Nilgün Şengöz
Assistant Professor & AI Researcher specializing in Deep Learning, Explainable AI, and Medical Image Processing.
Bridging AI Research with Real-World Impact
I am an Assistant Professor (Dr. Öğr. Üyesi) in the Department of Information Systems and Technologies at Burdur Mehmet Akif Ersoy University's Gölhisar School of Applied Sciences. My work sits at the intersection of artificial intelligence and practical applications in healthcare, computer vision, and beyond.
Currently, I am a Postdoctoral Researcher at the University of Nottingham (February 2026 – February 2027), supported by the prestigious TÜBİTAK 2219 International Postdoctoral Research Fellowship.
I am passionate about making deep learning models not only accurate but also transparent and interpretable — helping bridge the gap between complex algorithms and the clinicians who rely on them.
Ph.D. in Computer Engineering
Specialization in AI & Image Processing
TÜBİTAK 2219 Fellow
International Postdoctoral Research Fellowship
Assistant Professor
Burdur Mehmet Akif Ersoy University
Exploring the Frontiers of Intelligent Systems
My research spans multiple interconnected domains, with a focus on developing AI solutions that are both powerful and transparent.
Contributions to the Scientific Community
For a complete list, visit my Google Scholar profile.
Experience & Education
Recognition & Milestones
🏆 NATCOR Winning Team
NATCOR @ University of Nottingham — EPSRC
Winning Team of the Practical Challenges Competition in "Heuristic Optimisation and Learning" — solving problems on complexity theory, heuristics, meta-heuristics, hyper-heuristics and large-scale data analytics. (13–17 April 2026)
🎉 100+ Google Scholar Citations
Google Scholar
Reached the milestone of 100+ citations across publications in deep learning, explainable AI, and medical image processing.
View Profile📄 PATAT Conference — Paper Accepted
International Conference on the Practice and Theory of Automated Timetabling
Our group paper has been accepted to the prestigious PATAT conference, contributing to the field of automated timetabling and scheduling using AI-driven approaches.
TÜBİTAK 2219 Fellowship
TÜBİTAK — The Scientific and Technological Research Council of Turkey
International Postdoctoral Research Fellowship supporting research at the University of Nottingham, UK.
Thoughts, Travels & Discoveries
Sharing my journey through AI research, academic life abroad, and the places I explore along the way.
Binlerce Kilometre Uzakta, Kendi Tarihimle Yüzleşmek — British Museum
Osmanlı eserlerini Londra'da görmenin verdiği o tuhaf his: bir yanda heyecan, öte yanda derin bir hüzün. Çiçekler dahi ait olduğu toprakta büyür...
Londra'da British Museum'a gittiğimde, içimde bambaşka bir his uyandı. Özellikle Osmanlı tarihi bölümüne doğru nerdeyse koşar adımlarla gittim. Sanki mâverâdan bir ses beni müzenin o kısmına çağırıyordu. Cam vitrinlerin arkasında duran her eser; bir çini parçası, bir tuğra, işlemeli bir kaftan, bir ferman... Hepsi benim tarihimden, benim köklerimden bir parçaydı.
İnsan kendi medeniyetinin izlerini binlerce kilometre uzakta, yabancı bir toprağın göbeğinde, yabancı bir dilin rehberliğinde görünce garip bir his yaşıyor. Bir yanda coşku var — "Evet, atalarım böyle eserler bıraktı, bunlar var oldu, hayatta kaldı" diye bir gurur. Öte yanda ise sessiz, ağır bir hüzün oturdu gönlüme.
Çünkü o eserler orada olmamalıydı. Her şey ait olduğu yerde anlam kazanır. Bir çini, İznik'te; bir kaftan, Topkapı'da; bir ferman, yazıldığı toprakta soluduğunda gerçek sesini verir. Çiçekler dahi ait olduğu toprakta büyür, gelişir, kök salar. Söküldüğünde yine çiçektir belki, ama kokusu eksiktir, toprağından uzaktır.
Müzeden çıkarken şunu düşündüm: Bir bilim insanı olarak ben de şu an kendi toprağımdan uzaktayım. TÜBİTAK bursuyla Nottingham'dayım, araştırıyorum, öğreniyorum, kendimi geliştiriyorum. Ama fark şu ki ben geri döneceğim. Topraklarıma, öğrencilerime, bilgimi taşıyacağım ülkeme. O eserler ise ait olmadığı yerde kalacaklar.
British Museum dünya coğrafyasındaki tüm eserleri görebileceğiniz harika bir yer ama atalarımın mirasını olması gerektiği gibi kendi vatanımda yer almaması bizden sonraki nesillere anlatacak hikayelerimizin hep yarım kalmasına sebep olacaktır ne yazık ki...
Explainable AI: Why Transparency Matters in Healthcare
As AI systems become more prevalent in medical diagnosis, the need for transparency and interpretability grows exponentially...
As AI systems become more prevalent in medical diagnosis, the need for transparency and interpretability grows exponentially. In my research, I focus on Explainable AI (XAI) methods that help clinicians understand how algorithms reach their decisions.
Gradient-weighted Class Activation Mapping (Grad-CAM) is one of the key techniques I use to visualize which parts of a histopathological image the model focuses on when making a diagnosis. This visual feedback is crucial for building trust between AI systems and healthcare professionals.
The challenge is not just building accurate models — it's building models that doctors can understand, trust, and ultimately use to improve patient outcomes. This is where XAI bridges the gap between algorithmic power and clinical practice.
In our recent study on paratuberculosis diagnosis, we demonstrated that Grad-CAM heatmaps closely aligned with the regions pathologists identified as diagnostically relevant, validating the model's reasoning process.
First Weeks at University of Nottingham
Starting a postdoctoral journey in a new country is both exciting and challenging. Here are my impressions of Nottingham...
Starting a postdoctoral journey in a new country is both exciting and challenging. Nottingham has welcomed me with its beautiful green campus, friendly people, and a vibrant research community.
The University of Nottingham offers incredible resources for researchers. The labs are well-equipped, the library is extensive, and there are countless seminars and workshops to attend.
Being part of an international research community has already broadened my perspective on AI applications. The cultural exchange, the academic environment, and the opportunity to collaborate with researchers from diverse backgrounds make this journey truly rewarding.
Living in the UK as a TÜBİTAK 2219 fellow is a unique experience that I'm grateful for every day. I look forward to sharing more about my adventures in Nottingham and beyond!
Hybrid Models: Combining XGBoost with CNNs
Exploring how hybridizing traditional machine learning with deep neural networks can push accuracy boundaries...
Achieving high accuracy in image classification often exceeds what a single model can deliver. That's why I've been exploring hybrid approaches that combine the feature extraction power of deep learning with the classification strength of XGBoost.
Deep learning models like VGG16 and ResNet are excellent at extracting complex visual features from images. However, feeding these features into XGBoost can further enhance classification performance beyond what either method achieves alone.
In our recent work on rotten fruit detection and histopathology classification, this hybrid approach consistently outperformed standalone models, achieving accuracy rates above 99% in several benchmarks.
The key insight is that deep learning excels at representation learning while gradient boosting excels at decision boundaries. Combining both gives us the best of both worlds.
Let's Collaborate
I'm always open to discussing research collaborations, academic partnerships, and opportunities in AI and deep learning. Feel free to reach out!
🇬🇧 Currently in Nottingham
I'm conducting postdoctoral research at the University of Nottingham as a TÜBİTAK 2219 Fellow until February 2027.