Mohammad Joghataee

Mohammad
Joghataee

Researcher in healthcare analytics. Ph.D. candidate in Computer Science at Auburn University. I study electronic health records, clinical text, wearable sensors, and neuroimaging, with a focus on methods that can inform care.

mzj0081@auburn.edu Scholar LinkedIn ORCID CV
Mohammad Joghataee

About

About

My research is in healthcare analytics. I work on applications of AI and machine learning. During my Ph.D., I have built and evaluated models on clinical data — structured EHR variables, unstructured notes, wearable sensor streams, and brain imaging. I am advised by Dr. Ashish Gupta in the Harbert College of Business and co-advised by Dr. Xiao Qin in the Department of Computer Science and Software Engineering.

Research

Three lines of work.

01

EHR data, structured and unstructured

Early identification of rare fungal infections and tuberculosis from hospital records — labs, vitals, and notes. The aim is to surface an unusual infection while there is still time to act, without treating a retrospective model as a screening program.

  • Machine learning approaches to identify rare fungal and tuberculous infections in hospitalized patients. International Journal of Medical Informatics, 2026
  • Development and validation of a preliminary multivariable diagnostic model for identifying unusual infections in hospitalized patients. Biomolecules and Biomedicine, 2024
02

Sensors and wearable devices

Movement and decision-making from devices people already wear. Accelerometers for body-movement classification, and methods that connect concussion history to perceptual decision-making.

  • History of multiple concussions associated with impaired perceptual decision-making metrics identified by both machine learning and statistical regression methods. PLOS One, 2026
  • Development of a body movement classification algorithm using wearable accelerometers: a pilot study. AMIA, 2024
03

Time series and neuroscience

Resting-state fMRI for PTSD and post-concussive syndrome. Temporal deep models and explainable feature-based models so the result can be read next to the circuit, not only next to an AUC.

  • Channel-recalibrated temporal deep learning for interpretable classification of PTSD and post-concussive syndrome using resting-state fMRI. JAMIA, under review
  • Multimodal approaches for classifying PTSD and PCS-PTSD from resting-state fMRI. Pre-ICIS SIGDSA, 2025
  • Predicting PTSD severity in veterans: an explainable fMRI-based machine learning approach. Pre-ICIS SIGDSA, 2025

Honors

Awards.

Charles E. Gavin Fellowship

Auburn University · $6,000

Tiger Transit Analysis award

Best project, Tiger Transit Analysis

Harbert College of Business, Auburn University · $1,500

Outstanding departmental graduate award

Outstanding Departmental Annual Graduate Award

Spring 2025 · $2,000

Service

Editing, reviewing, judging.

  • Review coordinator, AMCIS 2025, Montreal
  • Managing editor, Decision Sciences Journal of Innovative Education, 2025
  • Reviewer, European Conference on Information Systems, 2025
  • Reviewer, Journal of Business Analytics, 2024
  • Reviewer, SIGDSA 2026
  • Reviewer, Information Systems Frontiers, 2024
  • Judge, Alabama Science and Engineering Fair, 2023

Life

Outside the lab.

I like lifting, running, camping, cooking, and photography, and I love my cat! His name is Mr. Leon.