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College of Applied Science and Technology

Dr. Nariman Ammar

Assistant Professor of Computer Science
School Information Technology
Office
WIH Williams Hall 17e
Office Phone
(309) 438-8017office
phone
Email
nammar@ilstu.eduemail
  • About
  • Education
  • Awards & Honors
  • Research

Current Courses

279.001Algorithms And Data Structures

168.013Structured Problem Solving Using The Computer

168.016Structured Problem Solving Using The Computer

426.002Advanced Software Engineering

168.019Structured Problem Solving Using The Computer

168.025Structured Problem Solving Using The Computer

Teaching Interests & Areas

Software Programming
Software Design
Software Engineering
Advanced Web Programming
Data Science and data analysis

Research Interests & Areas

Health Informatics
Explainable AI
Knowledge Representation
Applied Machine Learning
Privacy Management

PhD Computer Science

Wayne State University
Detroit, Michigan, United States

Summer Research Grant (SRG)

School of Information Technology, Illinois State University
2023

University Research Grant (URG)

College of Science and Applied Technolog, Illinois State University
2023

Impact Award

Illinois State University
2023

Best Paper Award

IEEE Computing Society
2015

Graduate Professional Scholarship

Wayne State University Graduate School
2010

Conference Proceeding

Alghatani, K., Rezgui, A., & Ammar , N. Predicting Out-of-Hospital Vital Sign Measurements through Deep Learning (2023)

Journal Article

Ammar , N. Digital Personal Health Coaching Platform for Promoting Human Papillomavirus Infection Vaccinations and Cancer Prevention: Knowledge Graph-Based Recommendation System. JMIR formative research (2023)
Reese, J., Blau, H., Casiraghi, E., Bergquist, T., Loomba, J., Callahan, T., Laraway, B., Antonescu, C., Coleman, B., Gargano, M., Wilkins, K., Cappelletti, L., Fontana, T., Ammar , N., Antony, B., Murali, T., Caufield, J., Karlebach, G., McMurry, J., Williams, A., Moffitt, R., Banerjee, J., Solomonides, A., Davis, H., Kostka, K., Valentini, G., Sahner, D., Chute, C., Madlock-Brown, C., Haendel, M., & Robinson, P. Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.. EBioMedicine 87 (2023): 104413.
Brakefield, W., Ammar , N., & Shaban-Nejad, A. An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model.. JMIR formative research 6.7 (2022): e36055.
Cushing, A., Khan, M., Kysh, L., Brakefield, W., Ammar , N., Liberman, D., Wilson, J., Shaban-Nejad, A., & Espinoza, J. Geospatial data in pediatric asthma in the United States: a scoping review protocol.. JBI evidence synthesis 20.11 (2022): 2790-2798.
Gaudio, E., Ammar , N., Gunturkun, F., Akkus, C., Brakefield, W., Wakefield, D., Pisu, M., Davis, R., Shaban-Nejad, A., & Schwartz, D. Defining Radiation Treatment Interruption Rates During the COVID-19 Pandemic: Findings From an Academic Center in an Underserved Urban Setting.. International journal of radiation oncology, biology, physics (2022)

Presentations

Leveraging Personalized Health Knowledge Graphs for Precision Clinical and Public Health. TBD. (2024)
An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model. Epidemiology of Health Disparities Seminar. Tulane University. (2023)
An approach for AI Explainability with Applications in Healthcare. Minnesota State University's Biological Sciences Friday Seminar. (2023)

Grants & Contracts

School Research Grant (SRG): Privacy-Preserving Framework using Prognostic ML Models for Applications in Precision Medicine.. School of Information Technology, Illinois State University. Illinois State University. (2023)
University Research Grant (URG): Machine Learning Powered Knowledge Discovery for Precision Maternal Fetal Medicine. College of Science and Applied Technolog. Illinois State University. (2023)