Dr. Ashkan Tashk | Applied AI | Excellence Award (Any Scientific field)

Dr. Ashkan Tashk | Applied AI | Excellence Award (Any Scientific field)

postdoc, Technical University of Denmark.

Dr. Ashkan Tashk is a highly accomplished electrical engineer and postdoctoral researcher with deep expertise in telecommunications, machine learning, and biomedical imaging. With a strong academic and teaching background, he has worked across multiple prestigious institutions in Denmark, Germany, and Iran. His career blends theoretical knowledge with applied innovations, particularly in AI-driven healthcare technologies, contributing significantly to interdisciplinary research and development. He is known for his dedication to science communication, teaching, and AI-based applications in medicine.

Publication Profile

Google scholar

🎓 Education Background:

Ashkan Tashk received his Ph.D. in Electrical Engineering with a focus on Telecommunications in 2015, following his M.Sc. (2010) and B.Sc. (2006) in the same field. His undergraduate project involved designing and constructing a prototype sunlight tracking platform—an early indication of his strong interest in applied engineering and innovation. His academic journey provided a solid foundation in electronics, signal processing, and machine learning, which continues to influence his research today.

💼 Professional Experience:

Dr. Tashk currently serves as a Postdoctoral Researcher at Denmark’s leading universities (2019–present). Prior to that, he worked as a telecommunications expert at FREC and completed a research internship at Karlsruhe Institute of Technology (KIT), Germany. His career includes teaching roles at the University of Southern Denmark, University of Copenhagen, and various Iranian academic institutions. He has taught courses in electrical circuits, microprocessors, statistics, numerical analysis, and MATLAB programming, while also publishing Persian-language technical tutorials and conducting workshops in Europe and Iran.

🏆 Awards and Honors:

Dr. Ashkan Tashk became an IEEE Senior Member in 2022, recognizing his professional maturity and significant contributions to electrical engineering. He has served as a session chair at multiple international conferences such as ACSIT2020 in Copenhagen and ICCAIRO2019 in Athens. He has also completed prestigious programs like the “Science Communication” course by the Royal Danish Academy of Sciences and Letters and the RCR workshop at the University of Copenhagen, demonstrating his commitment to ethical and effective scientific practice.

🔬 Research Focus:

Ashkan’s research centers on the application of artificial intelligence and machine learning in biomedical engineering, particularly in image processing, ultrasound tomography, and cancer diagnostics. Notable projects include developing LSTM-RF models for metastatic prostate cancer prediction, CNN-based biomedical segmentation tools, and advanced metabolomics data imputation methods. His work also spans sonar signal processing, image-based fingerprint recognition, and microprocessor-controlled automation systems. These interdisciplinary projects reflect his strong problem-solving abilities and technological foresight.

🧩 Conclusion:

Dr. Ashkan Tashk is a dynamic academic, educator, and innovator whose work bridges electrical engineering and biomedical science using modern AI tools. His technical skill set, coupled with his teaching excellence and global collaborations, position him as a thought leader in the integration of engineering and healthcare. Fluent in Persian, English, and Danish, and proficient in tools like Python, MATLAB, and various PLC programming languages, he continues to impact both academia and industry with his visionary contributions.

📚 Top Publications & Citations:

Semantic Segmentation of Biomedical Images Using Deep Convolutional Neural Networks
Journal: Journal of Medical Imaging and Health Informatics
Cited by: 24 articles

Predicting Metastatic Prostate Cancer via Biochemical Parameters Using LSTM and RF
Journal: Computers in Biology and Medicine
Cited by: 18 articles

Machine Learning Imputation for Large-scale Metabolomics Data
 Journal: Metabolomics
Cited by: 10 articles

Eye-Tracking Data Analysis Using AI for Cognitive Study
 Journal: IEEE Transactions on Affective Computing
Cited by: 7 articles

Sikandar Ali | Artificial Intelligence Award | Best Researcher Award

Dr. Sikandar Ali | Artificial Intelligence Award | Best Researcher Award

Postdoc Fellow, Inje University, South Korea

🎓 Sikandar Ali is a passionate AI researcher and educator specializing in Artificial Intelligence applications in healthcare. Currently pursuing a PhD at Inje University, South Korea, he has a strong academic background and extensive research experience in digital pathology, medical imaging, and machine learning. As a team leader of the digital pathology project, he develops innovative AI algorithms for cancer diagnosis while collaborating with a global team of researchers. Sikandar is a recipient of prestigious scholarships, accolades, and recognition for his contributions to AI and healthcare innovation.

Publication Profile

Google Scholar

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