Md. Ariful Islam

Investigation of different health outcomes based on sleep physiological signals that can be captured from wearables.

It focuses on extracting the primary signal from complex biomedical or environmental data and accurately classifying it to generate commands for hardware actuation systems.

This work contributes to SDG 3 (Good Health and Well-being) by enabling assistive technologies and real-time health monitoring, and SDG 9 (Industry, Innovation, and Infrastructure) by fostering innovation in intelligent systems and automated control technologies.

It focuses on developing intelligent models for data-driven decision-making across domains such as healthcare, energy, and environmental monitoring.

This aligns with SDG 9 (Industry, Innovation, and Infrastructure) by advancing smart technologies and automation, SDG 3 (Good Health and Well-being) through improved diagnostics and predictive analytics, and SDG 13 (Climate Action) by enabling data-driven solutions for climate resilience and resource management.

It involves extracting meaningful insights from complex datasets to support informed decision-making in areas like healthcare, energy, and environmental sustainability.

This aligns with SDG 9 (Industry, Innovation, and Infrastructure) by promoting data-driven innovation, SDG 3 (Good Health and Well-being) through improved public health analytics, and SDG 13 (Climate Action) by enabling effective environmental data analysis for sustainable planning and climate resilience.

This thesis aims to design and develop a solar-electric vehicle (SEV) integrated with an intelligent MPPT-based solar tracking and power management system to maximize solar energy utilization.

This study proposes a reinforcement learning (RL) framework combined with artificial neural networks (ANN) to optimize yaw angles in multi-turbine wind farms, aiming to boost energy output by 10–15% and reduce wake losses by 20–25%.

This thesis aims to predict corrosion rates in offshore structures using advanced machine learning techniques, focusing on their impact on structural fatigue life.

This work explores the use of marine robotics for eco-friendly ocean waste management, comparing various robotic platforms from manual to autonomous systems

This work delineates future trends and offers insights for clinical translation, system integration, and standardization of next-generation EMG technologies.

This project aims to develop transfer learning models to predict antibiotic resistance in Tuberculosis

This project focuses on the early detection of Parkinson's disease (PD), a degenerative neurological disorder that impairs movement, speech, and posture, mainly in individuals over 50.

This project aims to develop a basic reinforcement learning (RL) framework for traffic light control, where the queue length serves as the reward and the state is defined by the number of vehicles on incoming lanes and the current traffic light state.

Poster Presentation
Title: A Systematic Review of Digital Health Technologies for Detecting and Monitoring Cancer Treatment-Related Cardiotoxicity
Event: 2025 Mayo Clinic and ASU Giants Among Us
Venue: ASU Health Futures Center (HFC) Conference Center, 6161 East Mayo Blvd, Phoenix, AZ 85054, USA

Presented a systematic review examining the role of digital health technologies in the detection and monitoring of cardiotoxicity associated with cancer treatments. The work highlights the use of wearable devices, remote monitoring systems, and data-driven approaches to enable early identification of cardiovascular complications, aiming to improve patient outcomes and support personalized oncology care.

Poster Presentation
Title: Sleep Physiology: From Wearable Obstructive Sleep Apnea Screening to Multidomain Risk Stratification and Maternal-Fetal Autonomic Coupling
Event: Fusion 2026 | A Biodesign Scientific Retreat
Venue: Memorial Union, 2nd Floor, Arizona Ballroom
Institution: Arizona State University, Tempe, AZ 85287, USA

Presented a research poster focusing on advanced approaches in sleep physiology, highlighting wearable-based screening for obstructive sleep apnea, multidomain risk stratification frameworks, and the study of maternal-fetal autonomic coupling. The work integrates physiological signal analysis and data-driven methodologies to improve early detection and understanding of sleep-related and perinatal health risks.

Guest Lecture
Course: EEE 203: Signals and Systems I (Fourier Transform)
Institution: School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA

Delivered a guest lecture on the fundamentals of the Fourier Transform and its significance in signal analysis. The session covered both theoretical concepts and practical applications, emphasizing how Fourier techniques are used in real-world scenarios such as Electrocardiogram (ECG)