Currently, I am in the midst of my PhD studies in the field of artificial intelligence (AI), with a specific focus on the classification of colon cancer.
My academic journey reflects a sustained commitment to AI research. I successfully completed my graduation project on AI in 2006 and followed it up with a master's graduation project in AI in 2013. These projects not only honed my skills but also established a foundation for my continued exploration of AI's applications across various domains.
My ongoing PhD research is cantered around AI and addresses the complex challenge of colon cancer classification. This interdisciplinary project combines elements from mathematics, biology, and data science, aligning with the diverse requirements outlined for the position. The study not only contributes to advancements in AI but also holds potential implications for healthcare through the development of effective colon cancer classification methods
s a part of my PHD I am on the brink of publishing two papers in SCIE journals, both currently under review. My research focuses on leveraging Machine Learning and Deep Learning approaches to model, predict, and analyze biological datasets, particularly in the context of colon cancer. The dataset I am utilizing for my research is sourced from Kaggle ([login to view URL]), comprising histopathological images in JPEG format. Notably, my experience extends beyond JPEG images, as I am adept at working with DICOM images as well.
To further enhance my skills and stay abreast of the latest advancements in the field, I am actively participating in deep learning competitions on Kaggle. This practical engagement not only allows me to apply theoretical knowledge but also reinforces my ability to tackle real-world challenges in biological data analysis.
I embarked on my journey in AI in 2006, initiating my exploration with MATLAB. During this period, I employed M-files for constructing models and conducting tests. Over the years, I have developed a robust understanding of image processing, honing my skills on both MATLAB and Python.
In recent years, I have transitioned to predominantly using Python for my projects. This decision is influenced by the platform's versatility and the absence of significant licensing issues, making it an ideal choice for seamless and efficient project development. In recent years, I've actively engaged with a diverse array of libraries in the fields of image processing and AI. From fundamental libraries like NumPy to high-level frameworks such as PyTorch, Keras, and TensorFlow, I've navigated a wide variety of tools.This shift has not only streamlined my workflow but has also allowed for greater flexibility in navigating the complexities of AI and image processing tasks.
My 14-year tenure as an engineer reflects a continuous commitment to refining and applying my problem-solving abilities..
