About me

I am a Professor in the Department of Electrical and Electronics Engineering, National Magnetic Resonance Research Center (UMRAM), Aysel Sabuncu Brain Research Center, and Neuroscience Graduate Program at Bilkent University.

My research is targeted at developing computational imaging methods to examine the anatomy and function of biological systems in normal and disease states. To this end, my group develops cutting-edge imaging and data analysis methods for biomedical applications. We design ultra-fast magnetic resonance imaging (MRI) techniques via deep learning for targeted diagnosis of vascular, musculoskeletal and neurological diseases. On the neuroscientific front, we leverage functional MRI and machine learning to study the function of human sensory and cognitive systems during complex natural behavior. More information about my research program can be found on my lab's website.

Dr. Çukur is a faculty member at Bilkent University, Turkey since 2013. Prior to that, he was a postdoctoral fellow at University of California, Berkeley, CA. He worked with Prof. Jack L. Gallant in the Helen Wills Neuroscience Institute between 2010-2013. He devised novel machine learning techniques to build quantitative models of the human visual system under natural stimulation, and to infer the visual contents of the human brain.

He received his PhD in Electrical Engineering at Stanford University, CA. He worked with Prof. Dwight G. Nishimura and Prof. John M. Pauly as a member of the Magnetic Resonance Systems Research Laboratory between 2003-2009. During his doctoral work, he developed novel MRI acquisition and reconstruction methods to diagnose peripheral arterial disease, and to monitor treatment of cancer and other inflammatory diseases. Prior to his PhD, he received his B.S. degree in Electrical and Electronics Engineering from Bilkent University in 2003, as the class valedictorian.

Dr. Çukur was awarded Rambus Stanford Graduate Fellowship for his graduate studies. As a faculty member, he has received numerous prestigious awards including TUBITAK Career Award (2015), TUBA-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), and TUSEB Aziz Sancar Incentive Award (2021). He is a Senior Member of IEEE (2017) and a Fellow of ISMRM (2024).

My lab develops cutting-edge computational imaging techniques for biomedical research. Using these tools, we study the anatomy and function of biological systems in both normal and disease states. We have active research programs focusing on basic science and medical applications.

Our efforts in the medical field are focused on development of non-invasive diagnostic and monitoring technologies. We design ultra-fast and sensitive magnetic resonance imaging (MRI) and magnetic particle imaging (MPI) techniques through novel medical physics and deep learning approaches. Our designs utilize state-of-the-art neural network architectures and learning strategies. We utilize the fundamental advances in our techniques to push the envelope of sensitivity and specificity in targeted diagnosis of vascular, musculoskeletal and neurological diseases, and for molecular/cellular imaging.

We also pursue a computational neuroscience program targeted toward a quantitative understanding of human brain function and diseases. We use functional MRI and modern machine learning tools to study human sensory and cognitive systems during complex natural behavior. Specifically, we aim to locate functionally distinct areas of the human brain, and to reveal the type of information represented in each area. We are interested in how the human brain represents visual, auditory, and linguistic information; and how higher cognitive processes including attention, learning, and memory modulate these representations.

(Full list of publications is here.)

• EEE 443 / EEE 543 Neural Networks.
• EEE 482 / EEE 582 Computational Neuroscience.
• GE 461 Introduction to Data Science.
• MATH 241 Engineering Mathematics I.
• EEE 351 Engineering Electromagnetics.
• EEE 202 Circuit Theory.