Abeer Fiaz Hussain

PROFESSIONAL PROFILE Electrical Engineering Graduate | Biomedical Modeling, Machine Learning & Embedded Systems Passionate about transforming research insights into innovative, real-world solutions. With a strong foundation in biomedical modeling, machine learning, and embedded systems, I am a motivated and versatile engineering graduate passionate about transforming research insights into practical, cutting-edge solutions. I thrive on developing innovative projects that deliver tangible results and am eager to contribute technical expertise, drive meaningful impact, and grow professionally in dynamic, multidisciplinary environments. EDUCATION Bachelors of Electrical Engineering National University of Science and Technology , Islamabad , 3.2 (2026) INTERNSHIP EXPERIENCE MiNE Lab 15-Jun-2025 - 15-Aug-2025 During my research-based internship, I worked on a non-invasive melanoma detection project using bioelectrical impedance analysis. The work involved developing a multilayer electrical model of human skin (stratum corneum, viable skin, and subcutaneous adipose tissue) using parameters extracted from peer-reviewed biomedical literature. COMSOL Multiphysics was used to simulate impedance responses of healthy and melanoma-affected tissue under surface electrode excitation. This experience developed my skills in biomedical modeling, impedance-based tissue analysis, simulation-driven research, and scientific documentation. I gained practical exposure to literature-based parameterization, result interpretation, and technical reporting within a research environment. FINAL YEAR PROJECT Skin Impedance Based Melanoma Detection This project investigates a non-invasive method for melanoma detection using the electrical impedance properties of human skin. Melanoma causes changes in the structural and electrical characteristics of skin tissue, which can be reflected in measurable impedance variations. The skin is modeled as a multilayer structure consisting of the stratum corneum, viable skin (epidermis and dermis), adipose tissue and then muscle, with layer properties taken from published literature. Simulation-based analysis is used to compare impedance responses of healthy and melanoma-affected skin under surface electrode excitation. The study aims to evaluate the feasibility of skin impedance measurements as a low-cost screening approach for early melanoma detection. TECHNICAL EXPERTISE Research Intern – Skin-Impedance Melanoma Detection Developed a multilayer human skin model and performed simulations in COMSOL Multiphysics to analyze impedance differences between healthy and melanoma-affected tissue. Applied bioelectrical impedance analysis, electrode-based measurements, data interpretation, and technical reporting. Face-Detection Attendance System (Machine Learning) Implemented an automated attendance system using face detection, including masked faces. Applied machine learning algorithms, data preprocessing, model training, and real-time application integration.

Electrical Engineering Graduate | Biomedical Modeling, Machine Learning & Embedded Systems Passionate about transforming research insights into innovative, real-world solutions. With a strong foundation in biomedical modeling, machine learning, and embedded systems, I am a motivated and versatile engineering graduate passionate about transforming research insights into practical, cutting-edge solutions. I thrive on developing innovative projects that deliver tangible results and am eager to contribute technical expertise, drive meaningful impact, and grow professionally in dynamic, multidisciplinary environments.