Education
📘 Sikandar Ali holds a PhD in Artificial Intelligence in Healthcare (CGPA: 4.46/4.5) from Inje University, South Korea, where his thesis focuses on integrating pathology foundation models with weakly supervised learning for gastric and breast cancer diagnosis. He earned an MS in Computer Science from Chungbuk National University, South Korea (GPA: 4.35/4.5), with research on AI-based clinical decision support systems for cardiovascular diseases. His undergraduate degree is a Bachelor of Engineering in Computer Systems Engineering from Mehran University of Engineering and Technology, Pakistan, with a CGPA of 3.5/4.0.
Experience
💻 Sikandar is an experienced researcher and AI specialist. Currently working as an AI Research Assistant at Inje University, he focuses on cutting-edge projects in digital pathology, cancer detection, and medical imaging. Previously, he worked as a Research Assistant at Chungbuk National University, focusing on cardiovascular disease diagnosis using AI. His industry experience includes roles such as Search Expert at PROGOS Tech Company and Software Developer Intern at Hidaya Institute of Science and Technology.
Awards and Honors
🏆 Sikandar has received multiple awards, including the Brain Korean Scholarship, European Accreditation Council for Continuing Medical Education (EACCME) Certificate, and recognition as an outstanding Teaching Assistant at Inje University. He has also earned full travel grants for international conferences, extra allowances for R&D industry projects, and certificates for reviewing research papers in leading journals. Additionally, he is a Guest Editor at Frontiers in Digital Health.
Research Focus
🔬 Sikandar’s research focuses on developing AI algorithms for medical imaging, with expertise in weakly supervised learning, self-supervised learning, and digital pathology. His projects include designing AI systems for cancer detection, COVID-19 prediction, and IPF severity classification. He also works on object detection applications using YOLO models and wearable sensor-based activity detection for pets. His commitment to explainability and interpretability in AI models ensures their practical utility in healthcare.
Conclusion
🌟 Sikandar Ali is a dedicated AI researcher driving innovation in healthcare through artificial intelligence. With his strong educational foundation, diverse research experience, and impactful contributions, he aims to bridge the gap between AI and medicine, making healthcare more efficient and accessible.
Publications
Detection of COVID-19 in X-ray Images Using DCSCNN
Sensors 2022, IF: 3.4
A Soft Voting Ensemble-Based Model for IPF Severity Prediction
Life 2021, IF: 3.2
Metaverse in Healthcare Integrated with Explainable AI and Blockchain
Sensors 2023, IF: 3.4
Weakly Supervised Learning for Gastric Cancer Classification Using WSIs
Springer 2023
Classifying Gastric Cancer Stages with Deep Semantic and Texture Features
ICACT 2024
Computer Vision-Based Military Tank Recognition Using YOLO Framework
ICAISC 2023
Activity Detection for Dog Well-being Using Wearable Sensors
IEEE Access 2022
Cat Activity Monitoring Using Wearable Sensors
IEEE Sensors Journal 2023, IF: 4.3
Deep Learning for Algae Species Detection Using Microscopic Images
Water 2022, IF: 2.9
Comprehensive Review on Multiple Instance Learning
Electronics 2023
Hybrid Model for Face Shape Classification Using Ensemble Methods
Springer 2021
Cervical Spine Fracture Detection Using Two-Stage Deep Learning
IEEE Access 2024