Jennifer Fowler
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Epidemiologist
Also affiliated: Duke University (2012); Arkansas Science & Technology (2024); ORCID (2021); Arkansas Biosciences Institute (2020–2022)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Jennifer Fowler's research focuses on the application of advanced computational techniques, particularly deep learning and artificial intelligence, to medical imaging and data analysis. Her work investigates the use of ensemble U-Net models for segmenting kidney tumors in CT images and employs transfer learning for COVID-19 diagnosis using chest X-rays. Fowler has also studied the combination of brain MRI imaging with other data types to improve Alzheimer's disease diagnosis and examined the severity of COVID-19 in emergency room patients based on chest X-ray images.
Her research extends to data science ecosystems and educational initiatives. Fowler was a co-PI on an NSF EPSCoR Workshop grant focused on "Artificial Intelligence (AI) with No-Boundary Thinking (NBT) to Foster Collaborations in Research, Education and Training," which received $49,999. She has also contributed to publications on realizing statewide data science ecosystems and the evolution of online training programs in data analytics and research for underrepresented students in STEM. Fowler leads a research group and maintains an active lab website, collaborating with researchers from Arkansas State University and the University of Arkansas at Fayetteville.
Metrics
- h-index: 3
- Publications: 11
- Citations: 67
Selected Publications
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Envisioning and Realizing a Statewide Data Science Ecosystem (2024)
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Arkansas Summer Research Institute: The Evolution of an Engaging Online Training Program in Data Analytics and Research Targeting Underrepresented Students in STEM (2024)
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Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images (2022)
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Study the combination of brain MRI imaging and other datatypes to improve Alzheimer’s disease diagnosis (2022)
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COVID19 Diagnosis Using Chest X-rays and Transfer Learning (2022)
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Identify differentially expressed genes with large background samples (2021)
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An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images (2021)
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Transfer learning with chest X-rays for ER patient classification (2020)
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Cardiac or Infectious? Transfer Learning with Chest X-Rays for ER Patient Classification (2020)
Federal Grants 1 $49,999 total
Collaboration Network
Top Collaborators
- An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images
- COVID19 Diagnosis Using Chest X-rays and Transfer Learning
- Study the combination of brain MRI imaging and other datatypes to improve Alzheimer’s disease diagnosis
- Identify differentially expressed genes with large background samples
- Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
- An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images
- COVID19 Diagnosis Using Chest X-rays and Transfer Learning
- Study the combination of brain MRI imaging and other datatypes to improve Alzheimer’s disease diagnosis
- Identify differentially expressed genes with large background samples
- Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
- An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images
- COVID19 Diagnosis Using Chest X-rays and Transfer Learning
- Study the combination of brain MRI imaging and other datatypes to improve Alzheimer’s disease diagnosis
- Identify differentially expressed genes with large background samples
- Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
- An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images
- COVID19 Diagnosis Using Chest X-rays and Transfer Learning
- Study the combination of brain MRI imaging and other datatypes to improve Alzheimer’s disease diagnosis
- Identify differentially expressed genes with large background samples
- Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
- An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images
- COVID19 Diagnosis Using Chest X-rays and Transfer Learning
- Study the combination of brain MRI imaging and other datatypes to improve Alzheimer’s disease diagnosis
- Identify differentially expressed genes with large background samples
- An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images
- Identify differentially expressed genes with large background samples
- An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images
- Identify differentially expressed genes with large background samples
- Identify differentially expressed genes with large background samples
- COVID19 Diagnosis Using Chest X-rays and Transfer Learning
- COVID19 Diagnosis Using Chest X-rays and Transfer Learning
- Study the combination of brain MRI imaging and other datatypes to improve Alzheimer’s disease diagnosis
- Study the combination of brain MRI imaging and other datatypes to improve Alzheimer’s disease diagnosis
- Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
- Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
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