Scholar
Asiful Arefeen
Google Scholar ID: NyouOYoAAAAJ
PhD Student, Arizona State University
Citations & Impact
Citations
314
H-index
8
i10-index
4
Publications
20
Co-authors
2
list available
Publications
Resume (English only)
Academic Achievements
- - Published 2 journal articles and 6 conference proceedings.
- - Created the AZT1D dataset and developed a pipeline to process raw data from Tandem insulin pump.
- - Developed GlyMan, a patient-centric glycemic management tool aimed at reducing hyperglycemic events experienced by patients with Type 1 diabetes.
- - Worked on GlyTwin, an advanced digital twin paradigm that senses abnormal glucose excursion beforehand and offers intervention through minimal behavioral changes.
- - Working on developing GlucoGuide, a web-portal for endocrinologists to summarize patient data from Tandem insulin pump and assist in retrospective interventions.
Research Experience
- - Research Affiliate, Mayo Clinic, October 2023 – Present: Improving Automated Insulin Delivery (AID) technology to reduce abnormal glycemic events.
- - Graduate Research Assistant, Embedded Machine Intelligence Lab (EMIL), ASU, August 2021 – Present: Research on explainable AI and label efficient AI.
- - Graduate Teaching Assistant, EECS, Washington State University, August 2020 – May 2021: TA for CPT_S 427 Computer Security, CPT_S 121 Program Design and Development C/C++, and CPT_S 122 Data Structures C/C++.
Education
- - PhD in Biomedical Informatics & Data Science, Arizona State University, August 2021 – Present, Advisor: Dr. Hassan Ghasemzadeh
- - MS in Computer Science, Arizona State University, January 2024 – Present
- - MS in Biomedical Informatics, Arizona State University, August 2021 – May 2023
- - BS in Electrical & Electronic Engineering, Bangladesh University of Engineering and Technology, February 2015 – April 2019
Background
- PhD student in the Biomedical Informatics program at Arizona State University (ASU). Research interests include 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.
Miscellany
- Interests include Explainable AI, Counterfactual XAI, Digital Twin Systems, Label Efficient AI, Mobile Health, Passive Sensing, and more.
