Scholar
Tolga Cukur
Google Scholar ID: 5GLKJvwAAAAJ
Bilkent University, Professor of Electrical Engineering
Citations & Impact
Citations
6,011
H-index
35
i10-index
88
Publications
20
Co-authors
48
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Rambus Stanford Graduate Fellowship
- TÜBİTAK Career Award (2015)
- TÜBA-GEBIP Outstanding Young Scientist Award (2015)
- BAGEP Young Scientist Award (2017)
- IEEE Turkey Research Encouragement Award (2017)
- Science Heroes Association Young Scientist of the Year Award (2017)
- METU Prof. Dr. Mustafa Parlar Foundation Research Incentive Award (2019)
- TUSEB Aziz Sancar Incentive Award (2021)
- IEEE Senior Member (2017)
- Fellow of ISMRM (2024)
Research Experience
- Professor at Bilkent University since 2013
- Postdoctoral Fellow, Helen Wills Neuroscience Institute, University of California, Berkeley (2010–2013); worked with Prof. Jack L. Gallant
- Developed novel machine learning models of the human visual system under natural stimulation during postdoc
- As a Ph.D. student (2003–2009) at Stanford’s Magnetic Resonance Systems Research Laboratory, developed novel MRI acquisition and reconstruction methods for diagnosing peripheral arterial disease and monitoring cancer/inflammatory disease treatment
- Current lab research includes non-invasive diagnostic technologies using ultra-fast, sensitive MRI and MPI via medical physics and deep learning
Background
- Professor in the Department of Electrical and Electronics Engineering at Bilkent University
- Affiliated with the National Magnetic Resonance Research Center (UMRAM), Aysel Sabuncu Brain Research Center, and Neuroscience Graduate Program
- Research focuses on developing computational imaging methods to study anatomy and function of biological systems in health and disease
- His group develops cutting-edge biomedical imaging and data analysis methods, designing ultra-fast MRI techniques via deep learning for targeted diagnosis of vascular, musculoskeletal, and neurological diseases
- Uses functional MRI and machine learning to investigate human sensory and cognitive systems during complex natural behaviors
