Burcu Tunga

Burcu Tunga is an Associate Professor in the Department of Mathematics at Istanbul Technical University, Faculty of Science and Letters. She holds a PhD in Computational Science and Engineering from the same institution and has been an active academic since 2005, progressing from Lecturer to her current rank. She has held key administrative roles, including Deputy Head of Department and membership in university-wide promotion and doctoral committees.

• PhD: Computational Science and Engineering, Istanbul Technical University (2004–2010)
• BS: Mathematics, Istanbul Technical University (1995–2000)

Her research lies at the intersection of applied mathematics and computer science, with a strong emphasis on image processing, data modeling, and High Dimensional Model Representation (HDMR). She develops and applies advanced mathematical models for image denoising, color-to-gray conversion, hyperspectral analysis, and machine learning-based diagnostics. Her work integrates fractional calculus, deep learning, and tensor decomposition to solve complex problems in medical imaging, agriculture, and materials science.

Recent publications (2022–2025) show a strong trend toward hybrid models combining HDMR with deep learning and fractional operators, applied to tasks such as coffee leaf disease detection, wood defect imaging, and hyperspectral anomaly detection. These reflect a consistent focus on enhancing image quality, feature extraction, and pattern recognition through novel mathematical frameworks.

Her scientific contributions are recognized through active research funding and a growing publication record. Key projects include:

• Anomaly Detection in Hyperspectral Images with HDMR (BAP-funded, 2024–2025)
• Disease Detection in Coffee Leaves with Deep Learning (completed, 2024)
• Content-Based Image Retrieval with HDMR (completed, 2018–2021)

Burcu Tunga advises several graduate students and collaborates widely across disciplines. She leads research involving tomographic reconstruction, stress wave analysis in trees, and financial time series prediction. Her lab focuses on developing non-destructive evaluation techniques, medical CAD systems, and intelligent image processing algorithms, often leveraging HDMR as a core methodological framework.