Behrooz Davazdahemami

Assistant Professor

University of Wisconsin–Whitewater

United States

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Behrooz Davazdahemami is an Assistant Professor at the University of Wisconsin Whitewater, USA. His research focuses on generative AI applications in education, machine learning frameworks for health risk assessments, and data-driven methodologies for disease prediction and pandemic analytics. Recent publications include studies on student attrition, chronic risk factors during pandemics, and the efficacy of social distancing measures related to COVID-19.

Articles (12)

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2025

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2025

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2023

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2022

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2022

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2022

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2022

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2020

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2020

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2020

Collaborators (2)

Amir Zadeh

Associate Professor

Wright State University

UNITED STATES

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Kazim Topuz

Assistant Profesor

University of Tulsa

UNITED STATES

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Behrooz Davazdahemami

Research Interests

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Articles (12)

Generative AI-enhanced interventions: a novel framework for predicting and mitigating freshman student attrition

Revitalizing the Scholarly Compass: Harnessing GPT-Powered Automation for Dynamic Aims & Scope Evolution in Academic Journals

A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases

A Machine Learning Framework for Assessing the Risk of Venous Thromboembolism in Patients Undergoing Hip or Knee Replacement

A deep learning approach for predicting early bounce-backs to the emergency departments

An explanatory analytics framework for early detection of chronic risk factors in pandemics

An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions

Miscommunication in the age of communication: A crowdsourcing framework for symptom surveillance at the time of pandemics

A novel diffusion-based model for estimating cases, and fatalities in epidemics: The case of COVID-19

No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic

Using predictive analytics to identify drug-resistant epilepsy patients

Data analytics for the sustainable use of resources in hospitals: Predicting the length of stay for patients with chronic diseases

Collaborators (2)

Amir Zadeh

Kazim Topuz

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