infrastructure for large scale population based studies, with the goals to reduce the risk of undesirable events like potentially avoidable emergency department visits and hospital-acquired infections, and to improve patient quality indicators. We also work on large scale integrative big spatial analytics for public health studies, by using healthcare data from open government initiatives, social media, and public data sources, with a goal for community or neighborhood level research. In particular, we work on big data driven studies for opioid epidemic.
Faculty: Mary Saltz, Janos Hajagos, Fusheng Wang, Joel Saltz
Programme overview
Main Subject
Engineering - General
Degree
PhD
Study Level
PHD
Study Mode
On Campus
BMI conducts research on techniques, tools, and systems that synthesize information to obtain a deep understanding of mechanisms underlying health and disease. BMI faculty engage in cutting-edge projects designed to contribute to integrative biomedical and translational research to enhance healthcare. These projects are carried out through synergistic activities with biomedical researchers and healthcare teams at Stony Brook and elsewhere in the U.S. and abroad. Research Areas include:
Computational and Systems Biology
This research area takes an interdisciplinary approach to the study of computation and systems-level analysis to resolve critical biological challenges. Researchers in this area focus on several computational and biologic components involving network analysis and reconstruction, modeling, algorithm development and genomic influence.
Faculty: Ramana V Davuluri
Bioinformatics
Faculty in this area develop methods and tools for the analysis, visualization, and translation of genomic data. Projects range from developing predictive models of response to therapy in human cancer to understanding gene regulation in model organisms.
Faculty: Ramana V Davuluri
Clinical Informatics
The Electronic Health Record (EHR) is the primary means for managing and storing a patient’s data. There has been a growing push to utilize data collected from EHR systems for secondary use, including automated computation of clinical phenotypes to improve accuracy of records, generation of real-time alerts to improve patient care, and analysis of records to advance our understanding of disease and healthcare and to improve the quality of care.
Faculty: Janos Hajagos, Fusheng Wang, Mary Saltz, Joel Saltz
Data Science
With the ever rise of available information often denoted as “big data”, this research takes a mathematical approach to provide actionable insights and our faculty work on the intersection of statistics, mathematics, and computer science. Another focus of our research is to develop and evaluate a suite of novel data and processing abstractions and optimizations for analysis of extremely large low-dimensional spatio-temporal data for scientific, biomedical, and clinical research. We develop and use quantitative models and methods to assist related research areas that are cross-platform and used on a variety applications.
Faculty: Chao Chen, Zhaozheng Yin, Tahsin Kurc, Joel Saltz, Prateek Prasanna, Ramana V Davuluri
Imaging Informatics
Imaging data is employed in care guidelines and clinical settings for virtually all cancer disease sites. With widespread adoption of digitizing microscope platforms, tissue images obtained from whole slide tissues or tissue microarrays are increasingly employed in clinical and research settings. Specific areas of research and development include deep learning methods for image classification and segmentation, image segmentation and feature computation pipelines, software for management and interrogation of image data and imaging features, high performance and Cloud computing methods and tools for image analysis and data management.
Faculty: Tahsin Kurc, Chao Chen, Zhaozheng Yin, Fusheng Wang, Joel Saltz, Prateek Prasanna
Population Healthcare Informatics
We are developing an integrated data gathering, integration, predictive-modeling and information-dissemination infrastructure for large scale population based studies, with the goals to reduce the risk of undesirable events like potentially avoidable emergency department visits and hospital-acquired infections, and to improve patient quality indicators. We also work on large scale integrative big spatial analytics for public health studies, by using healthcare data from open government initiatives, social media, and public data sources, with a goal for community or neighborhood level research. In particular, we work on big data driven studies for opioid epidemic.
Faculty: Mary Saltz, Janos Hajagos, Fusheng Wang, Joel Saltz
Computational and Systems Biology
This research area takes an interdisciplinary approach to the study of computation and systems-level analysis to resolve critical biological challenges. Researchers in this area focus on several computational and biologic components involving network analysis and reconstruction, modeling, algorithm development and genomic influence.
Faculty: Ramana V Davuluri
Bioinformatics
Faculty in this area develop methods and tools for the analysis, visualization, and translation of genomic data. Projects range from developing predictive models of response to therapy in human cancer to understanding gene regulation in model organisms.
Faculty: Ramana V Davuluri
Clinical Informatics
The Electronic Health Record (EHR) is the primary means for managing and storing a patient’s data. There has been a growing push to utilize data collected from EHR systems for secondary use, including automated computation of clinical phenotypes to improve accuracy of records, generation of real-time alerts to improve patient care, and analysis of records to advance our understanding of disease and healthcare and to improve the quality of care.
Faculty: Janos Hajagos, Fusheng Wang, Mary Saltz, Joel Saltz
Data Science
With the ever rise of available information often denoted as “big data”, this research takes a mathematical approach to provide actionable insights and our faculty work on the intersection of statistics, mathematics, and computer science. Another focus of our research is to develop and evaluate a suite of novel data and processing abstractions and optimizations for analysis of extremely large low-dimensional spatio-temporal data for scientific, biomedical, and clinical research. We develop and use quantitative models and methods to assist related research areas that are cross-platform and used on a variety applications.
Faculty: Chao Chen, Zhaozheng Yin, Tahsin Kurc, Joel Saltz, Prateek Prasanna, Ramana V Davuluri
Imaging Informatics
Imaging data is employed in care guidelines and clinical settings for virtually all cancer disease sites. With widespread adoption of digitizing microscope platforms, tissue images obtained from whole slide tissues or tissue microarrays are increasingly employed in clinical and research settings. Specific areas of research and development include deep learning methods for image classification and segmentation, image segmentation and feature computation pipelines, software for management and interrogation of image data and imaging features, high performance and Cloud computing methods and tools for image analysis and data management.
Faculty: Tahsin Kurc, Chao Chen, Zhaozheng Yin, Fusheng Wang, Joel Saltz, Prateek Prasanna
Population Healthcare Informatics
We are developing an integrated data gathering, integration, predictive-modeling and information-dissemination infrastructure for large scale population based studies, with the goals to reduce the risk of undesirable events like potentially avoidable emergency department visits and hospital-acquired infections, and to improve patient quality indicators. We also work on large scale integrative big spatial analytics for public health studies, by using healthcare data from open government initiatives, social media, and public data sources, with a goal for community or neighborhood level research. In particular, we work on big data driven studies for opioid epidemic.
Faculty: Mary Saltz, Janos Hajagos, Fusheng Wang, Joel Saltz
Scholarships
Selecting the right scholarship can be a daunting process. With countless options available, students often find themselves overwhelmed and confused. The decision can be especially stressful for those facing financial constraints or pursuing specific academic or career goals.
To help students navigate this challenging process, we recommend the following articles:
How to get a full scholarship
Looking for a fully-funded scholarship to see you into university? Find out how to boost your chances of getting one.
By Sabrina Collier
Updated Aug 29, 2024
990.7K
2237
Scholarships to study abroad
Find scholarships to study abroad with our lists of international scholarships – categorized by country, by subject, and by type of student.
By Staff Writer
Updated Aug 21, 2024
501.9K
788
Scholarship Applications: Frequently Asked Questions
Get answers to all your questions about scholarship applications, including tips on how to find scholarships and chances of success.
By Laura Tucker
Updated Aug 14, 2021
226.5K
335
More programmes from the university
PHD Programmes
583
Get closer to your dream programme
Your consent preferences have been set.
Time to start exploring.
Your account has been removed.
You can still browse the site or sign up again once you have parental consent.
Get closer to your dream programme