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Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
bio
Biography
Dr. Ayhan is a computer scientist. He studied computer engineering at Baskent University in Ankara, Turkey and received his BSc. and MSc. degrees in 2004 and 2007, respectively. In 2008, he joined the University of Louisiana at Lafayette to continue his graduate studies. He obtained another MSc. degree in computer science from UL Lafayette in May 2010 and completed his PhD research in May 2015. During his PhD, he studied machine learning for computerized diagnosis of Alzheimer’s disease from neuroimages. Between September 2015 and July 2017, he worked as an Assistant Professor at the Department of Computer Engineering at Isik University in Istanbul, Turkey. Then, he joined the University of Tübingen in Germany as a researcher, where he and his colleagues developed clinically-relevant deep learning solutions within the context of ophthalmology. After five years in the beautiful city of Tübingen, he moved to London, where he joined University College London and Moorfields Eye Hospital as a Senior Research Fellow. Dr. Ayhan has published his research outcomes in premier venues for both technical and clinical audiences. He was also a Researcher/Co-Investigator on a UKRI grant for Artificial Intelligence Innovation to Accelerate Health Research: From 2 million to 20 million: scaling and validating a foundation model for ophthalmology.
portfolio
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publications
Paper Title Number 1
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
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Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
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Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
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Paper Title Number 4
Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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Paper Title Number 5, with math \(E=mc^2\)
Published in GitHub Journal of Bugs, 2024
This paper is about a famous math equation, \(E=mc^2\)
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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research
Human-AI collaboration for discovery of novel biomarkers
Beyond assisting clinicians in diagnosis, one hope for AI in medical imaging is to identify novel biomarkers that are not easily found by humans. With the learnings from my work in clinically-minded AI, I will focus on diseases of the nervous and cardiovascular systems. I aspire to discover biomarkers and validate them through clinical trials that are also improved via the interaction of experts and models, or even agentic systems for greater sense of human-AI collaboration.
Algoritmic peers in medicine
AI promises to transform healthcare by not only improving clinical workflows and patient outcomes but also equitably distributing the benefits to everyone, everywhere. To live up to these promises, AI implementations must be informed by the needs of clinicians, patients, healthcare providers as well as legal policies, and thoroughly validated with a clinical mindset. Only then, the state-of-the-art AI models can be conceived as peers (or co-pilots) that train differently and make algorithmic decisions based on sophisticated analyses of data.
Vision and interests
Progress in artificial intelligence (AI) continues to accelerate at a dizzying pace. While instances of AI are expected to stimulate digital innovation and have transformative impacts in our lives through diverse applications, an extensive adoption of AI also calls for scrutiny in order to ensure the safety of these systems and the distribution of their benefits to all, especially in high-stakes fields like medicine. Thanks to my educational background and proven research experience in diverse teams of clinicians, neuroscientists as well as other data and computer scientists, I am well-equipped to address modern challenges regarding not only the development of AI-based solutions with tangible impact but also their thorough inspection prior to deployment in safety critical settings. My overarching goal is to facilitate the transformation of healthcare in the digital era through efficient use of AI at scale.
talks
Talk 1 on Relevant Topic in Your Field
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Tutorial 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
CSS 342 Data Structures, Algorithms, and Discrete Mathematics I
UW Bothell, Autumn 2025. For details and course content, please, visit Canvas, the official learning management system of the UW.
Courses
Philosophy
Teaching is the profession of interacting with individuals or groups in order to facilitate their intellectual pursuits. Therefore, a teacher must be motivated to contribute to the lives of others in the first place. As learning may occur in various settings via individual tasks, group activities, or reflections on experiences, teaching also requires decent observation and orchestration skills, by which the teacher stimulates the learning environment and improves the process for all participants. In order to live up to these promises all together, a teacher must be a self-learner as well as a performer who consumes knowledge with the objective of multiplying and transmitting it. Given the dizzying pace of science and technology in today’s world, acquisition, digestion and transmission of new knowledge is more crucial than ever to science, technology, engineering, and mathematics (STEM) education.