I am Asiful Arefeen, PhD student in the Biomedical Informatics program at Arizona State University (ASU). I am working as a graduate research assistant at the Embedded Machine Intelligence Lab (EMIL) led by Dr Hassan Ghasemzadeh.
Before ASU, I started my PhD in Computer Science at Washington State University (WSU) in 2020 but got transferred after a year. Prior to WSU, I completed my undergrad from Bangladesh University of Engineering and Technology (BUET) back in 2019.
I am interested in Explainable AI, Counterfactual Explanations techniques, AI driven intervention design, and Machine Learning Applications in Health Monitoring Algorithm/System development.
PhD Biomedical Informatics & Data Science
Arizona State University
MS Computer Science
Arizona State University
MS Biomedical Informatics & Data Science
Arizona State University
BS Electrical & Electronic Engineering
Bangladesh University of Eng. & Tech.
LEAD is an explainable AI technique that determines relative feature contributions by characterizing the decision boundary and perturbing critical samples along the decision boundary close to the test sample. LEAD achieves at least 6% improved fidelity and 7% improved consistency compared to LIME and SHAP.
MealMeter is a linear regression based technique applied on multi-modal data collected using a CGM sensor and a wristband and tracks meal macronutrients. MealMeter achieves as low as 0.37 average root mean squared relative errors (RMSRE) in carb tracking, which is at least 15.9% improvement compared to TabPFN foundational model and other baselines.
This method reduces the labeling cost to train an efficient multi-task neural network by adding an additional clustering step in the multi-task active learning loop, within the sampling process
[Apr, 2025] Asif has succesfully presented his thesis proposal titled “Toward AI-Driven Behavioral Interventions in Digital Twin Systems for Improved Health Outcomes”.
[Apr, 2025] Two of Asif’s papers titled “LEAD: Localized Explanations with Adversarial Decision Boundary Characterization for Interpretable Disease Prediction” and “MealMeter: Using Multimodal Sensing and Machine Learning for Automatically Estimating Nutrition Intake” got accepted at the IEEE Engineering in Medicine and Biology Society (EMBC) conference.
[Feb, 2025] Asif’s paper titled “Cost-Effective Multitask Active Learning in Wearable Sensor Systems” got accepted at the Sensors.
[Oct, 2024] Asif received NSF Student Travel Award to attend IEEE BHI'24 at Houston, TX.
[Sept, 2024] Asif’s paper titled “GlyMan: Glycemic Management using Patient-Centric Counterfactuals” got accepted at the IEEE BHI 2024.
