Skip to main content
School of Information 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
Website
https://about.illinoisstate.edu/nam... website
  • About
  • Education
  • Awards & Honors
  • Research

Biography

Dr. Ammar is an Assistant Professor of Computer Science in the School of IT at Illinois State University and a health informatics researcher at Ochsner Health. She received her PhD in Computer Science from Wayne State University in 2016, and a 3-year Health Informatics training in the Oak Ridge National Lab’s Center for Biomedical Informatics at the University of Tennessee Health Science Center, through which she has designed research pipelines for integrating downstream clinical and upstream non-clinical risk factors to study several health outcomes and incorporated results from those models into intelligent digital health platforms. Her research interests are in Health Informatics, Knowledge representation, and explainable AI.

Current Courses

426.001Advanced Software Engineering

279.001Algorithms And Data Structures

168.013Structured Problem Solving Using The Computer

168.016Structured 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

Abstract

Ammar , N. An explainable, ontology-based genetic algorithm for diagnosis and outcome prediction using patient phenotypic data. Abstracts from the 57(th) European Society of Human Genetics (ESHG) Conference.. The 57(th) European Society of Human Genetics (ESHG) Conference. European Journal of Human Genetics 32.Suppl 2 (2024): 797-8.

Conference Proceeding

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

Journal Article

Howard, E., Gillispie-Bell, V., Olet, S., Glenn, B., Ammar , N., & Price-Haywood, E. Evaluating Racial Disparities in Implementation and Monitoring of a Remote Blood Pressure Program in a Pregnant Population-A Retrospective Cohort Study.. Ochsner journal 24.1 (2024): 22-30.
Shaban-Nejad, A., Ammar , N., Kumsa, F., Hashtarkhani, S., White, E., Chinthala, L., Owens, C., Hayes, N., & Schwartz, D. Towards an Explainable AI Platform to Study Interruptions in Cancer Radiation Therapy.. Studies in health technology and informatics 310 (2024): 1501-1502.
White, B., Prasad, R., Ammar , N., Yaun, J., & Shaban-Nejad, A. Digital Health Innovations for Screening and Mitigating Mental Health Impacts of Adverse Childhood Experiences: Narrative Review. JMIR Pediatr Parent 7 (2024): e58403.
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.

Presentations

Applied ML for Precision Clinical and Public Health. Artificial Intelligence Brown Bag Series. Committee on the Responsible Use of ArtificiaI Intelligence (AI) at Illinois State University. (2024)
Enhanced Ochsner Emergency Department Overcrowding Score (OEDOCS2.0). Illinois State University Annual Symposium. Illinois State University. (2024)
Leveraging Personalized Health Knowledge Graphs for Precision Clinical and Public Health. TBD. (2024)
Machine Learning for Precision Clinical Health: Two case studies from the Emergency and Maternal Fetal Medicine domains.. Center for Cybersecurity Research and Education (CCRE) Research Seminar series. School of IT. (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)