About Me
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 Explanation techniques, AI driven intervention design, Machine Learning Applications in Health Monitoring Algorithm/System development, Interactive tools development for Doctors, Practitioners, and Timeseries Foundation models.
- Explainable AI
- Counterfactual XAI
- Digital Twin Systems
- Label Efficient AI
- AI Driven Intervention Design
- Mobile Health
- Passive Sensing
- Foundation Models
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PhD Biomedical Informatics & Data Science
Arizona State University
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MS Computer Science
Arizona State University
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MS Biomedical Informatics
Arizona State University
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BS Electrical & Electronic Engineering
Bangladesh University of Eng. & Tech.
Experience
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Research Affiliate
Mayo ClinicI am working as a research affiliate for Dr. Bithika Thompson. My task is to improve the current Automated Insulin Delivery (AID) technology to reduce the number of abnormal glycemic events. Here is a recap of what I have done so far:- 1. Created the AZT1D dataset. I developed a pipeline to process raw data from Tandem insulin pump.
- 2. Developed GlyMan, a patient-centric glycemic management tool aimed at reducing hyperglycemic events experienced by patients with Type 1 diabetes.
- 3. Worked on GlyTwin, an advanced digital twin paradigm that senses abnormal glucose excursion beforehand and offers intervention through minimal behavioral changes.
- 4. Working on developing GlucoGuide, a web-portal for endocrinologists to summarize patient data from Tandem insulin pump and assist in retrospective interventions.
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Graduate Research Assistant
Embedded Machine Intelligence Lab, ASUResponsibilities include:- – I have been doing research on explainable AI and label efficient AI
- – I have published 2 journal articles and 6 conference proceedings.
- – I taught BMI 310: Android Application Development for Population Health during Spring'24.
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Graduate Teaching Assistant
EECS, Washington State UniversityI was TA for the following courses:- – CPT_S 427 Computer Security (Fall'20), taught by Dr. Ananth Jillepalli
- – CPT_S 121 Program Design and Development C/C++ (Spring'21), taught by Andrew O'Fallon
- – CPT_S 122 Data Structures C/C++ (Spring'21), taught by Andrew O'Fallon
Education
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PhD Biomedical Informatics & Data Science
Arizona State UniversitySupervised by . -
MS Computer Science
Arizona State University -
MS Biomedical Informatics
Arizona State University -
BS Electrical & Electronic Engineering
Bangladesh University of Eng. & Tech.
LEAD: Localized Explanations with Adversarial Decision Boundary Characterization for Interpretable Disease Prediction
47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'25)
2025
[Aug, 2025] Asif received CHS Graduate Student Travel Support to attend the IEEE BSN 2025 Conference in Los Angeles, CA.".
[June, 2025] Asif received Graduate College Travel Grant, CHS Graduate Student Travel Support and ASU Graduate Student Government (GSG) Travel Award to attend the IEEE EMBC 2025 Conference in Copenhagen, Denmark.".
[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.