Abdullah Mamun

Graduate Research Associate

Arizona State University

About Me

I am a Ph.D. student in Computer Science at Arizona State University. I work as a Graduate Research Associate at Embedded Machine Intelligence Lab (EMIL) under the supervision of Dr. Hassan Ghasemzadeh.

Currently, my work includes building and optimizing deep learning models for time-series data. My works have been published in Progress in Biomedical Engineering Journal (IF: 7.7), Sensors Journal (IF: 3.5), AAAI'25, COBME Journal (IF: 4.2), EMBC'22, BSN'22, BSN'23, EMBC'25, and I have multiple journal and confernce submissions under review. I also actively peer-review conference and journal submissions. I have reviewed 35 submissions as of March 2026 that include 5 IEEE JBHI, 2 AAAI 2026, 1 BHI'23, 6 BHI'24, 1 BHI'25, 1 PerCom’23, 5 CHIL’24, 2 CHIL'25, 4 ML4H’24, 4 ML4H'25, 1 IEEE EMBC’25, 2 IEEE BSN'25, and 1 Guthrie Clinic Journal of Medicine submissions.

Interests

Deep Learning
Explainable AI
Time-Series Forecasting
Generative Models
Counterfactual Explanations

Education

Ph.D. in Computer Science, Dec 2026 (expected)
Arizona State University
M.S. in Computer Science, May 2025
Arizona State University
B.Sc. in Computer Science & Engineering, Oct 2018
Bangladesh University of Engineering and Technology

Publications

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability

We present a structured overview of methods, challenges, and solutions, aiming to support researchers and practitioners in developing reliable and explainable AI solutions for digital health. This paper is further enriched with detailed discussions of the contributions toward robustness and explainability in digital health, the development of trustworthy AI systems in the era of LLMs, and various evaluation metrics for measuring trust and related parameters such as validity, fidelity, and diversity.

Progress in Biomedical Engineering

Abdullah Mamun, Shovito Barua Soumma, Hassan Ghasemzadeh

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability

AI-Powered Wearable Sensors for Health Monitoring and Clinical Decision Making

A comprehensive review of AI-powered wearable biosensors emphasizing how machine learning and edge AI enable real-time health monitoring and personalized care, with insights on digital twins, LLMs, and challenges in privacy, scalability, and clinical integration.

Current Opinion in Biomedical Engineering

Shovito Barua Soumma, Abdullah Mamun, Hassan Ghasemzadeh

AI-Powered Wearable Sensors for Health Monitoring and Clinical Decision Making

LLM-Powered Prediction of Hyperglycemia and Discovery of Behavioral Treatment Pathways from Wearables and Diet

We developed GlucoLens, that takes sensor-driven inputs and uses advanced data processing, large language models, and explainable machine learning models to predict postprandial AUC and hyperglycemia from diet, physical activity, and recent glucose patterns.

Sensors Journal

Abdullah Mamun, Asiful Arefeen, Susan B. Racette, Dorothy D. Sears, Corrie M. Whisner, Matthew P. Buman, Hassan Ghasemzadeh

LLM-Powered Prediction of Hyperglycemia and Discovery of Behavioral Treatment Pathways from Wearables and Diet

Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors

A privacy-preserving system that leverages Gramian Angular Field (GAF) transformations, Federated Learning, and wearable sensor data to detect Freezing of Gait (FoG) in individuals with Parkinson’s Disease

The 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 14–17, 2025, Copenhagen, Denmark

Shovito Barua Soumma, S M Raihanul Alam, Rudmila Rahman, Umme Niraj Mahi, Abdullah Mamun, Sayyed Mostafa Mostafavi, Hassan Ghasemzadeh

Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors

Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals

We introduce MetaBoost, a novel hybrid framework that integrates SMOTE, ADASYN, and CTGAN, optimizing synthetic data generation through weighted averaging and iterative weight tuning to enhance the model’s performance (achieving a 1.87% accuracy improvement over individual balancing techniques).

The 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 14–17, 2025, Copenhagen, Denmark.

Sanyam Paresh Shah, Abdullah Mamun, Shovito Barua Soumma, Hassan Ghasemzadeh

Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals

See all publications

Preprints

AIMI: Leveraging Future Knowledge and Personalization in Sparse Event Forecasting for Treatment Adherence

AIMI is a knowledge-guided adherence forecasting system that leverages smartphone sensors and previous medication history to estimate the likelihood of forgetting to take a prescribed medication.

arXiv preprint

Abdullah Mamun, Diane J. Cook, Hassan Ghasemzadeh

AIMI: Leveraging Future Knowledge and Personalization in Sparse Event Forecasting for Treatment Adherence

Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health

We propose “Artificial Intelligence (AI) for Modeling and Explaining Neonatal Health” (AIMEN), a deep learning framework that not only predicts adverse labor outcomes from maternal, fetal, obstetrical, and intrapartum risk factors but also provides the model’s reasoning behind the predictions made.

arXiv preprint

Abdullah Mamun, Lawrence D. Devoe, Mark I. Evans, David W. Britt, Judith Klein-Seetharaman, Hassan Ghasemzadeh

Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health

Multimodal Physical Activity Forecasting in Free-Living Clinical Settings: Hunting Opportunities for Just-in-Time Interventions

This research aims to develop a lifestyle intervention system, called MoveSense, that forecasts a patient’s activity behavior to allow for early and personalized interventions in real-world clinical environments.

arXiv preprint

Abdullah Mamun, Krista S. Leonard, Megan E. Petrov, Matthew P. Buman, Hassan Ghasemzadeh

Multimodal Physical Activity Forecasting in Free-Living Clinical Settings: Hunting Opportunities for Just-in-Time Interventions

ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence

ActSafe utilizes a context-free grammar based approach for extracting and mapping MTCs from patient education materials.

arXiv preprint

Parker Seegmiller, Joseph Gatto, Abdullah Mamun, Hassan Ghasemzadeh, Diane Cook, John Stankovic, Sarah Masud Preum

ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence

Experience

Instructor

Arizona State University

Aug 2024 – Present Phoenix, Arizona

I am currently teaching the Fall 2024 course BMI 311 - Modeling Biomedical Knowledge at Arizona State University.

Graduate Research Associate

Arizona State University

Dec 2021 – Present Phoenix, Arizona

I am currently working on a few projects concurrently. My work mostly includes time-series data analysis with machine learning, explainable AI, data generation with generative models, counterfactual explanations, etc.

Teaching Assistant

Washington State University

Aug 2021 – Dec 2021 Pullman, Washington

I was a TA for a semester for the course Advanced Data Structures in C++ (Fall 2021 Cpt_S 223). I helped the students with their queries and difficulties related to programming and theory, graded homeworks and programming assignments, and held office hours. Dr. Yan Yan was the instructor of the course.

Research Assistant

Washington State University

Jan 2021 – Aug 2021 Pullman, Washington

Lecturer

United International University

Sep 2019 – Jan 2021 Dhaka, Bangladesh

Software Developer

HLC Technologies Ltd.

Nov 2018 – Sep 2019 Dhaka, Bangladesh